RECORD
Finding Street Gang Members on Twitter
Social and Information Networks · Submitted 29 Oct 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our classifier achieves a promising F1 score with a low false positive rate.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. | background 1.00 | 0.12 | 0.03 | asserted 1.00 |
| s1 | Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. | background 0.90 | 0.06 | 0.04 | hedged possibility 1.00 |
| s2 | Finding these posts, however, requires a method to discover gang member Twitter profiles. | gap 0.67 | 0.07 | 0.02 | asserted 0.99 |
| s3 | This is a challenging task since gang members represent a very small population of the 320 million Twitter users. | background 0.93 | 0.11 | 0.02 | asserted 1.00 |
| s4 | This paper studies the problem of automatically finding gang members on Twitter. | contribution 0.93 | 0.82 | 0.04 | asserted 0.98 |
| s5 | It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. | contribution 0.87 | 0.93 | 0.08 | asserted 1.00 |
| s6 | 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. | result 0.61 | 0.90 | 0.09 | asserted 1.00 |
| s7 | Features from this review are used to train a series of supervised classifiers. | method 1.00 | 0.92 | 0.05 | asserted 1.00 |
| s8 | Our classifier achieves a promising F1 score with a low false positive rate. | result 1.00 | 0.96 | 0.67 | asserted 0.97 |
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The paper
Original abstract
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.
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