{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T18:26:00Z","timestamp":1783448760946,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,3]],"date-time":"2021-10-03T00:00:00Z","timestamp":1633219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100018693","name":"Horizon Europe","doi-asserted-by":"publisher","award":["825171"],"award-info":[{"award-number":["825171"]}],"id":[{"id":"10.13039\/100018693","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The widespread use of social networks has brought to the foreground a very important issue, the veracity of the information circulating within them. Many natural language processing methods have been proposed in the past to assess a post\u2019s content with respect to its reliability; however, end-to-end approaches are not comparable in ability to human beings. To overcome this, in this paper, we propose the use of a more modular approach that produces indicators about a post\u2019s subjectivity and the stance provided by the replies it has received to date, letting the user decide whether (s)he trusts or does not trust the provided information. To this end, we fine-tuned state-of-the-art transformer-based language models and compared their performance with previous related work on stance detection and subjectivity analysis. Finally, we discuss the obtained results.<\/jats:p>","DOI":"10.3390\/info12100409","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T16:23:58Z","timestamp":1633623838000},"page":"409","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Combating Fake News with Transformers: A Comparative Analysis of Stance Detection and Subjectivity Analysis"],"prefix":"10.3390","volume":"12","author":[{"given":"Panagiotis","family":"Kasnesis","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, University of West Attica, 12244 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3221-0745","authenticated-orcid":false,"given":"Lazaros","family":"Toumanidis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, University of West Attica, 12244 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1921-4466","authenticated-orcid":false,"given":"Charalampos Z.","family":"Patrikakis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, University of West Attica, 12244 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Derczynski, L., Bontcheva, K., Liakata, M., Procter, R., Wong Sak Hoi, G., and Zubiaga, A. 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