The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape
Abstract
This study investigates whether microblogging messages on Twitter validly mirror the political landscape off-line and can be used to predict election results. In the context of the 2009 German federal election, we conducted a sentiment analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is used extensively for political deliberation and that the mere number of party mentions accurately reflects the election result. The tweets' sentiment (e.g., positive and negative emotions associated with a politician) corresponds closely to voters' political preferences. In addition, party sentiment profiles reflect the similarity of political positions between parties. We derive suggestions for further research and discuss the use of microblogging services to aggregate dispersed information.
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Opinion Mining and Sentiment Analysis
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Opinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372
The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
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The political blogosphere and the 2004 U.S. election: divided they blog
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Predicting the future with social media
Asur, S., & Huberman, B. A. (2010) · 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology · doi:10.1109/wi-iat.2010.63 · Cited by 2,081
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
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Cited by
Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior
Stieglitz, S., & Dang-Xuan, L. (2013) · Journal of Management Information Systems · doi:10.2753/mis0742-1222290408 · Cited by 1,604
Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”
Zhang, X., Fuehres, H., & Gloor, P. A. (2011) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2011.10.562 · Cited by 640
More than words: Social networks’ text mining for consumer brand sentiments
Mostafa, M. M. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.01.019 · Cited by 623
Tweets and Trades: the Information Content of Stock Microblogs
Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014) · European Financial Management · doi:10.1111/j.1468-036x.2013.12007.x · Cited by 561
Self-citationStance and Sentiment in Tweets
Mohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017) · ACM Transactions on Internet Technology · doi:10.1145/3003433 · Cited by 449
Metadata
- Authors
- Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M.
- Venue
- Social Science Computer Review
- Publisher
- SAGE Publications
- Published
- 2010-12-12
- Volume
- 29
- Issue
- 4
- Pages
- 402-418
- ISSN
- 0894-4393 (ISSN-L)
- 1552-8286
- OpenAlex
- W2037481123
- Cited by
- 447
- References
- 28
- FWCI
- 81.09
- Type
- journal article
- Language
- en
- Open access
- closed
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