Negative emotions shape the diffusion of cancer tweets: toward an integrated social network–text analytics approach

Xiaohui WANG*, Edmund W.J. Lee

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

14 Citations (Scopus)


Purpose: Drawing on the cognitive-functional model of emotions and emotional contagion, the authors aim to examine the role of negative emotions in the diffusion of cancer tweets. Design/methodology/approach: Using an integrated approach of social network and text analytics, the authors analyzed 142,883 cancer tweets from February to March 2018. The roles of negative emotions, emotional contagion, cancer themes and user influence on the diffusion of cancer tweets were examined. Findings: Results indicated that cancer tweets expressing negativity and anger diffused more widely, while those expressing sadness or fear were less likely to diffuse. However, contrary to the authors’ expectation, cancer tweets expressing negative emotions (i.e. negativity, anger and fear) were less likely to arouse similar emotions among retweets, thus suggesting that emotions in cancer tweets were not as contagious as they seemed. Finally, user influence was the most important factor explaining the diffusion of cancer tweets, although cancer-related themes (i.e. affective, informative and social) had marginal effects on likelihood of diffusion. Originality/value: Using a novel integrated social network–text analytics approach, the authors found that to understand cancer tweets' diffusion, it is critical to go beyond examining the content of tweets about cancer and the influence of messengers – the virality of cancer tweets is inextricable from the negative emotions.

Original languageEnglish
Pages (from-to)401-418
Number of pages18
JournalInternet Research
Issue number2
Publication statusPublished - 10 Nov 2020

Scopus Subject Areas

  • Communication
  • Sociology and Political Science
  • Economics and Econometrics

User-Defined Keywords

  • Big data
  • Cancer
  • Information diffusion
  • Negative emotions
  • Social network analysis
  • Text analytics


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