[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
Очень интересный фрагмент ещё с далекого 1988 года. «Чужие среди нас»
Тогда много кто смотрел и не понимал.
Сейчас уже люди стали вникать.
Но меня улыбнул один момент, когда он видел через очки «мертвых»
А теперь давайте вспомним, как я говорил о своих видениях ?
Когда на улице иногда вижу их без всяких очков
😉
Жаль это многим сейчас не интересно и звучит как фантазия сумасшедшего и им это на руку, чем дольше люди так живут и думают, тем проще им жить среди нас.
Но я ведь помню, знаю и буду дальше об этом говорить!


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
❗
⚡
 По оперативным линиям СЦКК ДНР за 7 февраля поступила информация
о жертвах среди гражданских лиц
 в городах Республики:
г. Донецк (Кировский район):
- по ул. Замоскворечной, 27 в результате подрыва на мине ПФМ "
Лепесток
" получил ранение
парень-подросток 2007 г.р.
#ДетиНеМишень
#лепестки
,
#интерактивная_карта
🧨
обстрел в
11:40
:
-  при обстреле по ул. Коммунаров, 32 пострадали 5 человек: женщины 1986, 1960, 1956, 1974 г.р. и мужчина 1963 г.р.
❗
По оперативным линиям СЦКК ДНР за 7 февраля поступила информация о повреждениях в результате обстрелов ВФУ городов Республики:
г. Донецк (Кировский район):
- ул. Коммунаров, 32, 34 - многоквартирные жилые дома.
н.п. Горловка (Никитовский район):
- зафиксированы повреждения котельной № 15 "Донбасстеплоэнерго".
Сведения о последствиях обстрелов продолжают поступать.
@online_dnr_sckk


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
🤬
В Днепре посреди улицы мужчина предлагал детям покурить «шишки»
❗
Вчера вечером по ул. Передовая мужчина подошел к двум подросткам и предложил детям покурить шишки.
Когда горожанин понял, что дети не собираются употреблять и вызывают полицию, начал угрожать.
Ребята позвонили по телефону 102 и последовали за мужчиной, который предлагал им наркотики.
При этом информировали полицейских, где они находятся.
В пер. Арктическая полиция задержала мужчину.
При себе у 53-летнего горожанина были наркотические вещества.


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
122 країни проголосували за співпрацю «ООН» із Радою Європи.
У резолюції зокрема зазначається, що рф – країна агресор.
За резолюції проголосували також "союзники" РФ, такі як Китай, Бразилія, Вірменія, Казахстан, Індія.
https://t.me/operativnoZSU


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
Беспилотник Sirko, разработанный харьковскими инженерами, запустили в серийное производство
Около недели назад комиссия от Минобороны Украины приняла решение об использовании Сирка в войсках. Пока что о государственной закупке речь не идет, комплексы закупают для военных за волонтерские средства.
Материал, из которого сделан дрон – «радиопрозрачный», то есть его почти невозможно увидеть в радиозоне.
Хорошие отзывы о дроне уже идут с Купянского направления, из Херсона, Николаевской области.


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
☝
 Наразі в багатьох країнах світу реєструються випадки захворювання на сказ тварин, тому основною вимогою Європейської комісії при в’їзді на її територію є обов’язкова вакцинація тварини проти сказу, а також проходження дослідження на наявність антитіл до збудника сказу. Дослідження проводяться не раніше ніж через 30 днів після дати вакцинації. В результаті дослідження власник має отримати результат, який покаже наявність титру антитіл до вірусу сказу в сироватці крові тварини.

🇪🇺
 Також зазначаємо, що компетентними органами окремих країн-членів Європейського Союзу, введено спрощені умови для некомерційного переміщення домашніх тварин з України на територію ЄС.

📝

Вимоги країн світу  до некомерційного переміщення тварин, а також перелік уповноважених акредитованих лабораторій в Україні, в яких роблять тест на антитіла до збудника сказу
 опубліковані на офіційному вебпорталі Держпродспоживслужби в розділі «Міжнародне співробітництво», підрозділ «Ветеринарія та безпечність», рубрика «Вимоги до некомерційного переміщення тварин».

