{"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/community-member-retrieval-on-social-media","title":"Community Member Retrieval on Social Media using Textual Information","arxiv_id":"1804.05499","date":"2018-04-16","proceeding":"NAACL 2018 6","authors":["Aaron Jaech","Shobhit Hathi","Mari Ostendorf"],"abstract":"This paper addresses the problem of community membership detection using only\ntext features in a scenario where a small number of positive labeled examples\ndefines the community. The solution introduces an unsupervised proxy task for\nlearning user embeddings: user re-identification. Experiments with 16 different\ncommunities show that the resulting embeddings are more effective for community\nmembership identification than common unsupervised representations.","url_abs":"http://arxiv.org/abs/1804.05499v1","url_pdf":"http://arxiv.org/pdf/1804.05499v1.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":"community-member-retrieval-on-social-media","repo_url":"https://github.com/ajaech/twittercommunities","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}