Good friends, bad news - affect and virality in Twitter

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Good friends, bad news - affect and virality in Twitter
Authors: Lars Kai Hansen, Adam Arvidsson, Finn Årup Nielsen, Elanor Colleoni, Michael Etter
Citation: Future Information Technology 185, Part 1 in Communications in Computer and Information Science : 34-43. 2011 December
Editors: J. J. Park, L. T. Yang, C. Lee
Publisher: Springer
Meeting: 2011 International Workshop on Social Computing, Network, and Services
Database(s): arXiv (arxiv/1101.0510) Citeulike Google Scholar cites Microsoft Academic Search
DOI: 10.1007/978-3-642-22309-9_5.
Link(s): http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/5982/pdf/imm5982.pdf
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Good friends, bad news - affect and virality in Twitter is a study on virality (information diffusion) with retweeting in Twitter and the effect of sentiment analyzed with sentiment analysis.

The word list used for the sentiment analysis is available on the Internet as AFINN: [1] and is further described in A new ANEW: evaluation of a word list for sentiment analysis in microblogs.

[edit] Data

Posterings from Twitter were download by two means:

  1. Querying on "COP15" via the search interface
  2. Collection of random tweets via the Twitter stream.

[edit] Method

  • English tweets was distinguish with a word list
  • A news classifier based on NLTK naïve Bayes classifier was trained on the Brown corpus
  • A generalized linear model analyzed was Twitter posting features where correlated with retweeting.
  • Text sentiment analysis with the AFINN word list

[edit] Related studies

  1. Analyzing and predicting news popularity on Twitter
  2. Analyzing and predicting viral tweets
  3. Bad news travel fast: a content-based analysis of interestingness on Twitter
  4. Determinants of information retweeting in microblogging
  5. Do people prefer to pass along good or bad news? valence and relevance of news as predictors of transmission propensity
  6. Does bad news go away faster?
  7. Easier contagion and weaker ties make anger spread faster than joy in social media
  8. Emotional divergence influences information spreading in Twitter
  9. Emotions and information diffusion in social media--sentiment of microblogs and sharing behavior
  10. Emotional divergence influences information spreading in Twitter
  11. Experimental evidence of massive-scale emotional contagion through social networks
  12. Finding influential neighbors to maximize information diffusion in Twitter
  13. In the mood for being influential on Twitter
  14. Lifespan and propagation of information in on-line social networks: a case study
  15. Predicting discussions on the social semantic web
  16. Politics, sharing and emotion in microblogs
  17. Positive news makes readers feel good: a "silver-lining" approach to negative news can attract audiences
  18. On the role of conductance, geography and topology in predicting hashtag virality
  19. Retweets--but not just retweets: quantifying and predicting influence on Twitter
  20. Retweeting analysis and prediction in microblogs: an epidemic inspired approach
  21. Sentiment flow through hyperlink networks
  22. Sentiment in Twitter events
  23. Sentiment propagation in social networks: a case study in LiveJournal
  24. Social transmission, emotion, and the virality of online content
  25. The dynamics of health behavior sentiments on a large online social networks
  26. The dynamics of information diffusion on on-line social networks
  27. The impact of content sentiment and emotionality on content virality
  28. The role of multimedia content in determining the virality of social media information
  29. The structure of foreign news: the presentation of the Congo, Cuba and Cyprus crises in four Norwegian newspapers
  30. Trade-off between virality and mass media in influence in the topological evolution of online social networks
  31. Want to be retweeted? large scale analytics on factors impacting retweet in Twitter network
  32. What is Twitter, a social network or a news media?
  33. What makes online content viral? (the study with New York Times information diffusion)
  34. Whisper: tracing the spatiotemporal process of information diffusion in real time (information visualization of information diffusion)
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