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10.1109/ASONAM.2016.7752311

http://scihub22266oqcxt.onion/10.1109/ASONAM.2016.7752311
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C5508795!5508795 !28713880
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suck abstract from ncbi


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pmid28713880
      Proc+IEEE+ACM+Int+Conf+Adv+Soc+Netw+Anal+Min 2016 ; 206 (ä): 685-692
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  • 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): 685-692 PMID28713880 show 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 F(1) score with a low false positive rate.
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