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Anger is more influential than joy: sentiment correlation in weibo
Rui Fan1, Jichang Zhao2, Yan Chen1
1State Key Laboratory of Software Development Environment, Beihang University, Beijing, P. R. China.
Plos One
|October 22, 2014
Summary
Online social media users exhibit varying emotional connections, with anger correlating more strongly than joy or sadness. User interactions and network size significantly influence sentiment correlation in online social networks.
Area of Science:
- Social Media Analysis
- Computational Social Science
- Affective Computing
Background:
- The rapid expansion of online social media platforms like Weibo in China has created vast networks of interconnected users.
- Understanding how emotions spread and correlate among users is crucial for analyzing online social dynamics.
- Previous research has explored sentiment analysis but often overlooks the nuanced correlations of specific emotions within large social networks.
Purpose of the Study:
- To investigate the correlation patterns of different affective states (anger, joy, sadness) among users on a large-scale social media platform.
- To examine the impact of user interaction frequency and network size (number of friends) on sentiment correlation.
- To provide empirical insights for developing models of sentiment influence and propagation in online social networks.
Main Methods:
- Analysis of user-generated content and social connection data from Weibo, a major Chinese microblogging platform.
- Quantitative assessment of sentiment correlation between pairs of users based on shared affective states.
- Statistical modeling to evaluate the influence of interaction frequency and network size on observed sentiment correlations.
Main Results:
- A significantly higher correlation was observed for anger compared to joy among connected users.
- The correlation of sadness between users was found to be surprisingly low.
- Increased interaction frequency and a larger number of friends were associated with stronger sentiment correlation between users.
Conclusions:
- User affective states are not uniformly correlated; specific emotions like anger show stronger network effects.
- Social network structure, particularly interaction levels and user connectivity, plays a significant role in shaping sentiment correlation.
- Findings contribute to a deeper understanding of emotional dynamics and propagation mechanisms within online social environments.
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