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• W2277095315 abstract "There is a sociolinguistic interest in studying the socialpower dynamics that arise on online social networksand how these are reflected in their users’ use of lan-guage. Online social power prediction can also be usedto build tools for marketing and political campaigns thathelp them build an audience. Existing work has focusedon finding correlations between status and linguistic fea-tures in email, Wikipedia discussions, and court hearings.While a few studies have tried predicting status on thebasis of language on Twitter, they have proved less fruit-ful. We derive a rich set of features from literature ina variety of disciplines and build classifiers that assignTwitter users to different levels of status based on theirlanguage use. Using various metrics such as number offollowers and Klout score, we achieve a classification ac-curacy of individual users as high as 82.4%. In a secondstep, we reached up to 71.6% accuracy on the task of pre-dicting the more powerful user in a dyadic conversation.We find that the manner in which powerful users writediffers from low status users in a number of differentways: not only in the extent to which they deviate fromtheir usual writing habits when conversing with othersbut also in pronoun use, language complexity, sentimentexpression, and emoticon use. By extending our analysisto Facebook, we also assess the generalisability of ourresults and discuss differences and similarities betweenthese two sites." @default.
• W2277095315 created "2016-06-24" @default.
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• W2277095315 date "2014-05-16" @default.
• W2277095315 modified "2023-10-11" @default.
• W2277095315 title "Emoticons and Phrases: Status Symbols in Social Media" @default.
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• W2277095315 doi "https://doi.org/10.1609/icwsm.v8i1.14545" @default.
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