❗
 Водночас, слід зауважити, компетентні органи деяких з країн - членів ЄС наголошують на необхідності проведення тесту на наявність антитіл до збудника сказу  після прибуття до кінцевого місця призначення (вже після перетину кордону) з метою профілактики поширення вірусу сказу, про що представники зазначених компетентних органів інформують власників тварин у пунктах пропуску. Тест для перевірки ефективності вакцинації проти сказу домашніх тварин поза межами України можна зробити в
лабораторіях, уповноважених в країнах ЄС
 і в країнах, що не входять до ЄС.


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
📱
Чат-бот зі штучним інтелектом ChatGPT відтепер доступний в Україні.
Проте програма не працюватиме на тимчасово окупованих росією територіях, щоб вороги не використовували її для антиукраїнської пропаганди.
👉

Рівне Головне
Стікери
 |
Надіслати новину


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
☀
Доброго ранку
🇺🇦
Хочу сказати декілька слів, щодо вчорашнього кацапського висеру на рахунок «мєнтів».
👮
Бригада «Лють»,
 одна з бригад Гвардії наступу, є формуванням працівників Національної поліції, всі, хто туди приєдналися -
виключно добровольці
, тобто це вмотивований підрозділ.
По-друге, в нас навіть «мєнти» дають п*зди вс рф
🫠
Офіцер ✙
 | Підписатись


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
Сім'я Савенко передали бійцям ЗСУ інструменти, каремати, засоби особистої гігієни та каністри.
Дякуємо!


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
#История_Одессы
Скоро Октоберфест станет нашим национальным праздником. Пиво в Одессе любили и пили всегда. Все началось с немецких переселенцев, которые в 19м веке построили в Одессе пивовареные заводы, которые выпускали продукцию прекрасного качества. Первые упоминания о пивоварнях датируются 1802 годом – на тот момент их было две. Настоящим уголком Баварии в Одессе стала Водяная балка ( Бугаевка). Именно там, прежде всего из-за близости хорошей воды, были построены многочисленные пивоварни. Пиво выпускалось как свежее, так и в бутылках. Фасованое в тару, оно отпралялось в самые дальние уголки Империи, даже на Дальний Восток.


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
Сегодня многие спикеры от власти прогнозируют наступление, которое должен принести начавшееся похолодание. И хотя это весьма опрометчиво на фоне серьезных потерь ВСУ, про которые заявил посол Украины в Лондоне Вадим Пристайко в интервью американскому журналу Newsweek: "Мы теряем людей направо и налево. Мы не афишируем, сколько из погибших военные или гражданские, но можете представить, что цифры огромны, непостижимы. И города, некоторые из которых полностью разрушены".
Рискнуть и начать наступление подталкивают сложившиеся для ОП обстоятельства и США, которые настаиваю на новых победах. Администрация Байдена сможет сохранить, хотя бы уже обещанную помощь с перспективой ее расширения, только если ВСУ продемонстрируют новые успехи на поле боя.
Сегодня в Вашингтоне, который является основным донором и лоббистом вооружения для ВСУ произошел политический кризис и раскол. Более того, сложилась ситуация не в пользу Украины. К власти пришли республиканцы, которые в 15-м голосовании смогли выбрать спикера Конгресса. Это стало возможным вследствие договоренностей Трампа с Маккарти, последнему пришлось пойти на уступки трампистам - один из ключевых компромиссов стало обещание не увеличивать военные расходы США в следующем году, что очень серьезно задевает интересы Киева. Да и в целом трамписты очень скептически настроена в отношении продолжения помощи Украине в нынешних объёмах. А что еще более важно трампист Мэтью Гетц может стать новым главой оборонного комитета Палаты представителей. Тот самый, который весьма критично отнесся к Зеленскому.
Все это разворачивается на фоне весьма неутешительных прогнозов для граждан США. Главный экономист Всемирного банка Ларри Саммерс спрогнозировал для Bloomberg, что в 2023 году американцев не ждет возвращение эры низких ставок ФРС. Чем выше ставка, тем дороже кредиты, и тем больше риска банкротства ряда компаний. Для обычных американцев потенциально это или потеря работы, или проседание в доходах. А это значит, что жесткая позиция по отношению к Украине будет поддержана среди большинства простых американцев, чьи личные доходы их больше интересуют, чем судьба далекой для них Украины.


