The field of natural language processing (NLP) is rapidly advancing, with a growing focus on social media analysis. Recent studies have explored the use of NLP techniques to detect hate speech, predict suicide risk, and analyze climate change communication. A key trend in this area is the development of novel datasets and frameworks for analyzing social media text, such as the MetaClimage database and the Holistix dataset. These resources are enabling researchers to better understand the complexities of online language and develop more effective tools for mitigating harmful content. Notable papers in this area include the introduction of the Protective Factor-Aware Dynamic Influence Learning framework for predicting subsequent suicide risk and the development of the CLAImate prototype for personalized and localized climate change communication. Overall, the field is moving towards a more nuanced understanding of online language and its implications for social media users.
Advances in Natural Language Processing for Social Media Analysis
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Exploring Gender Differences in Chronic Pain Discussions on Reddit
Application of CARE-SD text classifier tools to assess distribution of stigmatizing and doubt-marking language features in EHR
MetaClimage: A novel database of visual metaphors related to Climate Change, with costs and benefits analysis
Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives
Protective Factor-Aware Dynamic Influence Learning for Suicide Risk Prediction on Social Media
Abusive text transformation using LLMs
Meanings are like Onions: a Layered Approach to Metaphor Processing
From BERT to Qwen: Hate Detection across architectures
Testing Hypotheses from the Social Approval Theory of Online Hate: An Analysis of 110 Million Posts from Parler
Queueing for Civility: User Perspectives on Regulating Emotions in Online Conversations
RSD-15K: A Large-Scale User-Level Annotated Dataset for Suicide Risk Detection on Social Media
CLAImate: AI-Enabled Climate Change Communication through Personalized and Localized Narrative Visualizations
Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers
A Computational Framework to Identify Self-Aspects in Text