[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]
2024 год будет тяжелым для Украины на фронте - директор ЦРУ Уильям Бернс
В колонке для Foreign Affairs  он отметил, что этот год, вероятно, станет испытанием Украины на выносливость, последствия которого выйдут далеко за рамки героической борьбы страны за сохранение своей свободы и независимости.
По мнению Бернса, Путин думает, что время на его стороне, и его можно использовать для наращивания оборонного производства с помощью важнейших компонентов из Китая, а также вооружений и боеприпасов из Ирана и Северной Кореи.
Украина же должна "разрушить высокомерие Путина и продемонстрировать высокую цену продолжения конфликта для России", не только добиваясь успеха на фронте, но и нанося глубокие удары в тылу и в Черном море.


[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]
⚡
ЭТО ПОРАЗИТЕЛЬНО! Они воюют за ПОНИЖЕНИЕ уровня жизни!
https://rutube.ru/video/9268bb36eff554df0d1b1d0ae0d38aee/
Подписывайтесь на мой RUTUBE


[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]
Волонтёрская сводка по мобилизации в РФ за 18–19 февраля 2024:
Юлия Навальная продолжит дело мужа; Минобороны вербуют заключённых в женских колониях; вице-премьер Татьяна Голикова заявила, что к концу 2024 года «72% детей должны стать патриотами».
https://notes.citeam.org/mobilization-feb-18-19-2024


[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]
Кара з небес на голови окупантів.
🔥
Підтримуйте
110 окрему механізовану бригаду імені генерал-хорунжого Марка Безручка
 та переконуйтесь в ефективності вашої допомоги!
#110омбр

#на_захисті_України

#прямовціль

#вір_у_ЗСУ

#ОК_Північ


[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] Жеребянки  ... прощупали не кассетными боеприпасами ( Американского производства ) осетинских продавцов вареной кукурузы ...


[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 Дрогобыче Львовской области вручили повестку

инвалиду 1й группы с детства, парень без обеих рук, Руслан К.
Ему вручили мобилизационную повестку и отправили на медкомиссию, блять
‼
Которая, не усмотрев поставленный с детства диагноз и выписку МСЕК, все же отправила его на мед. комиссию по мобилизации
‼

Суки, может может вы, его еще и годным к службе признаете?
В Киеве также включен режим тотальной мобилизации. Вслед за Голосеевским и Печерским районами, приказы по мобилизации расклеиваются и в Подольском районе, военкомы оставляют повестки также в дверных проемах (такой способ вручения повестки, не является законным).
❗
Мы неоднократно
писали
, что
буквально перед новым годом и в Киеве ужесточились мобилизационные мероприятия, которые активно ведутся по всей Украине. Повестки в столице начали массово раздавать на улицах и по домашним адресам,
 чего в 2022м не наблюдалось.
Призывают массово и всех подряд


[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]
❗
Функції та завдання Фонду соціального страхування України за загальнообов’язковим державним соціальним страхуванням у зв’язку з тимчасовою втратою працездатності та від нещасного випадку передаються Пенсійному фонду України.
Фонд соціального страхування України розпочинає процедуру припинення.
ℹ

Закон України від 21.09.2022 № 2620
, а також постанови Кабінету Міністрів України
 від 15.11.2022 № 1289
 та
від 02.12.2022 № 1350
, якими внесено відповідні зміни до законодавства про загальнообов’язкове державне соціальне страхування та до рішень Уряду, набули чинності 01 січня 2023 року.
Відтепер уповноваженим органом управління в системі загальнообов’язкового державного соціального страхування є Пенсійний фонд України.
Докладніше
⤵


[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 багатьох країнах світу мені доводиться пояснювати, із яким злом зіткнулася Україна
 й чим воно загрожує, причому не лише нам. А всьому світові. Доводиться пояснювати, чому агресію треба зупиняти вчасно – до того, як вона заразить світ, немов епідемія.
🇱🇻

Але в Латвії цього точно не треба пояснювати.
Бо тут, на жаль, знають це з власного досвіду.
Багато найважчих історичних етапів наші країни пройшли разом.
Зокрема й імперську окупацію. Депортації, винищення нації, мови, відмова в самому праві на існування – у Латвії знають, що несе із собою російський імперський світ. Знають, що таке боротися за волю й за право бути собою на власній землі.
📍
Сьогодні в Ризі разом із міністром закордонних справ Латвії Кріш'янісом Каріньшем
рада була зустрітися з представниками латвійської та місцевої української громад.
 Їх зібралось понад 90 (!) – одночасно державні й недержавні організації, благодійні фонди та активісти, великі компанії та приватні бізнеси, лікарі й цілі лікарні, педагоги, підприємці, музиканти, центри реабілітації, релігійні діячі, громадські спілки, юристи та правозахисники, мерії, ветеранські спілки, банки, транспортники…
🫂

Уся Латвія, уся її людяність і небайдужість ніби втілилася в одному залі.
 І як же багато серед організацій та спілок тих, чия дата створення – 2022 рік – означає, що вони з’явилися спеціально, щоб допомагати Україні.
🤝

Подякувала всім і кожному особисто
за порятунок наших людей, прихисток, навчання й роботу, лікування та реабілітацію для поранених, зокрема воїнів.
🇺🇦

Подякувала потужній українській діаспорі Латвії
– підприємцям, учителям, науковцям, митцям – людям різних занять і доль, яких єднає батьківщина в серці. Які допомагають співвітчизникам в Україні та Латвії.
І вдячна всім благодійникам, освітянам, лікарям
, хто допомагає нашим дітям вчитися та зберігати здоров’я й можливості – в Україні та в Латвії.
Тим паче що не буває маленького добра.
 Воно завжди велике. Завжди більше за зло. Ви більші за зло, коли робите добро.
Дякуємо, Латвіє! Слава Україні!


