Early Risk Prediction of Depression Based on Social Media Posts in Arabic

Sabaneh, Kefaya; Salameh, Momen Abu; Khaleel, Fatima; Herzallah, Mohammad M.; Natsheh, Joman Y.; Maree, Mohammed

doi:10.1109/ictai59109.2023.00094

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2024

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Cited by 2 publications

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Sentiment Analysis Using Various Machine Learning Techniques on Depression Review Data

Alnaddaf,

Başarslan

2024

2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)

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Sentiment Analysis Using Various Machine Learning Techniques on Depression Review Data

Alnaddaf,

Başarslan

2024

2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)

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Ethics of Using LLMs in Content Moderation on Twitter

Ganiuly,

Smaiyl

2024

International Journal of Innovative Science and Research Techno

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As the number of users increases on social media each year, the number of posts that are made rises gradually. This is relevant for posts with negative characters including hate speech, misinformation, explicit material, or cyberbullying that influences terribly on users’ experience. This paper puts emphasis on content moderation with LLMs to avoid issues with bias, transparency, free speech, and accountability. Several experiments were conducted with pre-trained models to identify efficiency and arising ethical concerns while moderating posted data. Our findings reveal that LLMs demonstrate bias during the moderation of content from different demographics and minority communities. One of the most significant challenges found was the lack of transparency in the LLM's decision-making process. Despite the ethical concerns, the LLM demonstrated efficiency in processing large volumes of content, and this significantly reduced the time required to flag potentially harmful posts. This research highlights the need for a balanced approach to protecting freedom of speech while ensuring the ethical and responsible use of NLP on online platforms.

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Early Risk Prediction of Depression Based on Social Media Posts in Arabic

Cited by 2 publications

References 26 publications

Sentiment Analysis Using Various Machine Learning Techniques on Depression Review Data

Sentiment Analysis Using Various Machine Learning Techniques on Depression Review Data

Ethics of Using LLMs in Content Moderation on Twitter

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