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dc.contributor.authorZaman, Tauhid
dc.contributor.authorFox, Emily B.
dc.contributor.authorBradlow, Eric T.
dc.date.accessioned2017-08-31T18:33:10Z
dc.date.available2017-08-31T18:33:10Z
dc.date.issued2014-10
dc.identifier.issn1932-6157
dc.identifier.urihttp://hdl.handle.net/1721.1/111083
dc.description.abstractWe predict the popularity of short messages called tweets created in the micro-blogging site known as Twitter. We measure the popularity of a tweet by the time-series path of its retweets, which is when people forward the tweet to others. We develop a probabilistic model for the evolution of the retweets using a Bayesian approach, and form predictions using only observations on the retweet times and the local network or “graph” structure of the retweeters. We obtain good step ahead forecasts and predictions of the final total number of retweets even when only a small fraction (i.e., less than one tenth) of the retweet path is observed. This translates to good predictions within a few minutes of a tweet being posted, and has potential implications for understanding the spread of broader ideas, memes or trends in social networks.en_US
dc.language.isoen_US
dc.publisherInstitute of Mathematical Statisticsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1214/14-AOAS741en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceProf. Zaman via Shikha Sharmaen_US
dc.titleA Bayesian approach for predicting the popularity of tweetsen_US
dc.typeArticleen_US
dc.identifier.citationZaman, Tauhid, et al. “A Bayesian Approach for Predicting the Popularity of Tweets.” The Annals of Applied Statistics 8, 3 (September 2014): 1583–1611 The Annals of Applied Statistics © 2014 Institute of Mathematical Statisticsen_US
dc.relation.journalAnnals of Applied Statisticsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsZaman, Tauhid; Fox, Emily B.; Bradlow, Eric T.en_US
dspace.embargo.termsNen_US
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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