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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 research

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Citations
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Detecting variation of emotions in online activities

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Dissertation

Using Social Media to Predict Traffic Flow under Special Event Conditions

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Sentiment Analysis of Arabic Slang Comments on Facebook

TL;DR: A Slang Sentimental Words and Idioms Lexicon (SSWIL) of opinion words is built and a Gaussian kernel SVM classifier for Arabic slang language to classify Arabic news’ comments on Facebook is proposed.
Proceedings ArticleDOI

Discovery, Retrieval, and Analysis of the 'Star Wars' Botnet in Twitter

TL;DR: The Star Wars botnet as mentioned in this paper contains more than 350,000 bots tweeting random quotations exclusively from Star Wars novels, each of which contains a single type of bot, showing exactly the same properties throughout the botnet.
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

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

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.