[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
Приніс вам нового срачу.
Залужний типу призначив помічником-консультантом Тетяну Ефремову.
Тетяна красива жінки з Полтави і «Свободи», яка не приховувала в своїх соцмережах ні красу, ні політичні вподобання.
Обидва ці факти підривають пердаки вкраїнчикам.
Чому? Тому що наше суспільство ще досі оцінює людей за зовнішністю. Сказати по суті про кваліфікацію чи інтелект не можуть, але судять за світлинами. Мізогінія і лукізм (гуглимо) це совкова дрімучість, яка притаманна більшості наших співгромадян.
Друге: в цій країні люди МОЖУТЬ БУТИ з різними політичними вподобаннями. І від цього вони гіршими людьми не стають. Я знаю прекрасних людей з обох таборів. Так само я маю претензію до всіх лідерів цих таборів (бо я така людина). Але я завжди топитиму за плюралізм думок (і русофобію).
Мене одночасно засмучує і тішать такі срачі. Тішать, бо це означає що люди продовжують жити, а не очкувати, і назовні полізли старі травми, які ми лікуємо срачами. А засмучує, бо люди абстрагуються від війни і починають сратися. А ворог наступає нонстопом.


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
❗
Прежде всего, нам нужно решать задачи в рамках СВО
— Путин
«Нам нужно укреплять обороноспособность, укреплять вооруженные силы. Это происходит очень хорошим темпом и в хорошем качестве»,
 — сказал Владимир Путин.
Все основные задачи развития страны были озвучены президентом в послании Федеральному Собранию РФ:
«Наша задача заключается в том, чтобы, используя доверие граждан РФ, сделать все, чтобы поставленные задачи были решены, в цели достигнуты»,
— подчеркнул Путин.


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
❗
Пентагон оголосив про новий пакет військової допомоги Україні на суму 2,6 млрд доларів.
 Як і очікувалося, пакет включає боєприпаси, в тому числі:
- Додаткові боєприпаси для ЗРК Patriot;
- Додаткові боєприпаси для HIMARS;
- 155-мм та 105-мм артснаряди;
- 120-мм мінометні міни.
- ПТРК «Джавелін»
- авіабомби JDAM


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User] Рокош показав, що єдині, ким править путька - це лохи, з категорії "як би чого не вийшло", "знімайте тапки перед лавочкою", "ненасильницький протест", "кульки і ліхтарики". Тим-то росліберда і така небезпечна, адже, як тільки що блискуче продемонстровано - режим путьки тримається виключно на ній. Всілякі там муратови і веніки грають роль козлів у отарі овець, виконуючи розпорядження старого, хворого, клишоногого, лисого вовка, який чатує за кошарою. Як тільки прибіг пес, вовк одразу розчинився у повітрі. А барани як ходили біля кошари та мекали, так і ходять.


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
Уважаемые сотрудники и ветераны органов внутренних дел! Сердечно поздравляю вас с профессиональным праздником!
Сегодня мы чествуем тех, кто надежно защищает права граждан, свято бережет честь мундира, служит интересам Отечества и народа.
Мужество, решительность и твердость характера – неотъемлемые черты каждого сотрудника. Благодаря самоотверженному труду, верности долгу, готовности к работе в самых сложных условиях, вы решаете одну из важнейших государственных задач – укрепление законности и правопорядка на родной земле.
Выражаю особую признательность ветеранам. Ваш бесценный опыт служит нынешнему поколению надежным ориентиром в выборе дальнейшего пути. Своей активной жизненной позицией вы показываете пример честного и беззаветного служения Родине.
От всей души желаю вам несокрушимого здоровья, успешной службы, чтобы все дела были решаемы, а преступления – раскрываемы. Спокойствия и уюта вам и вашим семьям. Пусть ваш труд всегда будет оценен по достоинству, заслуженные награды своевременно настигают вас, а жизнь окружает только благодарными людьми.


