In the mood for being influential on Twitter

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In the mood for being influential on Twitter
Authors: Daniele Quercia, Jonathan Ellis, Licia Capra, Jon Crowcroft
Citation: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom)  : 307-314. 2011
Editors:
Publisher: IEEE
Meeting: 3rd IEEE International Conference on Social Computing
Database(s):
DOI: 10.1109/PASSAT/SocialCom.2011.27.
Link(s): http://www.cl.cam.ac.uk/~dq209/publications/quercia11mood.pdf
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In the mood for being influential on Twitter is a study on data from Twitter on the relationship between how influential a Twitter user is features on the language that is used in the tweet. They find that negativity determined through text sentiment analysis is correlated with Klout score and TrstRank.

[edit] Method

A dictionary called Linguistic Inquiry Word Count is used to characterize the tweets.

TrstRank is available from

http://api.infochimps.com/describe/soc/net/tw/trstrank

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