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2018 ACL ACL 2018

Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources

Abstract

AbstractIn the age of social news, it is important to understand the types of reactions that are evoked from news sources with various levels of credibility. In the present work we seek to better understand how users react to trusted and deceptive news sources across two popular, and very different, social media platforms. To that end, (1) we develop a model to classify user reactions into one of nine types, such as answer, elaboration, and question, etc, and (2) we measure the speed and the type of reaction for trusted and deceptive news sources for 10.8M Twitter posts and 6.2M Reddit comments. We show that there are significant differences in the speed and the type of reactions between trusted and deceptive news sources on Twitter, but far smaller differences on Reddit.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Data Science & Analytics and Deep Learning and Machine Learning
🧭 Keyword Pioneer — user reaction classification
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Maria Glenski , Tim Weninger , Svitlana Volkova

Topics

Machine Learning > Core Methods > Classification Machine Learning > Application Areas > Fairness Deep Learning > Learning Types > Classification Artificial Intelligence > Core AI > Natural Language Processing Data Science & Analytics > Applications > Social Media Analysis

Keywords

text classification social media analysis deep learning social media deception detection user reaction classification credibility analysis
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