01984nas a2200349 4500008004100000245005500041210005500096260000800151520101900159653002601178653001601204653002301220653001701243653001301260653002001273653001301293653001901306653001901325653002701344653001601371653001201387653003101399653002901430653002101459653001201480653001901492653001501511653002801526100002801554700001501582856003701597 2011 eng d00aUnveiling Hidden Patterns to Find Social Relevance0 aUnveiling Hidden Patterns to Find Social Relevance cOct3 a
Twitter is both a useful social networking device and an incredible marketing tool. However, it is also a venue for dangerous stalkers and a sub-world of internet users that most people would intuitively avoid if seeing them in real life. It would improve Twitter's safety to have filters available which would allow users to select an audience for their status updates without being forced into changing their profiles to a private setting. The hypothetical filter, or model, studied in this paper was based on two particular attributes: activity correlations and vocabulary similarities between users and followers. If implemented, this model would restrict the availability of status updates to an automatically generated group of socially relevant followers. The result of this study shows that both of the attributes can be used to define social relevance, however, it was found that activity patterns have better predictive capabilities than correlating vocabulary usage between users and followers.
10aactivity correlations10aCorrelation10adangerous stalkers10adata privacy10aFacebook10ahidden patterns10aInternet10aInternet users10amarketing tool10aOnline Social Networks10aPolynomials10aPrivacy10asocial networking (online)10asocial networking device10asocial relevance10aTwitter10aTwitter safety10aVocabulary10avocabulary similarities1 aBaatarjav, Enkh-Amgalan1 aDantu, Ram uhttps://nsl.cse.unt.edu/node/238