{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T03:42:55Z","timestamp":1771299775479,"version":"3.50.1"},"reference-count":138,"publisher":"Association for Computing Machinery (ACM)","issue":"7","license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"crossref","award":["1943370"],"award-info":[{"award-number":["1943370"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2025,10,18]]},"abstract":"<jats:p>Social media have become essential in daily life, serving as platforms for public opinion, personal growth, and news consumption. This shift in how people access news has led to an increase in misinformation, including fake news intended to deceive. Various psychological and social factors, such as emotional appeal, cognitive biases, and social influence, drive individuals' susceptibility to believing false information. While prior studies have examined the believability of news content, large-scale analyses on real social media platforms remain limited, as well as studying factors influencing the believability of real and fake news separately and analyzing similarities and differences.<\/jats:p>\n          <jats:p>This study introduces a new dataset of 14,535 Twitter user comments, annotated to measure user believability in real versus fake news. Using this dataset, we address the problem of predicting news believability and study which user or news characteristics predict believability in real and fake news, as well as checking whether believability enhances fake news detection. We employ machine learning models incorporating news style, emotional content, and user traits to predict believability and apply explainability methods to clarify key characteristics influencing user belief. Overall, the models achieved significant results in detecting news believability and several news and user-based features such as writing style, emotion, personality, and psychology have been individuated as strong predictors of believability in news. We further integrate believability insights into advanced fake news detectors, demonstrating improved performance. To our knowledge, this is the first large-scale human-annotated English dataset designed for studying news believability, which we have released for use in future research.<\/jats:p>","DOI":"10.1145\/3757691","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T17:32:00Z","timestamp":1760635920000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Understanding News Consumers' Perceptions of Believability: A Study of Real and Fake News"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5333-9873","authenticated-orcid":false,"given":"Mostofa Najmus","family":"Sakib","sequence":"first","affiliation":[{"name":"Boise State University, Boise, Idaho, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6938-7309","authenticated-orcid":false,"given":"Md Shoaib","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Boise State University, Boise, Idaho, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0361-9728","authenticated-orcid":false,"given":"Francesca","family":"Spezzano","sequence":"additional","affiliation":[{"name":"Boise State University, Boise, Idaho, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9744-7076","authenticated-orcid":false,"given":"Anne","family":"Hamby","sequence":"additional","affiliation":[{"name":"Boise State University, Boise, Idaho, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2020. Personality Prediction from Text. https:\/\/github.com\/jkwieser\/personality-prediction-from-text. Accessed: 2025-04-12."},{"key":"e_1_2_1_2_1","first-page":"1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 7, CSCW1","author":"Aghajari Zhila","year":"2023","unstructured":"Zhila Aghajari, Eric PS Baumer, and Dominic DiFranzo. 2023. Reviewing interventions to address misinformation: the need to expand our vision beyond an individualistic focus. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1-34."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1257\/jep.31.2.211"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/08838151.2019.1653101","article-title":"Conferring resistance to digital disinformation: The inoculating influence of procedural news knowledge","volume":"63","author":"Amazeen Michelle A","year":"2019","unstructured":"Michelle A Amazeen and Erik P Bucy. 2019. Conferring resistance to digital disinformation: The inoculating influence of procedural news knowledge. Journal of Broadcasting & Electronic Media 63, 3 (2019), 415-432.","journal-title":"Journal of Broadcasting & Electronic Media"},{"key":"e_1_2_1_5_1","volume-title":"Exposure to social engagement metrics increases vulnerability to misinformation. arXiv preprint arXiv:2005.04682","author":"Avram Mihai","year":"2020","unstructured":"Mihai Avram, Nicholas Micallef, Sameer Patil, and Filippo Menczer. 