CrisisNews: A Dataset Mapping Two Decades of News Articles on Online Problematic Behavior at Scale

2025-10-14

AI Summary

Social media crises—discrete, high-impact events originating and propagating on social platforms, causing widespread societal harm—demand systematic investigation beyond fragmented content analysis. This study introduces CrisisNews, the first large-scale, longitudinal, event-centric dataset comprising 93,250 global news articles spanning two decades. We propose a multidimensional classification framework—grounded in manual coding and automated annotation—that captures stakeholder roles, behavioral types, and consequential impacts. By tracing crisis evolution over time, we identify recurrent diffusion patterns. Our work shifts the HCI research paradigm from user-generated content (UGC) fragments to structured, event-driven analysis, thereby establishing the first empirically grounded, open-source resource for designing proactive platform-level risk interventions and fostering trustworthy online ecosystems.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Humans and AI: Crowd Sourcing and Human Computation
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Analysis of web-mediated communications, communities, and crowds
• Web Mining and Content Analysis: Content-based information diffusion
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

As social media adoption grows globally, online problematic behaviors increasingly escalate into large-scale crises, requiring an evolving set of mitigation strategies. While HCI research often analyzes problematic behaviors with pieces of user-generated content as the unit of analysis, less attention has been given to event-focused perspectives that track how discrete events evolve. In this paper, we examine 'social media crises': discrete patterns of problematic behaviors originating and evolving within social media that cause larger-scale harms. Using global news coverage, we present a dataset of 93,250 news articles covering social media-endemic crises from the past 20 years. We analyze a representative subset to classify stakeholder roles, behavior types, and outcomes, uncovering patterns that inform more nuanced classification of social media crises beyond content-based descriptions. By adopting a wider perspective, this research seeks to inform the design of safer platforms, enabling proactive measures to mitigate crises and foster more trustworthy online environments.

Problem

• Mapping news coverage of social media crises over 20 years
• Analyzing stakeholder roles and behavior types in online crises
• Developing classification systems beyond content-based crisis descriptions

Innovation

• Dataset of news articles tracking social media crises
• Classifies stakeholder roles and behavior types
• Enables proactive crisis mitigation for safer platforms