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
Так бывает, когда на втором году войны Владимир Яценко ограбивший ПриватБанк, пытавшийся сбежать с Украины теперь заказывает у правоохранительных органов уголовные дела против военных.
Владимир Яценко ключевой фигурант по воровству денег из ПриватБанка.
Военные в первые дни войны на абсолютно пустых участках земли построили наблюдательные пункты и укреп районы, пришел Владимир Яценко и сказал убирайтесь нахрен, это моя земля.
Закончилось все тем, что Владимир Яценко занес денег и против военных открыли уголовное дело, что они без ведома Яценко на его земле построили военные сооружения.
Вдумайтесь, главный фигурант по воровству денег из ПриватБанка на втором году войны заказывает военных.
Владимир Яценко это такая мразь, которая сейчас пытается из себя волонтера сделать, но каждое свое якобы доброе дело сразу за деньги размещает в разных СМИ.
На пиар своих добрых якобы дел Владимир Яценко тратить больше денег, чем на сами добрые дела.
Яценко, ты идешь нахер или пострадаешь и ты и подставишь всех связанных с собой людей.
Животина, ограбила ПриватБанк на 5 ярдов и пытается дергаться еще.


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
👀
Общественные активисты подозревают руководство автотранспортного предприятия при Днепроблсовете в хищении средств на топливе
‼
«Громконтроль» опубликовал исследование, в котором говорится о чрезмерной трате денег на горючее для должностных лиц Днепропетровского облсовета, пользующихся транспортом КП «Спецавтобаза»
По расчетам ГК, коммунальное предприятие приобрело более 100 тысяч литров топлива в 2023 году.
Такого количества должно было хватить для того, чтобы проехать аж миллион километров.
На КП проводили проверку, чтобы выявить возможные нарушения в использовании бюджетных средств.
Ее результаты пока не опубликовали .
🤷♂


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
ДЕТИ - ЦВЕТЫ ЖИЗНИ
Пока простые работяги сидят в окопах, бегают от военкомов или пытаются любыми способами сбежать за границу, лучшие люди Украины живут в целости и сохранности.
😎
Вашему вниманию -
дети председателя Верховной Рады

Руслана Стефанчука
. Его сынок не захотел прожигать лучшие годы жизни в стране, находящейся в войне.
Папочка заботливо спрятал его и дочку в Вене, где теперь они могут жить так, как и заслуживают - пиво, пицца, Balenciaga, Gucci.
🥳
Для сына элиты все дороги открыты. И без него есть, кому гибнуть на этом фронте.


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
⚡
«World War III», — Дональд Трамп в Twitter намекнул, что мир находится на пороге третьей мировой войны.


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
Жахлива трагедія в Константинівці на Донеччині. Мерзенні рашистські терористи завдали удару по міському ринку. Повідомляють про 16 загиблих, серед них дитина. Десятки поранених. Мерзота чудово знала, куди цілить. Це недалеко від фронту, там у них море навідників і корегувальників, що передають дані в режимі реального часу. Вони гарантовано вибирали момент, коли на базарі, а це центр життя в прифронтовому понівеченому місті, буде якнайбільше цивільних місцевих мешканців. Ні про яких військових не йшлося. Виключно терор проти простих людей.
Співчуття й скорбота за загиблими й ненависть до рашистської гиді...


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
🇺🇦
 Звернення Президента України
Є нові санкції РНБО. Більш ніж 400 осіб та компаній. Більшість із них – російські. Це ВПК держави-терориста. Але також це іранські та сирійські особи, ті, хто допомагає терору.
Кожен такий наш санкційний крок ми ретельно опрацьовуємо. Українські санкції – це частина глобального тиску на Росію. Усі, хто виробляє зброю для терору проти України, хто допомагає Росії розпалювати агресію, зокрема постачаючи «Шахеди», хто підтримує російську руйнацію міжнародного права, можуть бути лише маргіналами для світу.
Є лише один суб’єкт, який руйнує життя: це Росія. Є лише один спосіб захистити життя: треба вигнати російську армію з української землі. І ми це зробимо.
Слава всім нашим воїнам, які зараз у бою! Слава кожному і кожній, хто допомагає! Дякую всім, хто тренує наших бійців, хто лікує від поранень, хто волонтерить і хто підтримує бойовий дух українців!
Слава Україні!
💛
💙


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
Сильные женщины живут в Украине
🫡
Буквально оттащила своего мужчину от этих
🤡
. Ну, а как по-другому ?…