2020. Exposure to social engagement metrics increases vulnerability to misinformation. arXiv preprint arXiv:2005.04682 (2020)."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1037\/xge0000729"},{"key":"e_1_2_1_7_1","first-page":"251 17","volume-title":"Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics","volume":"1","author":"Bar-Haim Roy","year":"2017","unstructured":"Roy Bar-Haim, Indrajit Bhattacharya, Francesco Dinuzzo, Amrita Saha, and Noam Slonim. 2017. Stance Classification of Context-Dependent Claims. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, Mirella Lapata, Phil Blunsom, and Alexander Koller (Eds.). Association for Computational Linguistics, Valencia, Spain, 251-261. https:\/\/aclanthology.org\/E17-1024"},{"key":"e_1_2_1_8_1","volume-title":"Adverbial stance types in English. Discourse processes 11, 1","author":"Biber Douglas","year":"1988","unstructured":"Douglas Biber and Edward Finegan. 1988. Adverbial stance types in English. Discourse processes 11, 1 (1988), 1-34."},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1177\/0261927X06286380","article-title":"Rhetorical question use and resistance to persuasion: An attitude strength analysis","volume":"25","author":"Blankenship Kevin L","year":"2006","unstructured":"Kevin L Blankenship and Traci Y Craig. 2006. Rhetorical question use and resistance to persuasion: An attitude strength analysis. Journal of language and social psychology 25, 2 (2006), 111-128.","journal-title":"Journal of language and social psychology"},{"key":"e_1_2_1_10_1","volume-title":"Mood and persuasion: A cognitive response analysis. Personality and social psychology bulletin 16, 2","author":"Bless Herbert","year":"1990","unstructured":"Herbert Bless, Gerd Bohner, Norbert Schwarz, and Fritz Strack. 1990. Mood and persuasion: A cognitive response analysis. Personality and social psychology bulletin 16, 2 (1990), 331-345."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1177\/1461444817750396"},{"key":"e_1_2_1_12_1","volume-title":"Negative affect and social judgment: The differential impact of anger and sadness. European Journal of social psychology 24, 1","author":"Bodenhausen Galen V","year":"1994","unstructured":"Galen V Bodenhausen, Lori A Sheppard, and Geoffrey P Kramer. 1994. Negative affect and social judgment: The differential impact of anger and sadness. European Journal of social psychology 24, 1 (1994), 45-62."},{"key":"e_1_2_1_13_1","unstructured":"R.L. Boyd A. Ashokkumar S. Seraj and J.W. Pennebaker. 2022. The Development and Psychometric Properties of LIWC-22. Technical Report. University of Texas at Austin Austin TX."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2020.106511"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0101832"},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","first-page":"e0253717","DOI":"10.1371\/journal.pone.0253717","article-title":"Determinants of individuals' belief in fake news: A scoping review determinants of belief in fake news","volume":"16","author":"Bryanov Kirill","year":"2021","unstructured":"Kirill Bryanov and Victoria Vziatysheva. 2021. Determinants of individuals' belief in fake news: A scoping review determinants of belief in fake news. PLoS one 16, 6 (2021), e0253717.","journal-title":"PLoS one"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0253717"},{"key":"e_1_2_1_18_1","volume-title":"Dual-process theories in social psychology","author":"Chaiken Shelly","unstructured":"Shelly Chaiken and Yaacov Trope. 1999. Dual-process theories in social psychology. Guilford Press."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106115"},{"key":"e_1_2_1_20_1","volume-title":"Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing. 289-292","author":"Che Xunru","year":"2018","unstructured":"Xunru Che, Dana\u00eb Metaxa-Kakavouli, and Jeffrey T Hancock. 2018. Fake news in the news: An analysis of partisan coverage of the fake news phenomenon. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing. 289-292."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3580805"},{"key":"e_1_2_1_22_1","volume-title":"International conference on machine learning. PMLR, 1725-1735","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and deep graph convolutional networks. In International conference on machine learning. PMLR, 1725-1735."