[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]
Міжнародне гуманітарне право існує для того, щоб під час воєн мирне населення менше страждало.
Але що робити, коли росіяни воюють саме з мирним населенням? Коли свідомо намагаються збільшити страждання саме мирних людей. Прицільно бʼють по житлових кварталах і цивільній інфраструктурі, по лікарнях і школах, складах із зерном.
Коли росіяни цілеспрямовано вбивають цивільних українців і тут же починають цинічно розмірковувати про страждання цивільного населення, наприклад, у Газі. Це створює відчуття абсурду. І вони роблять це, сидячи в ООН, в Радбезі.
Не дуже зрозуміло, що взагалі росіяни роблять в ООН: або навіщо там росіяни, або навіщо таке ООН?
Очевидно інше. Після цієї війни все буде іншим: і ООН, і Радбез, і міжнародне гуманітарне право.
Тим часом сьогодні вранці пʼятеро мирних українців загинули в Запоріжжі від удару російської ракети по житловому будинку
😢


[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]
«Блядь, вас один человек сдерживает! Один! Десятерых»
Істерика орківського командира через те, що наш воїн один стримує штурм противника. Цей грудневий бій закінчився мертвими і скаліченими окупантами, а наш боєць залишився живий-здоровий, відстояв позицію і дочекався підкріплення.
Источник:
8 ОГШБ 10 ОГШБр «Едельвейс»


[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]
Боєць на псевдо
Кавказ.
 До повномасштабного вторгнення був студентом. Сьогодні він — головний сержант взводу. Як обирав підрозділ, у якому служитиме — розповів у
#5питань
.
Що мотивувало долучитися до Третьої штурмової бригади?
Я обрав саме наш підрозділ за його дисципліну та високу вмотивованість кожного бійця. Інших варіантів, окрім як стати на захист своїх рідних та близьких я не бачив.
Як потрапив до 3-ї ОШБр?
З самого початку повномасштабної війни був у пошуках подібного підрозділу. А потім сюди мене до себе покликали друзі. Так я і став частиною штурмової бригади.
Ким був у цивільному житті?
Був студентом, навчався за спеціальністю «маркетинг».
З самого дитинства займався бойовими видами спорту.
Твій девіз по життю…
Цитую слова свого командира:
«Війна — це час справжніх чоловіків!»
Які плани після перемоги?
Після перемоги маю велике бажання помандрувати мальовничими місцями нашої країни. А також продовжити виборювати незалежність й свободу держави, відбудовуючи та розбудовуючи її.
￼
_________________________________
Приєднуйся до Третьої штурмової:
@ab3_rc_bot
3ОШБр
 |
Instagram
 |
Facebook
 |
YouTube
 |
ab3.army
 |
TikTok
 |
Підтримай Третю штурмову
🇺🇦


[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]
ЕС осудил проведение выборов в России )
Об этом заявил высокий представитель ЕС Жозеп Боррель.
" Выборы проходили в условиях постоянно сужающегося политического пространства, что привело к тревожному росту нарушений гражданских и политических прав, недопущению к выборам многих кандидатов [...]", - говорится в заявлении
Не то дело в Украине" Нет выборов нет нарушений"))


[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] Нет, диверсионная школа Украины весьма высока. Там и советский партизанский опыт, и подпольный бандеровский, и советский грушный… Ну и новые знания и практики НАТО.   У нас советский опыт и шикарный, мощный и успешный опыт борьбы с международным бандитским…


[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]
— А ти не боїшся, що у твою машину влучать? Це спокуслива ціль для артилерії ворога.
— Я не думаю про це. Я думаю про дружину і дітей. Про те, як ми будемо щасливо жити, коли я повернуся до них, після нашої перемоги.
Ось так відповів на питання водій вантажівки, який віз провізію та боєкомплект хлопцям на позиції. Його автомобіль сунув по розбитій дорозі серед полів, за кілька кілометрів від фронту, а навкруги, то ближче, то далі, гриміли вибухи від ворожих прильотів.
Вітаємо наших хоробрих автомобілістів, які доставляють на фронт усе необхідне для перемоги!
32 окрема механізована бригада


[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]
#анализ

Абсурд по Зеленски.
Почти 2 года ТЦК и военкомы кошмарили людей и рассказывали, что они имеют право проверять документы, а
как оказалось- нет.
Будут статьи? Уголовка всем военкомам и ТЦК?
Или власть окончательно считает украинцев «быдло-рабами», которые схавают весь беспредел Банковой.
Значит все, кто пострадал от рук «шестерок Зе» и не испугался, подал в суд на действия ТЦК и военкомов, могут легко его выиграть, и требовать посадок, и финансовой компенсации.
Интересно, что ещё мы узнаем через время?


[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]
🌐
Губернатор Флориды и республиканец-кандидат в президенты США Рон ДеСантис высказался, что принятие Украины в НАТО «не в интересах  безопасности США». И это ведь даже не Трамп. Нас ждут непростые времена президентской гонки в ключевом союзнике Америке.
@berezoview


[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] Якщо коротко дати характеристику тому, що відбувається по Криму і Керченській протоці - путіна нагинають і він змушений буде піти на поновлення зернової угоди, або ж втратить флот і судноплавство…


