{"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/false-information-on-web-and-social-media-a","title":"False Information on Web and Social Media: A Survey","arxiv_id":"1804.08559","date":"2018-04-23","proceeding":null,"authors":["Srijan Kumar","Neil Shah"],"abstract":"False information can be created and spread easily through the web and social\nmedia platforms, resulting in widespread real-world impact. Characterizing how\nfalse information proliferates on social platforms and why it succeeds in\ndeceiving readers are critical to develop efficient detection algorithms and\ntools for early detection. A recent surge of research in this area has aimed to\naddress the key issues using methods based on feature engineering, graph\nmining, and information modeling. Majority of the research has primarily\nfocused on two broad categories of false information: opinion-based (e.g., fake\nreviews), and fact-based (e.g., false news and hoaxes). Therefore, in this\nwork, we present a comprehensive survey spanning diverse aspects of false\ninformation, namely (i) the actors involved in spreading false information,\n(ii) rationale behind successfully deceiving readers, (iii) quantifying the\nimpact of false information, (iv) measuring its characteristics across\ndifferent dimensions, and finally, (iv) algorithms developed to detect false\ninformation. In doing so, we create a unified framework to describe these\nrecent methods and highlight a number of important directions for future\nresearch.","url_abs":"http://arxiv.org/abs/1804.08559v1","url_pdf":"http://arxiv.org/pdf/1804.08559v1.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":"false-information-on-web-and-social-media-a","repo_url":"https://github.com/VVRoseth/2.-Identifying_misinformation_in_disasters_FEMA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"false-information-on-web-and-social-media-a","repo_url":"https://github.com/bwoodhamilton/Social-Media-Misinformation-During-Disasters","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"false-information-on-web-and-social-media-a","repo_url":"https://github.com/bwoodhamilton/client_project_group_3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"graph-mining","task_name":"Graph Mining"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.08559","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}