[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]
Міністр Кубраков вирішив продемонструвати всій країні, як буде виглядати відновлення. Після підриву Каховської дамби виникла потреба будівництва водогонів до Кривого Рогу та Марганця. На початку червня Денис Шмигаль
заявив
, що вартість будівництва буде складати 1,5 млрд грн.
Але вже 19 липня Служба відновлення та розвитку інфраструктури у Дніпропетровській області уклала два договори на будівництво водогонів: один на 5,32 млрд грн, інший на 5,3 млрд грн. Тобто за місяць вартість будівництва виросла 1,5 млрд грн до 10,6 млрд грн. А хто ж переможець? О чудо, це Автомагістраль - Південь та Автострада, які входять до картелю Великого будівництва, і потрапили туди дякуючи одній людині - Кубракову.
На цьому цинізм цієї історії не закінчується. Через тиждень після виданих 10,6 млрд грн, раптом виявилось, що потрібно ще
провести тендери на консультації
, і на це виділяють ще 350 млн грн! Тобто консультації купили навіть не до основного тендеру, а після. Не дивно, що ніяк не можливо порахувати нормальну вартість цієї історії.
Так сталось, що я знаю експерта, який брав участь в оцінці будівництва водогону і разом з іншими вийшов на суму в 1,5 млрд грн. На днях він спитав мене “Вітя як? Там навіть з корупцією не більше 2 млрд грн. Невже в них настільки немає совісті і міри?”. Ні, не має.
Ті, кого не навчила доля Януковича - будуть змушені її повторити.


[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]
🇺🇦
🏂
🥈

Надія Гапатин здобула «срібло» етапу Кубка Європи зі сноубордингу!
Сьогодні в італійському Рачинесі було розіграно перший комплект медалей у паралельному слаломі в рамках фінального етапу Кубка Європи.
Представниця Закарпатської області уперше у кар'єрі піднялася на п'єдестал пошани змагань такого рівня. У великому фіналі Надія Гапатин поступилася лише спортсменці з Австрії.
Для Надії — це друга медаль в сезоні. У січні вона здобула «бронзу» на Універсіаді у Лейк-Плейсиді.
👏
Вітаємо з нагородою Кубка Європи нашу спортсменку та тренерський штаб команди! Завтра, 19 березня, знову вболіватимемо за українців у паралельному слаломі.

📷
Alex Spisak
#націяпереможців

#міжнароднізмагання

#олімпійськийспорт

#сноубординг


[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]
💥
Атаку безпілотників на Брянськ підтвердив й місцевий губернатор. Тим часом мінвбивства рф стверджує, що один із двох безпілотників було знищено в повітрі.
Підприємство яке "зачепив" БПЛА, "Кремній Ел" — є найбільшим в росії заводом з виробництва мікроелектроніки. Він брав участь у російській федеральній програмі "Альтернатива" з розробки деталей та пристроїв для ППО, літаків, кораблів тощо.


[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]
Для прикладу. Якщо ми демобілізуємо всіх, хто на службі більше 18 місяців, то ми повинні відразу віддати росії всю лівобережну Україну.
Ви згодні на такі умови?
🤔


[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]
😱
Я був у шоці, коли підписався на цей канал, раджу всім!
На відео показано те, що будь-якої миті може статися з нашою планетою.
Переконайтеся самі: на каналі
Наука та Факти
  розмотують на шматки міфи про те, що Земля плоска, а космос — вигадка та монтаж.
⚠

Попереджаю
: обережно гортайте
цей канал
, ви можете пропасти в телефоні на довгі роки.


[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]
200-а омбр ЗС рф частково втратила позиції в районі села Григорівка на Бахмутському напрямку
Інформація потребує підтвердження (!) Повідомляють, що підарів було вибито з двох опорних пунктів


[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]
📌
Сотрудники центра занятости посетили угледобывающее предприятие Лутугинского района
Специалисты территориального отделения Государственного учреждения — Республиканский центр занятости Луганской Народной Республики в Лутугинском районе посетили предприятие угледобывающей промышленности ПП «Шахтоуправление Луганское» ГУП ЛНР «РТК „Востокуголь“.
- Руководство предприятия отметило, что у шахты есть будущее, несмотря на сложности, а это новые рабочие места, - отметила специалист центра занятости.
Предприятию требуются квалифицированные рабочие. На сегодняшний день в территориальное отделение предоставлено множество вакансий. Шахта обеспечена проходческой техникой, иногородним предоставляется транспорт, гарантирован социальный пакет. Регулярно выплачивается достойная заработная плата.
Встреча прошла в тесном диалоге. В ходе визита работодатель смог задать все интересующие его вопросы, а также подтвердил свою готовность и в дальнейшем продолжать активное сотрудничество с территориальным отделением.


[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]
У истории с внесением австрийского Райффайзенбанка в украинский список «спонсоров войны» (из-за чего Австрия наложила вето на 12-й пакет антироссийских санкций ЕС и отозвало его после того, как банк из списка исключили ) есть и практический аспект.
На фоне претензий от украинских властей в том, что Райффайзен продолжает работу в РФ, его украинскую «дочку» отстранили от программы кредитования бизнеса «5-7-9» (кредиты для бизнеса под маленький процент), который приносит банкам неплохой доход.
И до сих пор, к слову, в этой программе не восстановили.
«К сожалению, Райффайзенбанк не восстановил работу по кредитной госпрограмме 5-7-9%. Как только вернёмся к ней - будет официальное заявление руководства. Мы приостановили работу по 5-7-9% около года назад", - сообщили "Стране" в банке.
На сайте Фонда развития предпринимательства (ведёт программу 5-7-9%) Райффайзенбанка также нет в списке банков-партнеров.
Является ли возвращение банка в эту программу одним из условий Австрии по снятию вето на антироссийские санкции - увидим в ближайшее время.


