Open AccessProceedings Article
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
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TLDR
It is found that the mere number of messages mentioning a party reflects the election result, and joint mentions of two parties are in line with real world political ties and coalitions.Abstract:
Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician Our results show that Twitter is indeed used extensively for political deliberation We find that the mere number of messages mentioning a party reflects the election result Moreover, joint mentions of two parties are in line with real world political ties and coalitions An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further researchread more
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SSentiA: A Self-supervised Sentiment Analyzer for classification from unlabeled data
Salim Sazzed,Sampath Jayarathna +1 more
TL;DR: A self-super supervised hybrid methodology SSentiA (Self-supervised Sentiment Analyzer) is presented that couples an ML classifier with a lexicon-based classifier for sentiment classification from unlabeled data and suggests that in domains where annotated data are unavailable, SSent iA can significantly improve the performance of sentiment classification.
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Los Twindignados: The Rise of the Indignados Movement on Twitter
Narseo Vallina-Rodriguez,Salvatore Scellato,Hamed Haddadi,Carl Forsell,Jon Crowcroft,Cecilia Mascolo +5 more
TL;DR: This work studies an extensive set of millions of tweets themed around the 2011 regional and local elections in Spain and their accompanying civil unrest to suggest that online social networks serve more as media for interactive engagement between grassroots peers, rather than as platforms for politicians to attract mass attention.
References
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Journal ArticleDOI
The psychological meaning of words: LIWC and computerized text analysis methods
TL;DR: The Linguistic Inquiry and Word Count (LIWC) system as discussed by the authors is a text analysis system that counts words in psychologically meaningful categories to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles and individual differences.
Journal ArticleDOI
Detecting influenza epidemics using search engine query data
Jeremy Ginsberg,Matthew H. Mohebbi,Rajan Patel,Lynnette Brammer,Mark S. Smolinski,Lawrence B. Brilliant +5 more
TL;DR: A method of analysing large numbers of Google search queries to track influenza-like illness in a population and accurately estimate the current level of weekly influenza activity in each region of the United States with a reporting lag of about one day is presented.
Proceedings ArticleDOI
Why we twitter: understanding microblogging usage and communities
TL;DR: It is found that people use microblogging to talk about their daily activities and to seek or share information and the user intentions associated at a community level are analyzed to show how users with similar intentions connect with each other.
Proceedings ArticleDOI
The political blogosphere and the 2004 U.S. election: divided they blog
Lada A. Adamic,Natalie S. Glance +1 more
TL;DR: Differences in the behavior of liberal and conservative blogs are found, with conservative blogs linking to each other more frequently and in a denser pattern.
Proceedings ArticleDOI
Predicting the Future with Social Media
TL;DR: It is shown that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors and improve the forecasting power of social media.