},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1080\/1057356980140204","article-title":"The multisyllabic word dilemma: Helping students build meaning, spell, and read ''big'' words","volume":"14","author":"Cunningham Patricia M","year":"1998","unstructured":"Patricia M Cunningham. 1998. The multisyllabic word dilemma: Helping students build meaning, spell, and read ''big'' words. Reading & Writing Quarterly: Overcoming Learning Difficulties 14, 2 (1998), 189-218.","journal-title":"Reading & Writing Quarterly: Overcoming Learning Difficulties"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1108\/JSIT-03-2022-0060"},{"key":"e_1_2_1_25_1","first-page":"853","volume-title":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"14","author":"Dai Enyan","year":"2020","unstructured":"Enyan Dai, Yiwei Sun, and Suhang Wang. 2020. Ginger cannot cure cancer: Battling fake health news with a comprehensive data repository. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 14. 853-862."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.2021.1990607"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462990"},{"key":"e_1_2_1_28_1","first-page":"1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 7, CSCW2","author":"Drolsbach Chiara Patricia","year":"2023","unstructured":"Chiara Patricia Drolsbach and Nicolas Pr\u00f6llochs. 2023. Diffusion of community fact-checked misinformation on twitter. Proceedings of the ACM on Human-Computer Interaction 7, CSCW2 (2023), 1-22."},{"key":"e_1_2_1_29_1","first-page":"1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 6, CSCW2","author":"Efstratiou Alexandros","year":"2022","unstructured":"Alexandros Efstratiou and Emiliano De Cristofaro. 2022. Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1-35."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1037\/1089-2680.8.2.78"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.127"},{"key":"e_1_2_1_32_1","first-page":"1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 7, CSCW2","author":"Guo Chen","year":"2023","unstructured":"Chen Guo, Nan Zheng, and Chengqi Guo. 2023. Seeing is not believing: a nuanced view of misinformation warning efficacy on video-sharing social media platforms. Proceedings of the ACM on Human-Computer Interaction 7, CSCW2 (2023), 1-35."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271709"},{"key":"e_1_2_1_34_1","first-page":"15486","article-title":"Mixed graph neural networkbased fake news detection for sustainable vehicular social networks","volume":"24","author":"Guo Zhiwei","year":"2022","unstructured":"Zhiwei Guo, Keping Yu, Alireza Jolfaei, Gang Li, Feng Ding, and Amin Beheshti. 2022. Mixed graph neural networkbased fake news detection for sustainable vehicular social networks. IEEE Transactions on Intelligent Transportation Systems 24, 12 (2022), 15486-15498.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_2_1_35_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems 30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_2_1_36_1","volume-title":"Graph neural networks with continual learning for fake news detection from social media. arXiv preprint arXiv:2007.03316","author":"Han Yi","year":"2020","unstructured":"Yi Han, Shanika Karunasekera, and Christopher Leckie. 2020. Graph neural networks with continual learning for fake news detection from social media. arXiv preprint arXiv:2007.03316 (2020)."},{"key":"e_1_2_1_37_1","first-page":"1348 13","volume-title":"Proceedings of the Sixth International Joint Conference on Natural Language Processing, Ruslan Mitkov and Jong C. Park (Eds.). Asian Federation of Natural Language Processing","author":"Hasan Kazi Saidul","year":"2013","unstructured":"Kazi Saidul Hasan and Vincent Ng. 2013. Stance Classification of Ideological Debates: Data, Models, Features, and Constraints. In Proceedings of the Sixth International Joint Conference on Natural Language Processing, Ruslan Mitkov and Jong C. Park (Eds.). Asian Federation of Natural Language Processing, Nagoya, Japan, 1348-1356. https:\/\/aclanthology.org\/I13-1191"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1002\/cb.376"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v11i1.14976"},{"key":"e_1_2_1_40_1","volume-title":"The COVMis-stance dataset: stance detection on twitter for COVID-19 misinformation. arXiv preprint arXiv:2204.02000","author":"Hou Yanfang","year":"2022","unstructured":"Yanfang Hou, Peter van der Putten, and Suzan Verberne. 2022. The COVMis-stance dataset: stance detection on twitter for COVID-19 misinformation. arXiv preprint arXiv:2204.02000 (2022)."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2675133.2675202"},{"key":"e_1_2_1_42_1","first-page":"1","volume-title":"Proceedings of the ACM on human-computer interaction 5, CSCW1","author":"Jahanbakhsh Farnaz","year":"2021","unstructured":"Farnaz Jahanbakhsh, Amy X Zhang, Adam J Berinsky, Gordon Pennycook, David G Rand, and David R Karger. 