Wen Shi
Wen Shi｜师文
PhD candidate in
Department of Earth System Science
Tsinghua University
Email: shi-w18@mails.tsinghua.edu.cn
Education Background
Sep 2011 – June 2015
Bachelor degree, Renmin University of China, School of Journalism and Communication
Sep 2015 – June 2018
Master degree, Tsinghua University, School of Journalism and Communication
Sep 2018 – Ongoing
Phd candidate, Tsinghua University, Department of Earth System Science
Research Experience
Climate change & Social media
1. What Framework Promotes Saliency of Climate Change Issues on Online Public Agenda: A Quantitative Study of Online Knowledge Community Quora
climate change; science communication; risk perception; agenda setting; social science
- Cognitive framework is proved to be least effective in raising public concern.
- Affective framework is relatively more influential in motivating people to participate in climate change discussion.
- Perceptual framework is most powerful in promoting public discussion and the only variable that can significantly motivate the public’s long-term desire to track issues.
Climate change & Global Warming
2. #Climatechange vs. #Globalwarming: Characterizing Two Competing Climate Discourses on Twitter with Semantic Network and Temporal Analyses
climate change; global warming; semantic network analysis; temporal analysis; public discourse; Twitter
- Climate change demonstrated a more scientific perspective and showed an attempt to condense climate discussions.
- Global warming triggered more political responses and showed a greater connection with phenomena.
- Temporal analysis suggests that traditional political discussions were gradually fading in both discourses but more recently started to revive in the form of discourse alliance in the climate change discourse.
Social media & Sentiment analysis
3. Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter
social bots; social media; sentiment analysis; COVID-19 pandemic; health emergency
- Social bots and humans shared a similar trend on sentiment polarity—positive or negative—for almost all topics.
- For the most negative topics, social bots were even more negative than humans.
- In most cases, social bots were more likely to actively amplify humans’ emotions, rather than to trigger humans’ amplification.