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
В Харькове арестован мужчина, который якобы нарисовал российские флаги в парковой зоне Зелёный Гай.
Полиция говорит о "сложной операции" по выявлению "преступника", но не факт, что задержанный причастен к этой акции.
Подробностей пока нет, мужчине желаем выстоять под давлением и пытками.
Первый Харьковский|
Предложить новости


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
Стародавні люди в Європі
 вимерли
 1.1 млн років тому через холод. Нове дослідження провели вчені з Університетського коледжу Лондона.
Представники виду Homo erectus прибули до Європи з Азії між 1,8 і 1,4 мільйона років тому. Однак, сліди їхнього існування зникають близько 1,1 мільйона років тому, створюючи "очевидний розрив у 200 000 років", як підкреслюють вчені.
Щоб з'ясувати ймовірні причини зникнення цілого виду людей, науковці використали морський планктон і пилкові зерна наземної рослинності з відкладень океану біля берегів Португалії. Низька температура води біля Лісабону, яка впала з 21 °C до 6°, створила непридатні умови для архаїчних мешканців.
У цьому також знайшли астрономічний слід, зокрема гравітаційний вплив Юпітера, який зробив орбіту Землі на той момент більш круговою, що є ключовим фактором у поясненні різкого похолодання.
Підпишіться на NV


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
🔥
Зараз на Запорізькому напрямку ЗСУ знищує окупанта в великих кількостях як і у живій силі, так і в техніці.
 Окупантам нічого не залишається окрім того як здаватися в полон українським підрозділам.
Слава нашим захисникам!
🇺🇦
🇺🇦


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
🤡
 Кацапи тричі "брали" Спірне:
1. Влітку. Коли вони дійсно його захопили і на початку вересня наші його відбили.
2. В жовтні. Навіть присвячували комусь взяття села.
3. В грудні. Видно, що після дози психотропів в мо рф почалися мультики.
На носі вже лютий 2023 року, а вони досі обстрілюють село, яке вже "брали" і яке вже "взяли"....
📍

deepstatemap.live/#15/48.8090/38.2441


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
💡
❌
Без світла залишились частково
Позняки
 та
Харківський
 масив.
Знову
локальна аварія
, обіцяють повернути світло
до 01:00

🤬
 Кожний вечір «локальні» аварії…
😕
Детально про АТАКУ читайте вище в каналі
☝
🏽
Посилання на наш канал

👉

https://t.me/+aAaZ8kQkkpo2ZjRi

Надсилайте новини в анонімний бот
➡
@novosty_kiev_bot
➡

ПІДПИСАТИСЬ


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
Військово-транспортний літак Іл-76 ВКС
💩
 розбився в аеропорту Гіссара, столиці Таджикистану, у місті Душанбе.
🔥
 Пісня


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
💥
Ситуация на донецком направлении, 9 января 2024 г.
▪
На
Кураховском

направлении
 ВС России продавливают оборону противника в
Георгиевке
, двигаясь из
Марьинки
. Южнее, у
Новомихайловки
, противник заявил, что ему удалось отодвинуть наши войска у населенного пункта.
▪
На южном выступе фронта
Авдеевского

участка
 в
Первомайском
 наши войска ведут активные действия, стремясь продвинуться в населенном пункте. Сообщают о боях восточнее
коксохимического

завода
.
▪
В направлении
Часов

Яра
 продолжается «ползучее» наступление наших войск. Враг отошел на окраины
Богдановки
 на севере направления. У н.п.
Хромово
 враг оказывает упорное сопротивление.


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
⚡
Ой, леле. Російські пабліки повідомляють, що військовий аеродром «Шайковка» в Калузькій області був атакований безпілотником.
Чуток багато. І що на місці падіння - велика вирва. Інші пишуть, що було пошкоджено літак, який нібито не використовується.
У будь-якому випадку це вже другий епізод атаки військових аеродромів із місцями базування стратегічних бомбардувальників Ту-22М3.
🍿
Хороша тенденція. (
Фото ілюстративне
)
@pekhno_news


[System]
You are an expert in the glittering_generalities manipulation technique. Glittering Generalities: Uses emotionally appealing but vague concepts like 'freedom' and 'justice' without specifics. List phrases that provide evidence of glittering_generalities in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the glittering_generalities manipulation.
To improve the classification of texts regarding the usage of Glittering Generalities, consider applying the following guidelines specifically designed to address the issues highlighted in your false positives and negatives.

