insight - NLP Research - # Propaganda Detection in Code-Switched Text

Detecting Propaganda Techniques in Code-Switched Social Media Text at MBZUAI

Q: How can the detection of propaganda in low-resource languages be improved?

To enhance the detection of propaganda in low-resource languages, several strategies can be implemented. Firstly, creating more annotated datasets specific to these languages will provide a foundation for training models effectively. These datasets should include a diverse range of text samples that accurately represent the language's nuances and variations. Additionally, leveraging multilingual pre-trained models like mBERT or XLM-RoBERTa can help capture cross-lingual patterns and improve performance on code-switched text. Fine-tuning these models on code-switched data directly rather than relying on translations can also lead to better results. Moreover, incorporating domain experts fluent in both the low-resource language and English during annotation processes can ensure accurate labeling of propaganda techniques.

Q: What ethical considerations should be taken into account when analyzing social media content for propaganda?

When analyzing social media content for propaganda, it is crucial to prioritize user privacy by anonymizing all collected data and refraining from recording any personally identifiable information. An informed consent process should be followed with participants, allowing them to withdraw their consent at any time without penalty. To mitigate biases in annotations, rigorous training programs for annotators along with clear annotation methodologies are essential. Transparency about the purpose of data collection and intended use of annotated data is vital to maintain trust with participants and uphold ethical standards.

Q: How can the findings from this research be applied to combat misinformation on social media platforms effectively?

The findings from this research can play a significant role in combating misinformation on social media platforms by enabling more accurate detection of propagandistic content. By developing robust models trained specifically on code-switched text containing propaganda techniques, platforms can implement automated systems to flag potentially misleading content efficiently. This proactive approach allows for quicker identification and removal of harmful information before it spreads widely among users. Furthermore, integrating machine learning algorithms that continuously learn from new instances of misinformation helps platforms stay ahead in detecting evolving forms of propaganda tactics effectively.

Conceitos essenciais

Detecting propaganda techniques in code-switched social media text is crucial for maintaining a healthy online environment and promoting accurate information exchange.

Resumo

The rise of social media has facilitated the spread of propaganda, influencing public opinions. Detecting propaganda in code-switched text poses challenges due to multilinguality. A novel task of detecting propaganda techniques in code-switched text was proposed, focusing on English and Roman Urdu. A corpus of 1,030 texts annotated with 20 propaganda techniques was created. Experiments showed the importance of directly modeling multilinguality for better results.

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Estatísticas

The dataset consists of 1,030 examples with 2,577 labeled spans annotated with 20 propaganda techniques.
Average example length: 147.56 ± 53.79 words.
Vocabulary size: 7154 words.

Citações

"Social media platforms have made it easier for individuals and organizations to promote their agenda and narratives quickly across large audiences."
"Propaganda negatively affects many people and may cause harm by spreading misleading facts and opinions."
"We propose a novel task of detecting propaganda techniques in code-switched text to create a healthier online environment."

Principais Insights Extraídos De

Detecting Propaganda Techniques in Code-Switched Social Media Text

by Muhammad Uma... às arxiv.org 03-19-2024

https://arxiv.org/pdf/2305.14534.pdf

Detecting Propaganda Techniques in Code-Switched Social Media Text

Perguntas Mais Profundas

How can the detection of propaganda in low-resource languages be improved?

To enhance the detection of propaganda in low-resource languages, several strategies can be implemented. Firstly, creating more annotated datasets specific to these languages will provide a foundation for training models effectively. These datasets should include a diverse range of text samples that accurately represent the language's nuances and variations. Additionally, leveraging multilingual pre-trained models like mBERT or XLM-RoBERTa can help capture cross-lingual patterns and improve performance on code-switched text. Fine-tuning these models on code-switched data directly rather than relying on translations can also lead to better results. Moreover, incorporating domain experts fluent in both the low-resource language and English during annotation processes can ensure accurate labeling of propaganda techniques.

What ethical considerations should be taken into account when analyzing social media content for propaganda?

When analyzing social media content for propaganda, it is crucial to prioritize user privacy by anonymizing all collected data and refraining from recording any personally identifiable information. An informed consent process should be followed with participants, allowing them to withdraw their consent at any time without penalty. To mitigate biases in annotations, rigorous training programs for annotators along with clear annotation methodologies are essential. Transparency about the purpose of data collection and intended use of annotated data is vital to maintain trust with participants and uphold ethical standards.

How can the findings from this research be applied to combat misinformation on social media platforms effectively?

The findings from this research can play a significant role in combating misinformation on social media platforms by enabling more accurate detection of propagandistic content. By developing robust models trained specifically on code-switched text containing propaganda techniques, platforms can implement automated systems to flag potentially misleading content efficiently. This proactive approach allows for quicker identification and removal of harmful information before it spreads widely among users. Furthermore, integrating machine learning algorithms that continuously learn from new instances of misinformation helps platforms stay ahead in detecting evolving forms of propaganda tactics effectively.