2021. Exploring lightweight interventions at posting time to reduce the sharing of misinformation on social media. Proceedings of the ACM on human-computer interaction 5, CSCW1 (2021), 1-42."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32233-5_49"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i5.20517"},{"key":"e_1_2_1_45_1","volume-title":"Does media literacy help identification of fake news? Information literacy helps, but other literacies don't. American behavioral scientist 65, 2","author":"Jones-Jang S Mo","year":"2021","unstructured":"S Mo Jones-Jang, Tara Mortensen, and Jingjing Liu. 2021. Does media literacy help identification of fake news? Information literacy helps, but other literacies don't. American behavioral scientist 65, 2 (2021), 371-388."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3674882"},{"key":"e_1_2_1_47_1","first-page":"1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 6, CSCW2","author":"Kaufman Robert A","year":"2022","unstructured":"Robert A Kaufman, Michael Robert Haupt, and Steven P Dow. 2022. Who's in the Crowd Matters: Cognitive Factors and Beliefs Predict Misinformation Assessment Accuracy. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1-18."},{"key":"e_1_2_1_48_1","first-page":"8783","volume-title":"Proceedings of the AAAI conference on artificial intelligence","volume":"34","author":"Serena Khoo Ling Min","year":"2020","unstructured":"Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, and Jing Jiang. 2020. Interpretable rumor detection in microblogs by attending to user interactions. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 8783-8790."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3196709.3196774"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1177\/1529100620946707"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3369026"},{"key":"e_1_2_1_52_1","volume-title":"Adaptive ensembles of fine-tuned transformers for llm-generated text detection. arXiv preprint arXiv:2403.13335","author":"Lai Zhixin","year":"2024","unstructured":"Zhixin Lai, Xuesheng Zhang, and Suiyao Chen. 2024. Adaptive ensembles of fine-tuned transformers for llm-generated text detection. arXiv preprint arXiv:2403.13335 (2024)."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2017.05.007"},{"key":"e_1_2_1_54_1","volume-title":"Detecting misinformation with llm-predicted credibility signals and weak supervision. arXiv preprint arXiv:2309.07601","author":"Leite Jo\u00e3o A","year":"2023","unstructured":"Jo\u00e3o A Leite, Olesya Razuvayevskaya, Kalina Bontcheva, and Carolina Scarton. 2023. Detecting misinformation with llm-predicted credibility signals and weak supervision. arXiv preprint arXiv:2309.07601 (2023)."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1257\/aer.20191777"},{"key":"e_1_2_1_56_1","volume-title":"Colleen M Seifert, Norbert Schwarz, and John Cook.","author":"Lewandowsky Stephan","year":"2012","unstructured":"Stephan Lewandowsky, Ullrich KH Ecker, Colleen M Seifert, Norbert Schwarz, and John Cook. 2012. Misinformation and its correction: Continued influence and successful debiasing. Psychological science in the public interest 13, 3 (2012), 106-131."},{"key":"e_1_2_1_57_1","volume-title":"Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors. arXiv preprint arXiv:2403.09747","author":"Li Guanghua","year":"2024","unstructured":"Guanghua Li, Wensheng Lu, Wei Zhang, Defu Lian, Kezhong Lu, Rui Mao, Kai Shu, and Hao Liao. 2024. Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors. arXiv preprint arXiv:2403.09747 (2024)."},{"key":"e_1_2_1_58_1","volume-title":"Large Language Model Agent for Fake News Detection. arXiv preprint arXiv:2405.01593","author":"Li Xinyi","year":"2024","unstructured":"Xinyi Li, Yongfeng Zhang, and Edward C Malthouse. 2024. Large Language Model Agent for Fake News Detection. arXiv preprint arXiv:2405.01593 (2024)."},{"key":"e_1_2_1_59_1","first-page":"1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 6, CSCW1","author":"Lima Gabriel","year":"2022","unstructured":"Gabriel Lima, Jiyoung Han, and Meeyoung Cha. 2022. Others are to blame: Whom people consider responsible for online misinformation. Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (2022), 1-25."},{"key":"e_1_2_1_60_1","volume-title":"FakeNewsGPT4: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs. arXiv preprint arXiv:2403.01988","author":"Liu Xuannan","year":"2024","unstructured":"Xuannan Liu, Peipei Li, Huaibo Huang, Zekun Li, Xing Cui, Jiahao Liang, Lixiong Qin, Weihong Deng, and Zhaofeng He. 2024. FakeNewsGPT4: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs. arXiv preprint arXiv:2403.01988 (2024)."