There is a pressing need to exploit health-related content from social media, a global source of data where key health information is posted directly by citizens, patients and other healthcare stakeholders. Use cases of disease related social media mining include disease outbreak/surveillance, mental health and pharmacovigilance. Current efforts address the exploitation of social media beyond English. The SocialDisNER task, organized as part of the SMM4H 2022 initiative, has applied the LINKAGE methodology to select and annotate a Gold Standard corpus of 9,500 tweets in Spanish enriched with disease mentions generated by patients and medical professionals. As a complementary resource for teams participating in the SocialDisNER track, we have also created a large-scale corpus of 85,000 tweets, where in addition to disease mentions, other medical entities of relevance (e.g., medications, symptoms and procedures, among others) have been automatically labelled. Using these large-scale datasets, co-mention networks or knowledge graphs were released for each entity pair type. Out of the 47 teams registered for the task, 17 teams uploaded a total of 32 runs. The top-performing team achieved a very competitive 0.891 f-score, with a system trained following a continue pre-training strategy. We anticipate that the corpus and systems resulting from the SocialDisNER track might further foster health related text mining of social media content in Spanish and inspire disease detection strategies in other languages.

For the past seven years, the Social Media Mining for Health Applications (#SMM4H) shared tasks have promoted the community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in public, user-generated content. This seventh iteration consists of ten tasks that include English and Spanish posts on Twitter, Reddit, and WebMD. Interest in the #SMM4H shared tasks continues to grow, with 117 teams that registered and 54 teams that participated in at least one task—a 17.5% and 35% increase in registration and participation, respectively, over the last iteration. This paper provides an overview of the tasks and participants’ systems. The data sets remain available upon request, and new systems can be evaluated through the post-evaluation phase on CodaLab.