{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/detecting-gang-involved-escalation-on-social","title":"Detecting Gang-Involved Escalation on Social Media Using Context","arxiv_id":"1809.03632","date":"2018-09-10","proceeding":"EMNLP 2018 10","authors":["Serina Chang","Ruiqi Zhong","Ethan Adams","Fei-Tzin Lee","Siddharth Varia","Desmond Patton","William Frey","Chris Kedzie","Kathleen McKeown"],"abstract":"Gang-involved youth in cities such as Chicago have increasingly turned to\nsocial media to post about their experiences and intents online. In some\nsituations, when they experience the loss of a loved one, their online\nexpression of emotion may evolve into aggression towards rival gangs and\nultimately into real-world violence. In this paper, we present a novel system\nfor detecting Aggression and Loss in social media. Our system features the use\nof domain-specific resources automatically derived from a large unlabeled\ncorpus, and contextual representations of the emotional and semantic content of\nthe user's recent tweets as well as their interactions with other users.\nIncorporating context in our Convolutional Neural Network (CNN) leads to a\nsignificant improvement.","url_abs":"http://arxiv.org/abs/1809.03632v1","url_pdf":"http://arxiv.org/pdf/1809.03632v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"detecting-gang-involved-escalation-on-social","repo_url":"https://github.com/serinachang5/contextifier","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.03632","atlas_url":"https://app.syntology.ai/?focus=1809.03632","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}