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10.1109/ASONAM.2016.7752311 http://scihub22266oqcxt.onion/10.1109/ASONAM.2016.7752311 C5508795!5508795!28713880     free     free     free       Proc+IEEE+ACM+Int+Conf+Adv+Soc+Netw+Anal+Min 2016 ; 206 (ä): 685-92 Nephropedia Template TP {{tp|p=28713880|t=2016. Finding Street Gang Members on Twitter |pdf=|usr=}}{{28713880}} gab.com Text Finding Street Gang Members on Twitter JO: Proc IEEE ACM Int Conf Adv Soc Netw Anal Min http://www.kidney.de/pget.php?&tw=1&pmid=28713880 #FOAvip Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate. Twit Text FOAVip Finding Street Gang Members on Twitter ????Proc IEEE ACM Int Conf Adv Soc Netw Anal Min http://www.kidney.de/pget.php?&tw=1&pmid=28713880 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28713880 #FOAvip English Wikipedia <ref>{{Cite pmid|28713880}}</ref> Finding Street Gang Members on Twitter #MMPMID28713880 Balasuriya L; Wijeratne S; Doran D; Sheth A Proc IEEE ACM Int Conf Adv Soc Netw Anal Min 2016[Aug]; 206 (ä): 685-92 PMID28713880show ga Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate. äFinding Street Gang Members on Twitter (epmc).pdf DeepDyve Pubget Overpricing