{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T11:39:34Z","timestamp":1772710774638,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T00:00:00Z","timestamp":1728172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Online social networks (OSNs) are inundated with an enormous daily influx of news shared by users worldwide. Information can originate from any OSN user and quickly spread, making the task of fact-checking news both time-consuming and resource-intensive. To address this challenge, researchers are exploring machine learning techniques to automate fake news detection. This paper specifically focuses on detecting the stance of content producers\u2014whether they support or oppose the subject of the content. Our study aims to develop and evaluate advanced text-mining models that leverage pre-trained language models enhanced with meta features derived from headlines and article bodies. We sought to determine whether incorporating the cosine distance feature could improve model prediction accuracy. After analyzing and assessing several previous competition entries, we identified three key tasks for achieving high accuracy: (1) a multi-stage approach that integrates classical and neural network classifiers, (2) the extraction of additional text-based meta features from headline and article body columns, and (3) the utilization of recent pre-trained embeddings and transformer models.<\/jats:p>","DOI":"10.3390\/fi16100364","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T07:30:18Z","timestamp":1728286218000},"page":"364","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Stance Detection in the Context of Fake News\u2014A New Approach"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7832-5081","authenticated-orcid":false,"given":"Izzat","family":"Alsmadi","sequence":"first","affiliation":[{"name":"Department of Computational, Engineering, and Mathematical Sciences, College of Arts and Science, Texas A&M University, San Antonio, TX 78224, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7539-0822","authenticated-orcid":false,"given":"Iyad","family":"Alazzam","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid 21163, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8170-8312","authenticated-orcid":false,"given":"Mohammad","family":"Al-Ramahi","sequence":"additional","affiliation":[{"name":"Department of Accounting and Finance, College of Business, Texas A&M University, San Antonio, TX 78224, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1169-9502","authenticated-orcid":false,"given":"Mohammad","family":"Zarour","sequence":"additional","affiliation":[{"name":"Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa 13133, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.bushor.2009.09.003","article-title":"Users of the world, unite! 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