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Sentiment-based influence detection on Twitter


The user generated content available in online communities is easy to create and consume. Lately, it also became strategically important to companies interested in obtaining population feedback on products, merchandising, etc. One of the most important online communities is Twitter: recent statistics report 65 million new tweets each day. However, processing this amount of data is very costly and a big portion of the content is simply not useful for strategic analysis. Thus, in order to filter the data to be analyzed, we propose a new method for ranking the most influential users in Twitter. Our approach is based on a combination of the user position in networks that emerge from Twitter relations, the polarity of her opinions and the textual quality of her tweets. Our experimental evaluation shows that our approach can successfully identify some of the most influential users and that interactions between users provide the best evidence to determine user influence.


  1. 1.

    Alpaydin E (2004) Introduction to machine learning (adaptive computation and machine learning). MIT Press, Cambridge

    Google Scholar 

  2. 2.

    Baeza-Yates RA, Ribeiro-Neto B (1999) Modern information retrieval. Addison-Wesley, Reading

    Google Scholar 

  3. 3.

    Bakshy E, Hofman JM, Mason W, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: International conference on web search and data mining (WSDM), Hong Kong, China

    Google Scholar 

  4. 4.

    Barabási A-L (2002) Linked: the new science of networks, 1st edn. Basic Books, New York

    Google Scholar 

  5. 5.

    Berry J, Keller E (2003) The influentials: one American in ten tells the other nine how to vote, where to eat, and what to buy. Free Press, New York

    Google Scholar 

  6. 6.

    Bonacich P (2007) Some unique properties of eigenvector centrality. Soc Netw 29(4):555–564

    Article  Google Scholar 

  7. 7.

    Brandes U (2008) On variants of shortest-path betweenness centrality and their generic computation. Soc Netw 30(2):136–145

    MathSciNet  Article  Google Scholar 

  8. 8.

    Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30:107–117

    Article  Google Scholar 

  9. 9.

    Cha M, Haddadi H, Benevenuto F, Gummadi KP (2010) Measuring user influence in Twitter: the million follower fallacy. In: Conference on weblogs and social media, Washington, District of Columbia, USA

    Google Scholar 

  10. 10.

    Chan KK, Misra S (1990) Characteristics of the opinion leader: a new dimension. J Advert 19:53–60

    Article  Google Scholar 

  11. 11.

    Chen P, Xie H, Maslov S, Redner S (2007) Finding scientific gems with Google’s PageRank algorithm. J Informetr 1(1):8–15

    Article  Google Scholar 

  12. 12.

    Costa LF et al. (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56:167

    Article  Google Scholar 

  13. 13.

    Dalip DH et al. (2009) Automatic quality assessment of content created collaboratively by web communities: a case study of Wikipedia. In: Joint conference on digital libraries (JCDL), Austin, Texas, USA, pp 295–304

    Google Scholar 

  14. 14.

    Golbeck J, Hansen D (2011) Computing political preference among Twitter followers. In: Proceedings of the 2011 annual conference on human factors in computing systems, CHI ’11, Vancouver, British Columbia, Canada. ACM, New York, pp 1105–1108

    Chapter  Google Scholar 

  15. 15.

    Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: International conference on web search and data mining (WSDM), New York, New York, USA, pp 241–250

    Google Scholar 

  16. 16.

    Hagberg A, Schult D, Swart P Networkx. High productivity software for complex networks.

  17. 17.

    Huang J, Thornton KM, Efthimiadis EN (2010) Conversational tagging in Twitter. In: Conference on hypertext and hypermedia, Toronto, Ontario, Canada, pp 173–178

    Google Scholar 

  18. 18.

    Huberman BA, Romero DM, Wu F (2008) Social networks that matter: Twitter under the microscope. Social science research network working paper series

  19. 19.

    Jain RK (1991) The art of computer systems performance analysis: techniques for experimental design, measurement, simulation, and modeling. Wiley/Interscience, New York

    MATH  Google Scholar 

  20. 20.

    Jansen BJ et al. (2009) Twitter power: tweets as electronic word of mouth. J Am Soc Inf Sci Technol 60(11):2169–2188

    Article  Google Scholar 

  21. 21.

    Katz E, Lazarsfeld P, CUB of Applied Social Research (1955) Personal influence: the part played by people in the flow of mass communications. Foundations of communications research. Free Press, New York

    Google Scholar 

  22. 22.

    Krishnamurthy B, Gill P, Arlitt M (2008) A few chirps about Twitter. In: Workshop on online social networks (WOSP), Seattle, Washington, USA, pp 19–24

    Chapter  Google Scholar 

  23. 23.

    Kwak H, Lee C, Park, H, and Moon S (2010) What is Twitter, a social network or a news media. In: International conference on World Wide Web (WWW), Raleigh, North Carolina, USA.

    Google Scholar 

  24. 24.

    Leavitt A, Burchard E, Fisher D, Gilbert S (2009) The influentials: new approaches for analyzing influence on Twitter

    Google Scholar 

  25. 25.

    Lee C, Kwak H, Park H, Moon S (2010) Finding influentials based on the temporal order of information adoption in Twitter. In: International conference on World Wide Web (WWW), Raleigh, North Carolina, USA, pp 1137–1138

    Google Scholar 

  26. 26.

    O’Connor B, Balasubramanyan R, Routledge BR, Smith NA (2010) From tweets to polls: linking text sentiment to public opinion time series. In: International AAAI conference on weblogs and social media (ICWSM), Washington, District of Columbia, USA

    Google Scholar 

  27. 27.

    Ressler S (1993) Perspectives on electronic publishing: standards, solutions, and more

    Google Scholar 

  28. 28.

    Ruhnau B (2000) Eigenvector-centrality—a node-centrality? Soc Netw 22(4):357–365

    Article  Google Scholar 

  29. 29.

    Savage N (2011) Twitter as medium and message. Commun ACM 54:18–20

    Article  Google Scholar 

  30. 30.

    Van den Bulte C, Joshi YV (2007) New product diffusion with influentials and imitators. Mark Sci 26(3):400–421

    Article  Google Scholar 

  31. 31.

    Watts DJ, Dodds PS (2007) Influentials, networks, and public opinion formation. J Consum Res 34(4):441–458

    Article  Google Scholar 

  32. 32.

    Weng J, Lim EP, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential twitterers. In: International conference on web search and data mining (WSDM), New York, New York, USA, pp 261–270

    Google Scholar 

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Correspondence to Carolina Bigonha.

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Bigonha, C., Cardoso, T.N.C., Moro, M.M. et al. Sentiment-based influence detection on Twitter. J Braz Comput Soc 18, 169–183 (2012).

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  • Twitter
  • User influence