},{"key":"e_1_2_1_61_1","first-page":"1354","volume-title":"POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection. In Findings of the Association for Computational Linguistics: NAACL 2022","author":"Liu Yujian","year":"2022","unstructured":"Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nicholas Beauchamp, and Lu Wang. 2022. POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection. In Findings of the Association for Computational Linguistics: NAACL 2022, Marine Carpuat, Marie-Catherine de Marneffe, and Ivan Vladimir Meza Ruiz (Eds.). Association for Computational Linguistics, Seattle, United States, 1354-1374. doi:10. 18653\/v1\/2022.findings-naacl.101"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1177\/1461444809342697"},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the eighth international joint conference on natural language processing (volume 2: Short papers). 252-256","author":"Long Yunfei","year":"2017","unstructured":"Yunfei Long, Qin Lu, Rong Xiang, Minglei Li, and Chu-Ren Huang. 2017. Fake news detection through multiperspective speaker profiles. In Proceedings of the eighth international joint conference on natural language processing (volume 2: Short papers). 252-256."},{"key":"e_1_2_1_64_1","first-page":"1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 6, CSCW2","author":"Lu Zhuoran","year":"2022","unstructured":"Zhuoran Lu, Patrick Li, Weilong Wang, and Ming Yin. 2022. The effects of AI-based credibility indicators on the detection and spread of misinformation under social influence. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1-27."},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1177\/0093650220921321"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103354"},{"key":"e_1_2_1_67_1","unstructured":"Jing Ma and Wei Gao. 2020. Debunking rumors on twitter with tree transformer. ACL."},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2017.23"},{"key":"e_1_2_1_69_1","volume-title":"Reliance on emotion promotes belief in fake news. Cognitive research: principles and implications 5","author":"Martel Cameron","year":"2020","unstructured":"Cameron Martel, Gordon Pennycook, and David G Rand. 2020. Reliance on emotion promotes belief in fake news. Cognitive research: principles and implications 5 (2020), 1-20."},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1186\/s41235-020-00252-3"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1177\/2056305120935102"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0302380"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3418490"},{"key":"e_1_2_1_74_1","unstructured":"Ashlee Milton and Maria Soledad Pera. 2020. What snippets feel: Depression search and snippets. (2020)."},{"key":"e_1_2_1_75_1","first-page":"1","article-title":"Social Media Use Only Helps, and Does Not Harm","volume":"2","author":"Mitev Kaloyan","year":"2021","unstructured":"Kaloyan Mitev, Netta Weinstein, Sonya Karabeliova, Thuy-vy Nguyen, Wilbert Law, and Andrew Przybylski. 2021. Social Media Use Only Helps, and Does Not Harm, Daily Interactions andWell-Being. Technology, Mind, and Behavior 2, 1 (jun 25 2021). https:\/\/tmb.apaopen.org\/pub\/social-media-abstinence-and-interactions.","journal-title":"Daily Interactions andWell-Being. Technology, Mind, and Behavior"},{"key":"e_1_2_1_76_1","volume-title":"Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC","author":"Mohammad Saif","year":"2018","unstructured":"Saif Mohammad. 2018. Word Affect Intensities. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Nicoletta Calzolari, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Koiti Hasida, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, H\u00e9l\u00e8ne Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis, and Takenobu Tokunaga (Eds.). European Language Resources Association (ELRA), Miyazaki, Japan. https:\/\/aclanthology.org\/L18-1027"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S16-1003"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1177\/0002764219878224"},{"key":"e_1_2_1_79_1","volume-title":"Fake news detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673","author":"Monti Federico","year":"2019","unstructured":"Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M Bronstein. 2019. Fake news detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673 (2019)."