@conference {75,
	title = {Unveiling Hidden Patterns to Find Social Relevance},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on},
	year = {2011},
	month = {Oct},
	abstract = {<p>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{\textquoteright}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.</p>
},
	keywords = {activity correlations, Correlation, dangerous stalkers, data privacy, Facebook, hidden patterns, Internet, Internet users, marketing tool, Online Social Networks, Polynomials, Privacy, social networking (online), social networking device, social relevance, Twitter, Twitter safety, Vocabulary, vocabulary similarities},
	doi = {10.1109/PASSAT/SocialCom.2011.103},
	author = {Baatarjav, Enkh-Amgalan and Ram Dantu}
}
