Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Classifying sentiment in microblogs: is brevity an advantage?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
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Recently, political sentiment on social media websites has been receiving much attention both in research circles and in the news. However, political sentiment analysis has been largely performed on only a single social media source. It is unclear what outcomes would result if more than one source were used. We present a unique comparison of the textual content of two popular social media -- Twitter posts and YouTube comments -- over a common set of queries which include politicians, issues, and events. We show Twitter as a stream driven by news and outside sources with 40% share of its content lacking any sentiment, and YouTube as an outlet for opinionated speech. Specifically in YouTube, we find that the author's political stance and the sentiment of the document do not always match, and should be treated separately in analysis of political documents. We also examine the connection between social media sentiment and that of general population by comparing our findings to the Gallup poll, and show that neither discussion volume or sentiment expressed in the two social media were able to predict the republican Presidential nominee frontrunner.