{"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/forecasting-the-presence-and-intensity-of","title":"Forecasting the presence and intensity of hostility on Instagram using linguistic and social features","arxiv_id":"1804.06759","date":"2018-04-18","proceeding":null,"authors":["Ping Liu","Joshua Guberman","Libby Hemphill","Aron Culotta"],"abstract":"Online antisocial behavior, such as cyberbullying, harassment, and trolling,\nis a widespread problem that threatens free discussion and has negative\nphysical and mental health consequences for victims and communities. While\nprior work has proposed automated methods to identify hostile comments in\nonline discussions, these methods work retrospectively on comments that have\nalready been posted, making it difficult to intervene before an interaction\nescalates. In this paper we instead consider the problem of forecasting future\nhostilities in online discussions, which we decompose into two tasks: (1) given\nan initial sequence of non-hostile comments in a discussion, predict whether\nsome future comment will contain hostility; and (2) given the first hostile\ncomment in a discussion, predict whether this will lead to an escalation of\nhostility in subsequent comments. Thus, we aim to forecast both the presence\nand intensity of hostile comments based on linguistic and social features from\nearlier comments. To evaluate our approach, we introduce a corpus of over 30K\nannotated Instagram comments from over 1,100 posts. Our approach is able to\npredict the appearance of a hostile comment on an Instagram post ten or more\nhours in the future with an AUC of .82 (task 1), and can furthermore\ndistinguish between high and low levels of future hostility with an AUC of .91\n(task 2).","url_abs":"http://arxiv.org/abs/1804.06759v1","url_pdf":"http://arxiv.org/pdf/1804.06759v1.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":"forecasting-the-presence-and-intensity-of","repo_url":"https://github.com/tapilab/icwsm-2018-hostility","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"task-2","task_name":"Task 2"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.06759","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}