Skip to main content
Top
Published in: Information Systems Frontiers 5/2019

13-02-2018

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

Author: Wei-Lun Chang

Published in: Information Systems Frontiers | Issue 5/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
go back to reference 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. 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.
go back to reference 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 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
go back to reference Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205.CrossRef Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205.CrossRef
go back to reference Bollen, J., Mao, H., & Zeng, X. (2011a). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.CrossRef Bollen, J., Mao, H., & Zeng, X. (2011a). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.CrossRef
go back to reference 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. 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.
go back to reference Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM, 10(10–17), 30. Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM, 10(10–17), 30.
go back to reference 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. 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.
go back to reference 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 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
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.CrossRef Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.CrossRef
go back to reference 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. 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.
go back to reference Goh, K. I., Oh, E., Kahng, B., & Kim, D. (2003). Betweenness centrality correlation in social networks. Physical Review E, 67(1), 017101.CrossRef Goh, K. I., Oh, E., Kahng, B., & Kim, D. (2003). Betweenness centrality correlation in social networks. Physical Review E, 67(1), 017101.CrossRef
go back to reference 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. 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.
go back to reference 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 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
go back to reference 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. 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.
go back to reference Katz, E., & Lazarsfeld, P. (1955). Personal Influence: The Part Played by People in the Flow of Mass Communications. New York: The Free Press. Katz, E., & Lazarsfeld, P. (1955). Personal Influence: The Part Played by People in the Flow of Mass Communications. New York: The Free Press.
go back to reference 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. 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.
go back to reference 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 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
go back to reference Marsden, P. (2006). Consumer advisory panels: The next big thing in word-of- mouth marketing? Market Leader, 33, 45–47. Marsden, P. (2006). Consumer advisory panels: The next big thing in word-of- mouth marketing? Market Leader, 33, 45–47.
go back to reference Mei, Y., Zhong, Y., and Yang, J. (2015). Finding and Analyzing Principal Features for Measuring User Influence on Twitter (pp. 478–486). Redwood City. Mei, Y., Zhong, Y., and Yang, J. (2015). Finding and Analyzing Principal Features for Measuring User Influence on Twitter (pp. 478–486). Redwood City.
go back to reference 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. 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.
go back to reference Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.CrossRef Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.CrossRef
go back to reference 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 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
go back to reference Ramage, D., Dumais, S. T., & Liebling, D. J. (2010). Characterizing Microblogs with Topic Models. ICWSM, 10, 1–1. Ramage, D., Dumais, S. T., & Liebling, D. J. (2010). Characterizing Microblogs with Topic Models. ICWSM, 10, 1–1.
go back to reference Rogers, E. M. (1962). Diffusion of Innovations. New York: Free Press. Rogers, E. M. (1962). Diffusion of Innovations. New York: Free Press.
go back to reference 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. 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.
go back to reference 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 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
go back to reference Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307.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
go back to reference 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. 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.
go back to reference 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 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
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference 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. 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.
Metadata
Title
The Impact of Emotion: A Blended Model to Estimate Influence on Social Media
Author
Wei-Lun Chang
Publication date
13-02-2018
Publisher
Springer US
Published in
Information Systems Frontiers / Issue 5/2019
Print ISSN: 1387-3326
Electronic ISSN: 1572-9419
DOI
https://doi.org/10.1007/s10796-018-9824-0

Other articles of this Issue 5/2019

Information Systems Frontiers 5/2019 Go to the issue

Premium Partner