### 1. **Define Key Characteristics of Glittering Generalities:**
   - **Emotional Appeal:** Focus on whether the statement elicits a strong emotional reaction (e.g., pride, fear, hope).
   - **Vagueness:** Ensure the language used is ambiguous and lacks specificity. Look for words and phrases that sound grand but offer no concrete details.
   - **Potential for Misinterpretation:** Consider if the statement can be reasonably understood in varying contexts, leading to different interpretations.

### 2. **Engage with Context Considerations:**
   - **Contextual Clarity:** Analyze the surrounding context to clearly see if the phrase is being used in a vague manner or if it has a substantiated meaning.
   - **Historical and Cultural Relevance:** Take into account the historical and cultural significance of phrases to determine if they integrate emotion-baiting terms or concepts common to particular narratives or propaganda strategies.

### 3. **Include Semantic Analysis:**
   - **Key Terms Identification:** Create a lexicon of words associated with vague emotional appeal (e.g., freedom, justice, peace). Monitor their usage without accompanying specifics.
   - **NLP Techniques:** Utilize natural language processing tools to identify sentiment and contextual keyword usage. Texts using said phrases ought to be cross-referenced with the key emotional lexicon.

### 4. **Establish Criteria Across Categories:**
   - **Positive Identification:** For a statement to qualify as a Glittering Generality, it should have:
     - An emotionally charged term or phrase (e.g., "victory," "freedom")
     - Lack of specific detail on how such emotional states are achieved or maintained.
   - **Negative Identification:** Statements that provide constructive details or statistics indicating real situations or events should be critically assessed without presuming their emotional appeal influences the classification.

### 5. **Enhance Training Datasets:**
   - **Balance Positive and Negative Examples:** Create a diverse set of training data examples that include real-world instances where terms are both appropriately and inappropriately used to help your model learn nuance.
   - **Dynamic Updates:** Continually update your dataset with new examples that come up in current events or social discourse to stay relevant.

### 6. **Review Feedback Loops:**
   - **User Input Inclusion:** Incorporate feedback from real users, or utilize a team of reviewers to differentiate between emotionally vague statements and concrete, informative content.
   - **Iterative Learning:** Use active learning techniques where the model can be retrained with additional data sourced from its classification mistakes.

### 7. **Develop a Scoring System:**
   - **Create Scores:** Implement a scoring system where texts are graded based on emotional appeal, vagueness, and ambiguity. Statements crossing certain thresholds in these areas may be flagged as Glittering Generalities.
   - **Threshold Adjustment:** Regularly tune the scoring metrics to reduce false positives and negatives based on ongoing classifications.

### 8. **Focus on Practical Scenarios:**
   - **Test Real-Life Content:** Apply these guidelines to actual articles, posts, and comments from political discussions regularly to refine the model's understanding of the concept in the wild.

Following these guidelines systematically will increase the classifier's proficiency in distinguishing between statements that genuinely exemplify the Glittering Generalities technique and those that should not. It moves the focus from simply identifying emotional language to understanding how such language operates within context and conveys a message (or lack thereof) effectively.

[User]
🇺🇦
👊
Колишня російська САУ стала українським “Одіном”
🔺
95-а окрема десантно-штурмова бригада не одноразово демонструвала трофейну техніку росіян, яка служить Збройним силам України. На цей раз пропонуємо історію про самохідну артилерійську установку 2С19, яку ворог кинув, тікаючи з поля бою, та яка тепер несе смерть ворогу.
💬
Цю САУ наша арта трохи підбила, і росіяни просто не змогли завести її, коли тікали. То ми її забрали собі, відремонтували, замалювали ворожі знаки “z” та перейменували установку на честь бога війни та перемоги “Одіна”. Тепер техніка прекрасно справляється зі своїми обов’язками нищити окупантів”, - розповідає командир гармати Олександр.
🔺
За його словами, на рахунку САУ вже є один підбитий ворожий танк та ще купа росіян. Також виявляється, що ця установка доволі нова з маленьким пробігом.
Далі за посиланням:
➡

surl.li/ewdwc
#stoprussia

#ДШВ

#ЗСУ

#Завжди_Перші

#UkrainianAirAssaultForces
#AirAssaultForces

#WarInUkraine

#RussianAggression