Social Medium
Social media analysis focuses on understanding and leveraging the vast amount of textual and multimedia data generated on online platforms to address societal challenges and scientific questions. Current research heavily utilizes large language models (LLMs) and transformer-based architectures, coupled with graph neural networks and other machine learning techniques, to detect harmful content (e.g., hate speech, suicide ideation, misinformation), analyze user behavior and sentiment, and predict societal trends. This field is significant for its potential to improve mental health interventions, mitigate the spread of harmful information, and enhance our understanding of social dynamics, impacting both the social sciences and the development of more responsible and ethical online platforms.
Papers
A Framework for Identifying Depression on Social Media: MentalRiskES@IberLEF 2023
Simon Sanchez Viloria, Daniel Peix del Río, Rubén Bermúdez Cabo, Guillermo Arturo Arrojo Fuentes, Isabel Segura-Bedmar
Exploring Spatial-Temporal Variations of Public Discourse on Social Media: A Case Study on the First Wave of the Coronavirus Pandemic in Italy
Anslow Michael, Galletti Martina
Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
Yining Hua, Jiageng Wu, Shixu Lin, Minghui Li, Yujie Zhang, Dinah Foer, Siwen Wang, Peilin Zhou, Jie Yang, Li Zhou
Multitask learning for recognizing stress and depression in social media
Loukas Ilias, Dimitris Askounis
LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit Posts
Muskan Garg, Chandni Saxena, Debabrata Samanta, Bonnie J. Dorr
An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts
Muskan Garg, Amirmohammad Shahbandegan, Amrit Chadha, Vijay Mago