},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.3390\/computation9020020"},{"key":"e_1_2_1_81_1","volume-title":"Md Abdul Hamid, Muhammad Mostafa Monowar, and Md Saifur Rahman.","author":"Mridha Muhammad Firoz","year":"2021","unstructured":"Muhammad Firoz Mridha, Ashfia Jannat Keya, Md Abdul Hamid, Muhammad Mostafa Monowar, and Md Saifur Rahman. 2021. A comprehensive review on fake news detection with deep learning. IEEE access 9 (2021), 156151-156170."},{"key":"e_1_2_1_82_1","volume-title":"Computational personality analysis: Introduction, practical applications and novel directions","author":"Neuman Yair","unstructured":"Yair Neuman. 2016. Computational personality analysis: Introduction, practical applications and novel directions. Springer."},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412046"},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1093\/jcr\/ucad006"},{"key":"e_1_2_1_85_1","volume-title":"Stance classification for rumour analysis in twitter: Exploiting affective information and conversation structure. arXiv preprint arXiv:1901.01911","author":"Pamungkas Endang Wahyu","year":"2019","unstructured":"Endang Wahyu Pamungkas, Valerio Basile, and Viviana Patti. 2019. Stance classification for rumour analysis in twitter: Exploiting affective information and conversation structure. arXiv preprint arXiv:1901.01911 (2019)."},{"key":"e_1_2_1_86_1","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/21670811.2021.1981768","article-title":"News Engagement: The Roles of Technological Affordance, Emotion, and Social Endorsement","volume":"9","author":"Park Sora","year":"2021","unstructured":"Sora Park, Yoonmo Sang, Jaemin Jung, and Natalie Jomini Stroud. 2021. News Engagement: The Roles of Technological Affordance, Emotion, and Social Endorsement. Digital Journalism 9, 8 (2021), 1007-1017.","journal-title":"Digital Journalism"},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cognition.2018.06.011"},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2014.02.045"},{"key":"e_1_2_1_89_1","volume-title":"Automatic detection of fake news. arXiv preprint arXiv:1708.07104","author":"P\u00e9rez-Rosas Ver\u00f3nica","year":"2017","unstructured":"Ver\u00f3nica P\u00e9rez-Rosas, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2017. Automatic detection of fake news. arXiv preprint arXiv:1708.07104 (2017)."},{"key":"e_1_2_1_90_1","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1037\/0022-3514.40.3.432","article-title":"Effects of rhetorical questions on persuasion: A cognitive response analysis","volume":"40","author":"Petty Richard E","year":"1981","unstructured":"Richard E Petty, John T Cacioppo, and Martin Heesacker. 1981. Effects of rhetorical questions on persuasion: A cognitive response analysis. Journal of personality and social psychology 40, 3 (1981), 432.","journal-title":"Journal of personality and social psychology"},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110235"},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1559-1816.2004.tb02547.x"},{"key":"e_1_2_1_93_1","volume-title":"A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638","author":"Potthast Martin","year":"2017","unstructured":"Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. 2017. A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638 (2017)."},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/533"},{"key":"e_1_2_1_95_1","volume-title":"Socialnomics: How Social Media Transforms the Way We Live and Do Business","author":"Qualman E.","year":"2012","unstructured":"E. Qualman. 2012. Socialnomics: How Social Media Transforms the Way We Live and Do Business. Wiley. https:\/\/books.google.com\/books?id=p2A96JKRlY4C"},{"key":"e_1_2_1_96_1","doi-asserted-by":"crossref","first-page":"165","DOI":"10.58680\/rte198315716","article-title":"Syntactic complexity and readers' perception of an author's credibility","volume":"17","author":"Rafoth Bennett A","year":"1983","unstructured":"Bennett A Rafoth and Warren Combs. 1983. Syntactic complexity and readers' perception of an author's credibility. Research in the Teaching of English 17, 2 (1983), 165-169.","journal-title":"Research in the Teaching of English"},{"key":"e_1_2_1_97_1","first-page":"25","article-title":"Convolutional neural networks for sentence classification","volume":"6","author":"Rakhlin A","year":"2016","unstructured":"A Rakhlin. 2016. Convolutional neural networks for sentence classification. GitHub 6 (2016), 25.","journal-title":"GitHub"},{"key":"e_1_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-023-00437-1"},{"key":"e_1_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00054"},{"key":"e_1_2_1_100_1","volume-title":"Emotion elicits the social sharing of emotion: Theory and empirical review. Emotion review 1, 1","author":"Rim\u00e9 Bernard","year":"2009","unstructured":"Bernard Rim\u00e9. 