{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T17:15:55Z","timestamp":1791220555754,"version":"4.1.0"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2025,7,7]]},"abstract":"<jats:p>The threat that online fake news and misinformation pose to democracy, justice, public confidence, and especially to vulnerable populations has led to a sharp increase in the need for fake news detection and intervention. Whether multi-modal or pure text-based, most existing fake news detection methods depend on textual analysis of entire articles. However, these fake news detection methods come with certain limitations. For instance, fake news detection methods that rely on full text can be computationally inefficient, demand large amounts of training data to achieve competitive accuracy, and may lack robustness across different datasets. This is because fake news datasets have strong variations in terms of the level and types of information they provide; where some can include large paragraphs of text with images and metadata, and others can be a few short sentences. Perhaps if one could only use minimal information to detect fake news, fake news detection methods could become more robust and resilient to the lack of information. We aim to overcome these limitations by detecting fake news using systematically selected, limited information that is both effective and capable of delivering robust, promising performance. We propose a framework called SLIM (Systematically-selected Limited Information) for fake news detection. In SLIM, we quantify the amount of information by introducing information-theoretic measures. SLIM leverages limited information (e.g., a few named entities) to achieve performance in fake news detection comparable to that of state-of-the-art obtained using the full text, even when the dataset is sparse. Furthermore, by combining various types of limited information, SLIM can perform even better while significantly reducing the quantity of information required for training compared to state-of-the-art language model-based fake news detection techniques.<\/jats:p>","DOI":"10.1145\/3748239.3748243","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T19:19:42Z","timestamp":1751915982000},"page":"20-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Is Less Really More? Fake News Detection with Limited Information"],"prefix":"10.1145","volume":"27","author":[{"given":"Zhaoyang","family":"Cao","sequence":"first","affiliation":[{"name":"Data Lab, EECS Department, Syracuse University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Syracuse University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Zafarani","sequence":"additional","affiliation":[{"name":"Data Lab, EECS Department, Syracuse University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICITEED.2018.8534898"},{"key":"e_1_2_1_2_1","first-page":"1","volume-title":"2019 International Conference on Advanced Electrical Engineering (ICAEE)","author":"Amine B. M.","year":"2019","unstructured":"B. M. Amine, A. Drif, and S. Giordano. 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