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
Служба безопасности Украины расследует уголовное дело из-за неуспешной попытки украинских военных угнать российский самолет
Они разработали план, который, по словам участников операции, предполагал, что россиянину дадут "квадрат", в котором он в оговоренное время влетит в воздушное пространство Украины.
Перед российскими диспетчерами пилот должен был имитировать аварию. Украина объявила бы о взятии пилота в плен. Россиянин и его семья получили бы деньги и новые документы.
По договоренности, россиянин с самолетом должен был сесть 23 июля на аэродром "Канатово" под Кропивницким. Однако пилот не прилетел, аэродром в то утро обстреляли российскими ракетами. Один украинский военный погиб, были повреждены здания и военная техника.
Подписаться


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
Адмін телеграм каналу який поширив відео удару.... Зняв на відео себе та друзів.
Прикро і бридко було спостерігати його у військовій формі.
Написавши йому в особисті на телеграм каналі він мене заблокував. Смішно.
Не забудь ще закрити ТікТок та Інсту. За тобою вже виїхали.
Зробив він, а соромно мені.
@a_shtirlitz


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
🇸🇰
 Президентка Словаччини Зузана Чапутова
виступила проти
 надання Україні нового пакета військової допомоги, який підготував попередній уряд, у зв’язку з результатом парламентських виборів, де перемогла антиукраїнська партія Smer-SD — Dennik N.
Речник Чапутової пояснив, що вона таким чином "поважає результати демократичних виборів", переможець яких обіцяв виборцям "не давати Україні жодного патрона"
🔻
підписатися


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User] Не пустимо їх на Констаху. Це одна новина з візиту Сирського, бо задача відкинути їх подалі від траси.


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
🔰
KRAKEN: Історія спецпідрозділу Головного управління розвідки

МО
Як харківські ветерани Азову, представники Національного Корпусу, футбольні фанати та добровольці стали кістяком спецпідрозділу ГУР МОУ «KRAKEN». Якою зброєю воює сьогодні підрозділ, який починав зі звичайних автоматів Калашникова. Чому Кракен демонизують пропагандисти, політичне та військове керівництво рф.
Репортери “Української правди” проїхали від Харкова до Балаклії та Куп’янська разом із військовослужбовцями та офіцерами спецпідрозділу, які частково розкрили таємниці та подробиці ключових операцій зі звільнення населених пунктів Харківщини.
https://cutt.ly/Q8Dvs1g
СПЕЦПІДРОЗДІЛ ГУР KRAKEN
☠


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
💪
Закарпаття
приєднується
 до проєкту «Пліч-о-пліч всеукраїнські шкільні ліги». Про початок масштабних учнівських змагань в області повідомив голова ОВА Віктор Микита.
➡
 Учасники змагань виборюватимуть першість у таких видах спорту, як баскетбол, волейбол, футзал, спортивне орієнтування та черлідинг.
Спершу це будуть шкільні змагання, потім – на рівні громади, району та області. На фінальному етапі визначать переможців серед регіонів
🏆
Нагадаємо, що зареєструвати свою учнівську команду в президентському проєкті «Пліч-о-пліч всеукраїнські шкільні ліги» можна до 1 лютого 2024 року за
посиланням

👈
ЗакОВА>підписуйся


[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
Порушення зафіксоване WIM-системою: за перевантаження вантажівки перевізник сплатить 17 000 грн
☝
Дніпропетровська область (
WIM

60
). Порушник — вантажний автомобіль марки
DAF
(на фото).
🛣
WIM-система зафіксовала рух ТЗ із перевищенням нормативних параметрів загальної маси транспортного засобу на 19.13% (7.65 т): 47.65 т при дозволеній максимальній фактичній масі 40 т.
Відтак, перевізник сплатить штраф у розмірі 17 000 грн.
⚠
Зверніть увагу
Нині діють такі штрафи за порушення габаритно-вагових норм:
📌
від 5 % до 10 % – 8 500 грн;
📌
від 10 % до 20 % – 17 000 грн;
📌
від 20% до 30 % – 34 000 грн;
📌
понад 30% від встановленої норми – 51 000 грн.
Актуальні реквізити для сплати
🛣
WIM-штрафів можна переглянути за
посиланням.
👮♂
Укртрансбезпека закликає перевізників дотримуватися встановлених габаритно-вагових норм. Це дозволить зберегти дорожню інфраструктуру та уникнути непередбачених видатків.
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[System]
You are an expert in the fud manipulation technique. FUD (Fear, Uncertainty, Doubt): Sows fear, uncertainty, and doubt to manipulate perception and discourage opposition. List phrases that provide evidence of fud in the given sentence if they are present. Given the evidence, make a judgement whether the sentence contains the fud manipulation.
To effectively refine your classifier for detecting the Fear, Uncertainty, and Doubt (FUD) language manipulation technique in the provided texts, it is critical to establish clear classification guidelines. We need to reduce false positives by avoiding misclassification of benign or neutral phrases, and reduce false negatives by ensuring timely detection of FUD-related content. Below are recommended guidelines tailored for you:

