Where There is a Sea There are Pirates: Response to Jungherr, Jürgens, and Schoen
Social Science Computer Review
Mining web query logs to analyze political issues
Proceedings of the 3rd Annual ACM Web Science Conference
Predicting the 2011 dutch senate election results with Twitter
Proceedings of the Workshop on Semantic Analysis in Social Media
Tweeting across hashtags: overlapping users and the importance of language, topics, and politics
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Using explicit linguistic expressions of preference in social media to predict voting behavior
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Leveraging candidate popularity on Twitter to predict election outcome
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Tweets and votes, a special relationship: the 2009 federal election in germany
Proceedings of the 2nd workshop on Politics, elections and data
Multi-cycle forecasting of congressional elections with social media
Proceedings of the 2nd workshop on Politics, elections and data
Proceedings of the 3rd International Web Science Conference
A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data
Social Science Computer Review
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In their article â聙聹Predicting Elections with Twitter: What 140 Characters Reveal About Political Sentiment,â聙聺 the authors Andranik Tumasjan, Timm O. Sprenger, Philipp G. Sandner, and Isabell M. Welpe (TSSW) the authors claim that it would be possible to predict election outcomes in Germany by examining the relative frequency of the mentions of political parties in Twitter messages posted during the election campaign. In this response we show that the results of TSSW are contingent on arbitrary choices of the authors. We demonstrate that as of yet the relative frequency of mentions of German political parties in Twitter message allows no prediction of election results.