[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]
Арестович. Новая версия событий в Днепре.
Советник Офиса президента сегодня уже не уверен, что ракету, попавшую в жилой дом в Днепре,
сбила ПВО.
Он написал новый пост в Facebook, в котором объяснил, что
версию, озвученную им, ему "подсказал" знакомый бывший ПВОшник из Днепра
. И на самом деле,
это было лишь предположение Арестовича, а не утверждение.
 И вообще Арестович сильно устал, а зритель должен сам во всем разобраться.
"Мой товарищ, бывший ПВОшник, шёл по улице в момент попадания. Он мне сказал, что на 100% слышал два взрыва и опознал, первый как работу ПВО. До сих пор он не ошибался. И был уверен, что ракету сбили.
Месседж должен был бы звучать так:
 - Есть версия, что сбила ракета ПВО. Есть такие-то признаки в ее пользу. Есть другая версия - прямое попадание в дом.
А потом - то да се, да отвлеклись, да пошли по другой линии, это живой эфир. Если бы я был хоть чуть-чуть в форме, то вытянул бы линию"
, — написал советник ОП.


[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]
❗
Торгівельні організації Бельгії, Австрії, Нідерландів та Італії закликали «всі компанії ЄС» бойкотувати польських автоперевізників, що блокують кордон з Україною
🇺🇦
Підписатись

|
Запропонувати новину
✍


[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]
Нацполіція передала Збройним силам України арештовані двигуни до танків та броньованих машин

Спритники намагалися продати це майно, походження якого встановлюється, Міністерству оборони України. При цьому різниця між фактичною вартістю та комерційною пропозицією становить понад 53,7 млн грн.
Двигуни та інше військове обладнання до військової техніки, а також прилади нічного бачення вилучили та арештували в рамках розслідування кримінального провадження.
🔫
 Зокрема йшлося про двигуни до
броньованих машин і танків, у тому числі Т-72
.
За результатом проведеної експертизи, вартість вилучених двигунів становить 23,4 млн грн. Натомість спритники
планували продати його державі за понад 77 млн грн.
🔗
Деталі

🎞
Відео


[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]
🤯
 Отже,
понеділок минув, Штати не розвалились
. Значить, що? Значить тепер Україна ця ваша розвалиться, звичайно ж. В любой момєнт. Очєнь даже можливо, що вже навіть прямо завтра.
Ждьом, що вже там.


[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]
❗
Помощники Байдена обнаружили новую партию секретных документов — вне связанного с президентом вашингтонского офиса, — СМИ  Подписаться / Прислать новость


[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]
Очередные злоупотребления АРМА - топслужащий написал жалобу Главе государства и был уволен с должности
Владислав Романов, экс-руководитель Центрального межрегионального управления агентства,
обратился
 к Зеленскому с призывом предотвратить огромные риски для Украины, которые наносит АРМА во главе с А.Дума. В своем письме он указывает, что "властные полномочия фактически переданы внештатным советникам, а Председатель постоянно общается с коллегами матом и криком".
Как пишет топ-сотрудник АРМА, один из таких советников Вадим Грона под запись камер видеонаблюдения избил Романова и при свидетелях угрожал ему расправой после отказа выполнять незаконные поручения в отношении активов крупного производителя минеральной воды. Кроме того, он вместе с другим таким советником, решали судьбу сети "заправок Медведчука" Glusko на заседании, где вообще не было профильных официальных представителей АРМА.
В.Романов призывает привлечь к ответственности виновных должностных лиц и обеспечить соблюдение норм законодательства.
Сейчас Романов уволен, а руководитель АРМА Елена Дума
рапортует
 в своем ТГ-канале об "очищении рядов" АРМА.
Инсайдер UA  |

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]
Угорщина заявила, що не блокуватиме наступний транш військової допомоги Україні від ЄС сумою €500 млн.
А що случілось вже?


[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]
❇

100 лет – это только начало
Под таким названием в Амвросиевке прошел праздник в честь 100-летнего юбилея района.
Реализовали проведение праздника Амурский областной благотворительный фонд «Твори добро», при помощи Ресурсного центра НКО в Донецкой Народной Республике. Праздник состоялся при поддержке шефа-региона – Амурской области – и выигранного гранта от Президентского Фонда Культурных Инициатив.
Представитель Ресурсного центра поддержки НКО ДНР Наталья Заика рассказала, что для жителей было подготовлено несколько локаций с праздничными мероприятиями. Организаторы закупили техническое оборудование, сделали праздничное украшение всех локаций, закупили подарки для детей, а также пригласили к выступлению музыкальные коллективы из других регионов России.
Читать полностью...


[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] В ответ на достоверную информацию ряда западных и украинских журналистов , что в Украине шла подготовка исламских боевиков , к штурму жилых зданий и управлению коптеров А так же участие анисемитов нациков с Украине в инструктаже некоторых отрядов ХАМАС , у Зеленского выкатили фэйк, что якобы Вагнеровцы готовили Хамасовцев


[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]
💪🏻

Кордон з Білоруссю посилено новоствореними механізованими бригадами ЗСУ
Ці підрозділи призначені для ведення бойових дій у лісовій та болотистій місцевості. Тому боятися можливого нападу або провокативних дій зі сторони РБ – не треба.
👉

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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]
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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] «Па ходу, навєлі на нас хтота», - після того, як влучив снаряд, російські окупанти тікають з будинку, який займав їхній підрозділ.