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Information Systems Frontiers

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13.02.2018

The Impact of Emotion: A Blended Model to Estimate Influence on Social Media

verfasst von: Wei-Lun Chang

Erschienen in: Information Systems Frontiers | Ausgabe 5/2019

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Abstract

The goal of this research is to devise a model of social influence with sentiment analysis and help organization discover real influential people on social media. This study takes into account the quality of post and sentiment ratio simultaneously. We discovered the meaning of sentiment behind post, retweet, and reply is more important than numbers. This research selected four targets (two politicians and two celebrities) on Twitter to examine the proposed model. The results revealed the sentiment ratio of celebrities is higher than politicians. The reason may be the celebrities posted random issues in daily life and followers all supported them. However, the politicians’ tweets are easy to provoke a conflict which may cause emotional expressions from fans or followers. Sentiment analysis can adjust numbers based on the insights of content. We also provided the h-index to identify high impact of posted topics. The results showed various topics have different impact according to h-index. In summary, the proposed model can appropriately estimate the influence of a person in social media and assist firms allocate marketing resources efficiently.

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Asur, S., & Huberman, B. (2010). Predicting the future with social media. Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference, 1, 492–499.

Bae, Y., & Lee, H. (2012). Sentiment analysis of Twitter audiences: Measuring the positive and negative influence of popular Twitters. Journal of the American Society for Information Science and Technology, 63(12), 2521–2535.CrossRef

Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205.CrossRef

Bollen, J., Mao, H., & Zeng, X. (2011a). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.CrossRef

Bollen, J., Pepe, A., & Mao, H. (2011b) Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM 2011). Barcelona.

Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM, 10(10–17), 30.

Chen, W., Cheng, S., He, X., & Jiang, F. (2012). Influencerank: An efficient social influence measurement for millions of users in microblog. In Proceedings of 2012 the Second International Conference the on Cloud and Green Computing (pp. 563–570). Xiangtan: IEEE.

Corey, L. G. (1971). People who claim to be opinion leaders: identifying their characteristics by self-report. The Journal of Marketing, 35(4), 48–53.CrossRef

Dave, K., Lawrence, S., & Pennock, D. M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web (pp. 519–528). Budapest: ACM.

Diakopoulos, N. A., & Shamma, D. A. (2010). Characterizing debate performance via aggregated twitter sentiment. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1195–1198.

Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.CrossRef

Gilbert, E., & Karahalios, K. (2010). Widespread Worry and the Stock Market. In Proceedings of 4th Int’l AAAI Conference on Weblogs and Social Media (pp. 59–65). Washington.

Goh, K. I., Oh, E., Kahng, B., & Kim, D. (2003). Betweenness centrality correlation in social networks. Physical Review E, 67(1), 017101.CrossRef

He, X., Cheng, S., Chen, W., & Jiang, F. (2013). A novel measurement of the activation probabilities in information diffusion model. In Proceeding of 2013 International Conference on Information Society (i-Society) (pp. 130–135). Toronto.

Hirsh, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572.CrossRef

Java, A., Song, X., Finin, T., & Tseng, B. (2007). Why we twitter: understanding microblogging usage and communities. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (pp. 56–55). San Jose.

Katz, E., & Lazarsfeld, P. (1955). Personal Influence: The Part Played by People in the Flow of Mass Communications. New York: The Free Press.

Kim, E., Gilbert, S., Edwards, M. J., & Graeff, E. (2009). Detecting sadness in 140 characters: Sentiment analysis and mourning Michael Jackson on Twitter. Web Ecology, 3, 1–15.

Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a Social Network or a News Media? North Carolina: Proceeding of WWW 2010 Conference.CrossRef

Marsden, P. (2006). Consumer advisory panels: The next big thing in word-of- mouth marketing? Market Leader, 33, 45–47.

Mei, Y., Zhong, Y., and Yang, J. (2015). Finding and Analyzing Principal Features for Measuring User Influence on Twitter (pp. 478–486). Redwood City.

O'Connor, B., Balasubramanyan, R., Routledge, B. R., & Smith, N. A. (2010). From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. ICWSM, 11(122–129), 1–2.

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.CrossRef

Peng, S., Yang, A., Cao, L., Yu, S., & Xie, D. (2017). Social influence modeling using information theory in mobile social networks. Information Sciences, 379, 146–159.CrossRef

Ramage, D., Dumais, S. T., & Liebling, D. J. (2010). Characterizing Microblogs with Topic Models. ICWSM, 10, 1–1.

Rogers, E. M. (1962). Diffusion of Innovations. New York: Free Press.

Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A. (2011). Influence and passivity in social media. In Proceedings of the Machine learning and knowledge discovery in databases, 18-33.

Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media-Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217–248.CrossRef

Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307.CrossRef

Talbot, R., Acheampong, C., & Wicentowski, R. (2015), SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). Denver.

Tausczik, R. L., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29, 24–54.CrossRef

Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. ICWSM, 10, 178–185.

Turney, P. D. (2002). Thumbs up or thumbs down?: semantic orientation applied to unsupervisedclassification of reviews. In Proceedings of the 40th annual meeting of association for computational linguistics (pp. 417–424). Philadelphia.

Weng, J., Lim, E. P., Jiang, J., & He, Q. (2010). Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the third ACM International conference on Web search and data mining (pp. 261–270). New York City.

Ye, S., & Wu, S. F. (2010). Measuring message propagation and social influence on Twitter. Lecture Notes in Computer Science book series (LNCS), 6430, 216–231.

Titel: The Impact of Emotion: A Blended Model to Estimate Influence on Social Media
verfasst von: Wei-Lun Chang
Publikationsdatum: 13.02.2018
Verlag: Springer US
Erschienen in: Information Systems Frontiers / Ausgabe 5/2019
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-018-9824-0

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