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Exploring agent interaction patterns in the comment sections of fake and real news
Kailun Zhu1,2, Songtao Peng1,2, Jiaqi Nie1,2
1Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China.
Journal of the Royal Society, Interface
|November 26, 2024
Summary
Real news comment sections show more positive sentiment and stable interactions than fake news. This analysis of agent interactions on Reddit aids in early fake news detection.
Area of Science:
- Computational Social Science
- Network Analysis
- Natural Language Processing
Background:
- User comments are vital for discerning fake from real news.
- Previous research primarily focused on textual content, neglecting agent interactions.
- The dynamics of user interactions in fake vs. real news comment sections remain underexplored.
Purpose of the Study:
- To investigate agent interaction patterns in fake and real news comment sections on Reddit.
- To analyze network structure and node sentiment in these interactions.
- To identify potential indicators for early detection of fake news.
Main Methods:
- Analysis of a dataset containing both fake and real news from Reddit.
- Examination of network structures within comment sections.
- Sentiment analysis of user nodes and their clustering behavior.
Main Results:
- Real news comment sections exhibit more neutral and positive sentiments compared to fake news, which generates more negative sentiment.
- Nodes with similar sentiments demonstrate tighter-than-expected clustering.
- Sentiment distribution among nodes stabilizes early and remains consistent over time.
Conclusions:
- Agent interaction patterns and sentiment dynamics differ significantly between fake and real news.
- The observed stability in sentiment distribution offers a potential marker for news veracity.
- Findings provide theoretical insights and practical tools for real-time fake news detection in social networks.
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