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Vasileios Lampos - 2014 - Predicting and Characterising User Impact on Twitter
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EACL 2014 - E14-1043
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Vasileios Lampos - 2014 - Predicting and Characterising User Impact on Twitter
1.
Impactdistributionforuserswith high(H)valuesofthisfeatureas opposedtolow (L).Redlineisthe meanimpactscore. Tweetalotabout ShowbizandPolitics, with(L)orwithout (NL)using<URL>’s *bubblesizeinverseproportionaltolearnedGPARDkernel lengthscalesandrepresentpredicitverelevance;colourssplit betweenthetypesoffeatures;nuancesofthesamecolour areonlyforvisualeffect TopicscomputedoverreferenceTwittercorpus SpectralclusteringwithNPMIassimilaritymeasure damon,potter #tvd,harry elena,kate portman,pattinson hermione,jennifer senate,republican gop,police arrested,voters robbery,democrats presidential,elections topics(100)* Profile features(18)* Data 38,020UKlocatedTwitterusers from 14.04.2011-12.04.2012 ~48mildeduplicatedtweets Greenrepresentscorrelationusingprofile features,blueisthegainaddingtopics Task:Predictuserimpactbasedon featuresundertheuser’scontrol Ridgeregression(LIN) Non-linearmethods(GP) GaussianProcesseswithARDkernel impactPrediction β ν Impactdefinition β ν,δ τ PredictingandCharacterisingUserImpactonTwitter VasileiosLamposβ ,NikolaosAletrasν ,DanielPreoţiuc-Pietroδ ,TrevorCohnτ v.lampos@ucl.ac.uk,{n.aletras,daniel}@dcs.shef.ac.uk,trevor.cohn@gmail.com Source:
http://www.adweek.com/digital/best-practices-for-hashtags/
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