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
DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods
Lorenzo Lupo, Paul Bose, Mahyar Habibi, Dirk Hovy, Carlo Schwarz
Generator-Guided Crowd Reaction Assessment
Sohom Ghosh, Chung-Chi Chen, Sudip Kumar Naskar
SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media
Parisa Jamadi Khiabani, Arkaitz Zubiaga
Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language
Xiaohan Ding, Buse Carik, Uma Sushmitha Gunturi, Valerie Reyna, Eugenia H. Rho
Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset
Kirill Milintsevich, Kairit Sirts, Gaël Dias
Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media
Nikhil Narayan, Mrutyunjay Biswal
Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing
Seibi Kobara, Alireza Rafiei, Masoud Nateghi, Selen Bozkurt, Rishikesan Kamaleswaran, Abeed Sarker
ESG Sentiment Analysis: comparing human and language model performance including GPT
Karim Derrick