### Classification Guidelines

1. **Identify FUD Keywords and Phrases:**
   - Search for words that evoke strong emotions related to fear, confusion, or doubt. Examples include:
     - Fear: "страшно", "жах", "катастрофа", "вбивство"
     - Uncertainty: "можливо", "нема гарантій", "неясно"
     - Doubt: "не вірю", "здається", "сумніви", "начебто"
   - Note that while exact matches are useful, variations and derivatives of these words should also be included (e.g., "страх", "боятися", "паніка").

2. **Contextual Analysis:**
   - Evaluate the context in which the keywords are used. A phrase might contain a FUD keyword but could be in a context that is neutral or critical rather than manipulative. For example:
     - Statements like "таке трапляється" or "можливо, це не правда" can often indicate a doubt but should not automatically be classified as FUD if they are providing critical analysis or commentary.
   - Use surrounding words and verbs as signals to determine the intent behind statements.
     - For example, "прогноз" (forecast) or "може" (could) without further stressful qualifiers may signify speculation rather than manipulation.

3. **Sentiment Analysis:**
   - Implement sentiment analysis tools to determine the emotional tone of the statements. FUD language often has a negative sentiment associated with:
     - War, loss, crises, and disasters.
     - Look for shifts in sentiment when certain keywords are clustered together, indicating a potential FUD message.

4. **Avoiding False Positives:**
   - Be cautious with phrases related to future events that do not carry an emotional charge, such as:
     - "невдовзі настане день звільнення Криму" (the day of Crimea's liberation will soon arrive) may not qualify as FUD unless framed within a context of threat or fear.
   - Exclude neutral phrases even if they contain strong words. For example:
     - "введення санкцій – це добре" (the introduction of sanctions is good) should not be classified as FUD despite the gravity of the subject.
   - Regularly update the classifier with new terms and phrases as the prevailing discourse evolves to keep it current.

5. **Recognizing FUD Patterns:**
   - Patterns can help you capture FUD efficiently. For example:
     - Repeated warnings about threats or dangers in close succession can indicate a FUD intent.
     - "знищать", "губити", "загрожує" (destroying, killing, threatening) can be part of FUD linguistic constructs when coupled with an exaggerated tone.

6. **Classification Hierarchies:**
   - Use a tiered classification system that weights phrases according to how strongly they evoke fear, uncertainty, or doubt.
     - Tier 1 (low FUD): Neutral reporting or analysis devoid of emotional undertones.
     - Tier 2 (moderate FUD): Acknowledgement of threats without exaggerated or alarming language.
     - Tier 3 (high FUD): Alarmist statements that clearly seek to induce fear or doubt among the audience.

7. **Continuous Learning:**
   - Regularly test and evaluate your classifier using both false positive and false negative cases. Machine learning algorithms require ongoing adjustments, so use user feedback, expert reviews, and real-world examples to continue improving your model.

By employing these guidelines, your classifier should more effectively identify FUD-related content while minimizing false classifications. This will enable you to respond more accurately to problematic content that seeks to manipulate perceptions through fear, uncertainty, and doubt.

[User]
Украинские детишки копируют своих родителей, издеваясь над слабыми и беззащитными.
В селе Шевченково в Одесской области произошел отвратительный инцидент: школьники издевались над одноклассником из многодетной семьи, снимая  пытки на видео. Ребенка окунали головой в яму уличного туалета и заставляли бегать голым по школьному двору. Одним из зачинщиков был сын школьной учительницы которая резко слегла в больничку, типа из-за морального потрясения. На самом деле, “педагогиня» пытается уйти от ответственности: ей светит уголовное разбирательство.
Департамент образования области провел расследование и пришел к выводу, что «инцидент в школе является исключительным случаем, а не системным явлением». И  пожурил дирекцию школы- мол, недостаточно реагировала на проблему буллинга. Директора и заместителя директора привлекли к дисциплинарной ответственности и выделили денег на установку камер наблюдения в школе.
Детский омбудсмен и министр образования отмолчались.
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