The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior
Abstract
Microblogging services such as Twitter are said to have the potential for increasing political participation. Given the feature of 'retweeting' as a simple yet powerful mechanism for information diffusion, Twitter is an ideal platform for users to spread not only information in general but also political opinions through their networks as Twitter may also be used to publicly agree with, as well as to reinforce, someone's political opinions or thoughts. Besides their content and intended use, Twitter messages ('tweets') also often convey pertinent information about their author's sentiment. In this paper, we seek to examine whether sentiment occurring in politically relevant tweets has an effect on their retweetability (i.e., how often these tweets will be retweeted). Based on a data set of 64,431 political tweets, we find a positive relationship between the quantity of words indicating affective dimensions, including positive and negative emotions associated with certain political parties or politicians, in a tweet and its retweet rate. Furthermore, we investigate how political discussions take place in the Twitter network during periods of political elections with a focus on the most active and most influential users. Finally, we conclude by discussing the implications of our results.
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What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
Regression Analysis of Count Data
Cameron, A. C., & Trivedi, P. K. (2013) · doi:10.1017/cbo9781139013567 · Cited by 6,640
The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
Tausczik, Y. R., & Pennebaker, J. W. (2010) · Journal of Language and Social Psychology · doi:10.1177/0261927x09351676 · Cited by 5,774
The Wealth of Networks: How Social Production Transforms Markets and Freedom
Benkler, Y. (2017) · Yale University Press eBooks · doi:10.12987/9780300127232 · Cited by 3,680
The ripple effect: emotional contagion and its influence on group behavior
Barsade, S. G. (2002) · Administrative Science Quarterly · doi:10.2307/3094912 · Cited by 3,361
Regression Analysis of Count Data
Cameron, A. C., & Trivedi, P. K. (1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511814365 · Cited by 3,147
Why we twitter: understanding microblogging usage and communities
Java, A., Song, X., Finin, T., & Tseng, B. (2007) · Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis · doi:10.1145/1348549.1348556 · Cited by 2,645
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
Boyd, D., Golder, S., & Lotan, G. (2010) · 2010 43rd Hawaii International Conference on System Sciences · doi:10.1109/hicss.2010.412 · Cited by 2,051
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
O'Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14031 · Cited by 1,923
The Wealth of Networks: How Social Production Transforms Markets and Freedom
(2006) · Choice Reviews Online · doi:10.5860/choice.44-0999 · Cited by 1,903
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Emotion shapes the diffusion of moralized content in social networks
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A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
Zhou, F., Xu, X., Trajcevski, G., & Zhang, K. (2022) · ACM Computing Surveys · doi:10.1145/3433000 · Cited by 259
Brady, W. J., Wills, J. A., Burkart, D., Jost, J. T., & Van Bavel, J. J. (2019) · Journal of Experimental Psychology: General · doi:10.1037/xge0000532 · Cited by 188
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings
Demszky, D., Garg, N., Voigt, R., Zou, J., Shapiro, J., Gentzkow, M., & Jurafsky, D. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1304 · Cited by 164
“, 2017) and that emotive political tweets are more likely to be shared (Stieglitz and Dang-Xuan, 2012).”
Background
Metadata
- Authors
- Stieglitz, S., & Dang-Xuan, L.
- Venue
- 2012 45th Hawaii International Conference on System Sciences
- Publisher
- IEEE
- Published
- 2012-01
- Pages
- 3500-3509
- ISSN
- 1530-1605 (ISSN-L)
- OpenAlex
- W2149731496
- Cited by
- 288
- References
- 41
- FWCI
- 36.11
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Stieglitz, S., & Dang-Xuan, L. (2012). Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior. 2012 45th Hawaii International Conference on System Sciences. https://doi.org/10.1109/hicss.2012.476
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