In the mood for being influential on Twitter
|Conference paper (help)|
|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|
|Meeting:||3rd IEEE International Conference on Social Computing|
|Web:||DuckDuckGo Bing Google Yahoo! — Google PDF|
|Article:||Google Scholar PubMed|
|Restricted:||DTU Digital Library|
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.
A dictionary called Linguistic Inquiry Word Count is used to characterize the tweets.
TrstRank is available from