Abstract
Social media have a strong impact on the way users interact and share information. Several previous studies have highlighted how the structure of a social network can affect the dynamics of user interaction and information sharing. The majority of these studies have focused on the role of influencers, i.e. nodes with a central position in the network. Our claim is that while the information shared by influencers has a broader reach, the content of messages plays a critical role and can be a determinant of the social influence of the message irrespective of the centrality of the message’s author. In this paper, we put forward four hypotheses supporting this claim by focusing on the sentiment of posts to characterize content and test them on a data set of 500,000 messages from Twitter in the tourism domain. Overall, our hypotheses posit that negative posts are more influential than positive ones. Results show how negative tweets are retweeted more than positive tweets. However, the time dynamics of retweeting seem independent of the sentiment of tweets.
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References
Anholt, S. (2009). Competitive Identity: The New Brand Management for Nations, Cities and Regions. Palgrave Macmillan, Eds.
Aus, S., Galuba, W., Huberman, B.A. & Romero, D.M. (2010). Influence and passivity in social media. ACM.
Bakshy, E., Hofman, J. M., Mason, W. A. & Watts, D. J. (2011). Everyone’s an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on Web search and data mining (WSDM’ 11). New York, NY, USA: ACM, pp. 65–74.
Barbagallo, D., Cappiello, C., Francalanci, C. & Matera, M. (2011). Semantic sentiment analyses based on the reputation of Web information sources. In V. Sugumaran and J. A. Gulla (Eds), Applied Semantic Web Technologies. Taylor & Francis.
Barres, J. (1954). Class and committees in a Norwegian island parish. Human relations 7: 29–58.
Beck, T. & Davenport, J. (2001). The attention economy: Understanding the new currency of business. Cambridge: Harvard Business School press..
Benevenuto, F., Cha, M., Gummadi K.P. & Haddadi, H. (2010). Measuring user influence in Twitter: The million follower fallacy. In Proceedings of the 4 th International AAAI Conference on Weblogs and Social Media. Association for the advancement of artificial intelligence.
Berger, J. & Milkman, K. (2010) Social transmission, emotion, and the virality of online content. Wharton Research Paper.
Boyd, D., Golder, S. & Lotan, G. (2010). Tweet Tweet Retweet: Conversational Aspects of Retweeting on Twitter. In Proceedings of HICSS-42, Persistent Conversation Track. Kauai, HI: IEEE Computer Society. January 5-8, pp. 1–10.
Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. In Proceedings of the 7 th International World Wide Web Conference. 30 (1–7), pp. 107–117.
Brodka, P., Kazienko, P. & Musial, K. (2009). User position measure in social networks. In Proceedings of the 3 rd Workshop On Social Network Mining and Analysis. Paris, France: ACM.
Cartwright, D. (1959). Studies in social power. Vol. 6. University of Michigan, USA: Research Center for Group Dynamics.
Cartwright, D., Harary, F. & Norman, R. (1965). Structural models: An introduction to the theory of directed graphs. New York, NY, USA: John Wiley & Sons.
Chen, J., Nairn, R., Nelson, L., Bernstein, M. & Chi, E. (2010). Short and tweet: experiments on recommending content from information streams. In Proceedings of the 28th international conference on Human factors in computing systems (CHI’ 10). New York, NY, USA: ACM, pp. 1185–1194.
Chung, K.K., Davies, J. & Hossain, L. (2005). Exploring sociocentric and egocentric approaches for social network analysis. In Proceedings of International Conference on Knowledge Management. Pp. 1–8, Wellington, New Zealand, Asia Pacific.
Freeman, L.C. (1979). Centrality in social networks: Conceptual clarification. Social Networks 1(3): 215–239.
Galtung, J. & Ruge, M. (1965) The structure of foreign news: The presentation of the congo, cuba and cyprus crises in four norwegian newspapers. Journal of Peace Research 2(1): 64–90.
Granovetter, M.S. (1973). The strength of weak ties. The American Journal of Sociology 78(6): 1360–1380.
Katz E. & Lazarsfeld P. F. (1955). Personal influence; the part played by people in the flow of mass communications. Glencoe: Free Press.
Kwak, H., Lee, C., Park, H. & Moon, S. (2010). Finding influentials based on the temporal order of information adoption in Twitter. In Proceedings of the 19th international World Wide Web conference. Raleigh, North Carolina, USA: ACM, pp. 1137–1138.
Kwak, H., Lee, C., Park, H. & Moon, S. (2010). What is Twitter, a social network or a news media?. In Proceedings of the 19th international conference on World wide web (WWW’ 10). New York, NY, USA: ACM, pp. 591–600.
Leavitt, A. (2009). The influential: New approaches for analyzing influence on Twitter. Boston, USA: Web Ecology Project.
Mendoza, M., Poblete, B. & Castillo, C. (2010). Twitter under crisis: can we trust what we RT?. In Proceedings of the First Workshop on Social Media Analytics (SOMA’ 10). New York, NY, USA: ACM, pp. 71–79.
Milgram, S. (1967). The small world problem. Psychology Today 1(1): 60–67.
Qu, Y., Huang, C., Zhang, P. & Zhang., J. (2011). Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In Proceedings of the ACM 2011 conference on Computer supported cooperative work (CSCW’ 11). New York, NY, USA: ACM, pp. 25–34.
Rogers, E. M. (1995). Diffusion of innovations, 4th edition. New York: Free Press.
Scharl, A., Dickinger, A. & Weichselbraun, A. (2008). Analyzing News Media Coverage to Acquire and Structure Tourism Knowledge. Information Technology & Tourism 10(1): 3–17.
Sobel, K., Jansen, B. J., Zhang, M. & Chowdury, A. (2009) Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology 60(11): 2169–2188.
Starbird, K. (2010). Pass It On?: Retweeting in Mass Emergency. In Proceedings of the 7th International ISCRAM Conference — Seattle, USA, May 2010.
Zhou, Z., Bandari, R., Kong, J., Qian, H. & Roychowdhury, V. (2010). Information resonance on Twitter: watching Iran. In Proceedings of the First Workshop on Social Media Analytics (SOMA’ 10). New York, NY, USA: ACM, pp. 123–131.
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Barbagallo, D., Bruni, L., Francalanci, C., Giacomazzi, P. (2012). An Empirical Study on the Relationship between Twitter Sentiment and Influence in the Tourism Domain. In: Fuchs, M., Ricci, F., Cantoni, L. (eds) Information and Communication Technologies in Tourism 2012. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1142-0_44
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DOI: https://doi.org/10.1007/978-3-7091-1142-0_44
Publisher Name: Springer, Vienna
Print ISBN: 978-3-7091-1141-3
Online ISBN: 978-3-7091-1142-0