Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
Source abstract
Abstract
With the increasing reliance on social media as a primary source of news, the proliferation of rumors has become a pressing global concern that negatively impacts various domains, including politics, economics, and societal well-being. While significant efforts have been made to identify and debunk rumors in social media, progress in detecting and addressing such issues in the Arabic language has been limited compared to other languages, particularly English. This study introduces a context-aware approach to rumor detection in Arabic social media, leveraging recent advancements in Natural Language Processing (NLP). Our proposed method evaluates Arabic news posts by analyzing the emotions evoked by news content and recipients towards the news. Moreover, this research explores the impact of incorporating user and content features into emotion-based rumor detection models. To facilitate this investigation, we present a novel Arabic rumor dataset, comprising both news posts and associated comments, which represents a first-of-its-kind resource in the Arabic language. The findings from this study offer promising insights into the role of emotions in rumor detection and may serve as a catalyst for further research in this area, ultimately contributing to improved detection and the mitigation of misinformation in the digital landscape.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining · Spam and Phishing Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer Science and Information Systems, University of King Saud, Riyadh 11421, Saudi Arabia
Funding
Deanship of Scientific Research, King Saud University
References (87)
Show source-provided references
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