[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]
Созданный россией Киевский районный суд Симферополя арестовал Мемета Люманова, Азиза Азизова и Мустафу Абдураманова до 4 мая 2024 года
, сообщает «Крымская солидарность».
Все они были задержаны во вторник в ходе обысков, проведенных российскими силовиками в Бахчисарае.
«
Ребята с решением суда не согласны, и будут его обжаловать. Они на ногах с 4 утра им не давали есть. Сейчас их направят в СИЗО-2 Симферополя, где они будут содержаться во время следствия
», – рассказал адвокат Э
дем Семедляев
.
Ранее этот же
суд арестовал
 гражданского журналиста «Крымской солидарности»
Рустема Османова
.
Мемет Люманов, Азиз Азизов, Рустем Османов и Мустафа Абдураманов – активисты, регулярно посещали судебные заседания, помогали семьям крымских политзаключенных. Все четверо ранее задерживались российскими силовиками и привлекались к административной ответственности за проявление солидарности со своими осужденным единоверцам.
#россия_террорист


[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]
Четыре фиаско за неделю — визит  Зеленского в США и Канаду принёс Киеву одни проблемы
Хотя всё начиналось благоприятно для Украины, визит Зеленского в Северную Америку принёс Киеву дипломатические осложнения,
пишет
 Newsweek.
Поездка произошла на фоне размолвки с Польшей и завершилась появлением ветерана-нациста в парламенте Канады. А уже по возвращении появились новые заявления о причастности Украины к падению ракеты в Польше.
Кроме того, у Киева может возникнуть ещё одна проблема в связи с недавно опубликованными заявлениями о возможной причастности Украины к диверсии 26 сентября 2022 года, когда были взорваны газопроводы «Северный поток — 1» и «Северный поток — 2» на дне Балтийского моря.


[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]
Починаємо день з позитивних новин!
На росії вирішили порадувати матерей мобіков і заявили про збільшення випуску автомобіля Лада! «Автоваз» розраховує почати постачання авто у понад 300 дилерських центрів у травні.
Якраз планується український контранступ і плануються нові Лади. Все співпало!


[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]
🇺🇦

НАФТОГАЗ Украины
 предлагает
СКИДКУ
 на услуги ЖКХ, если потребители сдадут инакомыслящего соседа или уклониста.
Подписаться на канал


[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]
Рівненщина до кінця року забезпечена протитуберкульозною вакциною

💉
 В область
надійшла
 вакцина від туберкульозу (БЦЖ). Рівненщина отримала 40750 доз цієї вакцини. Така кількість повністю забезпечує потребу області до кінця року.
Про це повідомили в Рівненському обласному центрі контролю та профілактики хвороб.


[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]
СБУ сообщила о подозрении бывшему замначальника ДСНС Одесской области Александру Коротаеву.
По версии следствия, он присоединился к захватчикам и агитировал жителей Херсонской области вступать в батальоны, воюющие против Украины.
Ссылка на нас -
https://t.me/+XQJhAcf4zqUyZDMy
Одесса как она есть
 |
Наша инста
 |
Написать нам


[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]
На видео не просто пидар ватный, а тынанкегав григорий андреевич, 15.10.1939 г.р., уроженец города
г. Анадырь, чукотского автономного округа
Оно же старейшина «Ассоциации коренных малочисленных народов Чукотки», геолог


[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]
12 управленцев из Запорожской области стали «Лидерами Возрождения»

В Москве назвали победителей объединенного финала кадровых конкурсов «Лидеры возрождения», которые проводились в Донецкой и Луганской народных республиках, Запорожской и Херсонской областях. Из более 30 тысяч человек выиграли 104.

🏆
«Лидеров Возрождения» поздравили первый заместитель руководителя администрации президента России Сергей Кириенко, главы новых регионов.
«У нас сегодня есть огромная потребность в молодых, здоровых, умных, способных побеждать. Именно мы с вами пишем историю. Вы нужны своей стране, и новые регионы – это место, где каждый из вас может, получив социальный лифт, занять руководящие должности, применить свои навыки и таланты уже непосредственно в органах власти. В том числе в Запорожской области»
, - сказал врио губернатора Запорожской области
Евгений Балицкий
.

💪
Среди победителей –
заместитель министра экономического развития Запорожской области Богдан Кучеренко
.

«Победу в данном конкурсе воспринимаю как подтверждение своей компетенции перед руководством области. Я считаю, что человек должен быть носителем информации выбранной им профессии, поэтому на постоянной основе повышаю свои практико-теоретические знания»
, - подчеркнул
Богдан Кучеренко
.

«Важно, что одним из победителей кадрового конкурса стал участник от экономического блока нашей области. Нам нужны профессионалы и лидеры, готовые укрепить управленческую команду региона и сделать свой вклад в восстановление и развитие нового субъекта России»
, - отметил заместитель председателя правительства Запорожской области по экономике
Андрей Козенко.
Новый Мелитополь
💪
подпишись


[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]
«Не вагалася ні секунди»: лави ЗСУ поповнила ексдепутатка парламенту Швеції
Керолайн Норденгріп служитиме кадровим солдатом у званні сержанта в «Інтернаціональному легіоні».
Щоправда, у своєму Instagram вона поскаржилася на українську бюрократію, бо на збір усіх підписів та печаток вона витратила три дні.
▶
 Підписатись NC
📷

Прислати 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]
⚡
Путин согласился на продление зерновой сделки, - AFP со ссылкой на Эрдогана.
Доминируй, унижай
🤣
🤣
🤣


[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]
РЭМБО ПО УКРАИНСКИ.
Полная версия ближнего  боя с камеры украинского бойца. Видим, что позиция - это какой-то отдельный наблюдательный пункт в лесопосадке,  короткая неглубокая траншея и блиндаж на двоих. На позиции всего два человека. Воин вступает в ближний бой с штурмовой группой россиян. На видео хорошо заметно два солдата противника. В одного украинец стреляет из гранатомёта, второй подходит к траншее и наш боец его расстрелял в упор из автомата. Позицию удалось защитить, атака отбита. Настоящий подвиг, который достоин высокой награды.
Посмотрите видео.
Слава нашим воинам!