Challenges in Multilingual Adverse Drug Reaction Detection on Social Media: Insights from Case Studies

Adverse drug reactions are a significant concern in medical research and patient safety, and social media offers a valuable resource for broad and continuous monitoring of these reactions. However, extracting and analyzing relevant information about adverse drug reactions from social media poses several challenges for language models. Some challenges are linked to the characteristics of social media writing style, such as the use of hashtags, idiomatic expressions, and personal opinions. In this study, we identify these characteristics and investigate how language models perform in their presence in several languages. Our findings reveal that while current models can effectively classify some aspects of ADR-related content, significant challenges remain in accurately processing these social media-specific features. Further research is needed to enhance model performance and improve the reliability of ADR detection from social media data.