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

### Classification Guidelines

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

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

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

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

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

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

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

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

[User]
Питаєте про знищені днями 2 одиниці С-300.
Скажемо так - ми подібного не бачили.
Якщо ви розумієте, про що ми.


[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]
❗
💥
Этой ночью Севастополь атаковали надводные беспилотники ВСУ.

Как сообщил губернатор Михаил Развожаев, Черноморский флот отбил нападение. Один надводный беспилотник был уничтожен силами противоподводно-диверсионной обороны (ППДО), еще один взорвался сам. Объекты в городе не повреждены. Все службы города находятся в полной готовности.

На видео — момент уничтожения одного из вражеских БПЛА.

@newmelitopol


[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]
⚡
Сезон дрифту відміняється.
👮🏻♂
Хлопцям на зяряджених тачках у центрі Києва вручили повістки. Щовечора поблизу Олімпійського та парку Шевченка автолюбителі збираються, щоб кічнути новими обвєсами і движками.
🏎
Пацани зазвичай їздять без глушників, від чого люди хапаються за серце. Бо кожного разу здається, що десь ляпнув припас.
Тепер у армії буде більше водіїв-механіків. Ця спеціальність одна із найбільш необхідних.
🤔
Тепер питання:
Служба: честь, чи покарання?

[цікаво? то підпишись на канал]


[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]
Легендарный одесский ресторан Дача рискует стать частной дачей главы областного совета Григория Диденко.
Областной совет подал иск в суд об отмене права собственности известного ресторана. Событию предшествовало требование к хозяину оказать материальную помощь семье
Григория Диденко в размере 800 000$.
В противном случае Диденко обещал отобрать территорию ресторана для нужд своей семьи. Ресторан Дача должен удовлетворять гастрономические аппетиты одесситов, а не коррупционные аппетиты шайки Диденко.


[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]
⚠

Росія щодня втрачає до 1500 солдатів у боях за Бахмут
– НАТО
"Росія втрачає 1200, 1300 і навіть 1500 солдатів на день у ході поточного наступу, переважно в Бахмуті. Співвідношення вбитих і поранених становить один до трьох"
Хочу нагадати, хто не в курсі, Бахмут - це одне з 52 міст в донецькій області. А вони Київ за три дні хотіли
🌝


[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]
СБУ затримала на Сумщині ворожу інформаторку, яка «зливала» окупантам дані для можливого прориву російських ДРГ через кордон

Особливо ворога цікавила дислокація підрозділів ЗСУ і Держприкордонслужби, які забезпечують захист обласного центру і державного кордону на північно-східних рубежах України.
Розвіддані потрібні були загарбникам для здійснення прицільних повітряних атак по позиціях українських захисників та можливої підготовки «прориву» російських ДРГ на територію регіону.
Однак, усі ці «плани» окупантів було зірвано співробітниками СБУ, які своєчасно викрили ворожу інформаторку, задокументували її злочинні дії та затримали.
Зловмисницею виявилась проросійськи налаштована мешканка Сумського району, яка активно поширювала власні антиукраїнські дописи.
У разі успішного виконання завдань окупати обіцяли своїй поплічниці «безперешкодне» оформлення російського громадянства з «перспективою» подальшого виїзду до рф.
https://t.me/operativnoZSU


[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] Зустрівся з головою фракції Соціал-демократичної партії Німеччині в Бундестазі ФРН Рольфом Мютценіхом та лідером СДПН Ларсом Клінгбайлем. Наголосив на необхідності посилення підтримки України - озброєнням, навчанням наших військових, фінансовою допомогою.


[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]
Хтось продовжує дерусифікувати Полтаву підпільно.
Намалювали
 патріотичний символ на пам‘ятнику. Також зафарбували радянську зірку.
У центрі Полтави досі лишається пам’ятник братам Литвиновим, які були чекістами та брали участь у знищенні армії УНР.
ТРУХА
⚡
Полтава
Надіслати 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]
Хрен им, а не наши дети!
https://peremogi.livejournal.com/66802648.html
#аберрация сознания

#перемога опасносте!

#ублюдки и выродки просто сдохнут


[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]
Завдяки небайдужим громадянам перші «ударні роти БпЛА» отримали сучасні автівки
В рамках проєкту “
Армія дронів
” продовжується формування перших «ударних рот БпЛА».
Допомогти у їх доукомплектуванні зголосився Андрій Веселий - український підприємець, засновник БФ «Родини Веселого». Завдяки відповідальній роботі волонтерів та партнерів Фонду вчора було передано на потреби однієї з таких рот два нових пікапи Mitsubishi L200.
З 2014 фонд допомагає ветеранам Майдану та АТО (ООС), сім'ям загиблих захисників та тим, хто зараз протистоїть окупантам.
Долучитися до укомплектування «ударних рот БпЛА» може кожен.
Переможемо разом!
@AFUStratCom
 - найактуальніші і найсвіжіші новини


[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] Фейковые аккаунты Сирського были созданы враждебными спецслужбами и использовались для распространения лжи о ситуации на фронте. Эту информацию раскрыли специалисты Центра протидействия дезинформации и кибердепартамента СБУ, которые обнаружили мережу фейковых аккаунтов в социальных сетях.


[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]
Ну те, що Жернаков хворий на голову, ми з вами без усіляких ВЛК й довідок знали.
Але ж плоскостопість? У законопроекті по посиленню мобілізації, що Кабмін відкликав на доопрацювання взагалі пропонувалась призивати інвалідів другої та третьої групи.
Насправді ситуація навколо не доброчесності Жернакова це чергова історія, яка наочно демонструє брехню і цинізм наших так званих «реформаторів» та «експертів».
https://t.me/zib_news/9198