{"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/zoom-out-and-observe-news-environment","title":"Zoom Out and Observe: News Environment Perception for Fake News Detection","arxiv_id":"2203.10885","date":"2022-03-21","proceeding":"ACL 2022 5","authors":["Qiang Sheng","Juan Cao","Xueyao Zhang","Rundong Li","Danding Wang","Yongchun Zhu"],"abstract":"Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones, existing methods observe the language patterns of the news post and \"zoom in\" to verify its content with knowledge sources or check its readers' replies. However, these methods neglect the information in the external news environment where a fake news post is created and disseminated. The news environment represents recent mainstream media opinion and public attention, which is an important inspiration of fake news fabrication because fake news is often designed to ride the wave of popular events and catch public attention with unexpected novel content for greater exposure and spread. To capture the environmental signals of news posts, we \"zoom out\" to observe the news environment and propose the News Environment Perception Framework (NEP). For each post, we construct its macro and micro news environment from recent mainstream news. Then we design a popularity-oriented and a novelty-oriented module to perceive useful signals and further assist final prediction. Experiments on our newly built datasets show that the NEP can efficiently improve the performance of basic fake news detectors.","url_abs":"https://arxiv.org/abs/2203.10885v2","url_pdf":"https://arxiv.org/pdf/2203.10885v2.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":"zoom-out-and-observe-news-environment","repo_url":"https://github.com/ictmcg/news-environment-perception","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"fake-news-detection","task_name":"Fake News Detection"},{"task_slug":"misinformation","task_name":"Misinformation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2203.10885","atlas_url":"https://app.syntology.ai/?focus=2203.10885","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10885"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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