2009. Emotion elicits the social sharing of emotion: Theory and empirical review. Emotion review 1, 1 (2009), 60-85."},{"key":"e_1_2_1_101_1","first-page":"1018","volume-title":"Proceedings of The Web Conference","author":"Rosenfeld Nir","year":"2020","unstructured":"Nir Rosenfeld, Aron Szanto, and David C Parkes. 2020. A kernel of truth: Determining rumor veracity on twitter by diffusion pattern alone. In Proceedings of The Web Conference 2020. 1018-1028."},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addbeh.2020.106487"},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132877"},{"key":"e_1_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-12428-8"},{"key":"e_1_2_1_105_1","doi-asserted-by":"publisher","DOI":"10.1108\/AJIM-08-2021-0232"},{"key":"e_1_2_1_106_1","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1080\/15205436.2020.1716983","article-title":"When is disinformation (in) credible? Experimental findings on message characteristics and individual differences","volume":"23","author":"Schaewitz Leonie","year":"2020","unstructured":"Leonie Schaewitz, Jan P Kluck, Lukas Kl\u00f6sters, and Nicole C Kr\u00e4mer. 2020. When is disinformation (in) credible? Experimental findings on message characteristics and individual differences. Mass Communication and Society 23, 4 (2020), 484-509.","journal-title":"Mass Communication and Society"},{"key":"e_1_2_1_107_1","volume-title":"INFLUENCE OF THE 'NEWS FINDS ME' PERCEPTION ON NEWS SHARING AND NEWS CONSUMPTION ON SOCIAL MEDIA. Communication Today 10, 2 (11","author":"Segado-Boj Francisco","year":"2019","unstructured":"Francisco Segado-Boj, Jes\u00fas D\u00edaz-Campo, and Raquel Quevedo-Redondo. 2019. INFLUENCE OF THE 'NEWS FINDS ME' PERCEPTION ON NEWS SHARING AND NEWS CONSUMPTION ON SOCIAL MEDIA. Communication Today 10, 2 (11 2019), 90-105. https:\/\/libproxy.boisestate.edu\/login?url=https:\/\/www.proquest.com\/scholarly-journals\/ influence-news-finds-me-perception-on-sharing\/docview\/2317565001\/se-2 Copyright - Copyright Univerzita sv. Cyrila a Metoda v Trnave Nov 2019; Last updated - 2023-11-22."},{"key":"e_1_2_1_108_1","first-page":"1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 6, CSCW1","author":"Shahid Farhana","year":"2022","unstructured":"Farhana Shahid, Shrirang Mare, and Aditya Vashistha. 2022. Examining source effects on perceptions of fake news in rural India. Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (2022), 1-29."},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1145\/3305260"},{"key":"e_1_2_1_110_1","volume-title":"d.]. Linking Adverbials in Student and Professional Writing in Literary Studies: What Makes Writing Mature","author":"Shaw Philip","unstructured":"Philip Shaw. [n. d.]. Linking Adverbials in Student and Professional Writing in Literary Studies: What Makes Writing Mature. Academic Writing ([n.d.]), 215."},{"key":"e_1_2_1_111_1","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/978-3-030-72240-1_9","volume-title":"Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28-April 1, 2021, Proceedings, Part II 43","author":"Shrestha Anu","year":"2021","unstructured":"Anu Shrestha and Francesca Spezzano. 2021. Textual characteristics of news title and body to detect fake news: A reproducibility study. In Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28-April 1, 2021, Proceedings, Part II 43. Springer, 120-133."},{"key":"e_1_2_1_112_1","volume-title":"Studying fake news via network analysis: detection and mitigation. Emerging research challenges and opportunities in computational social network analysis and mining","author":"Shu Kai","year":"2019","unstructured":"Kai Shu, H Russell Bernard, and Huan Liu. 2019. Studying fake news via network analysis: detection and mitigation. Emerging research challenges and opportunities in computational social network analysis and mining (2019), 43-65."},{"key":"e_1_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330935"},{"key":"e_1_2_1_114_1","volume-title":"FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media. arXiv preprint arXiv:1809.01286","author":"Shu Kai","year":"2018","unstructured":"Kai Shu, Deepak Mahudeswaran, SuhangWang, Dongwon Lee, and Huan Liu. 2018. FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media. arXiv preprint arXiv:1809.01286 (2018)."},{"key":"e_1_2_1_115_1","doi-asserted-by":"publisher","DOI":"10.1145\/3137597.3137600"},{"key":"e_1_2_1_116_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290994"},{"key":"e_1_2_1_117_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341161.3342927"},{"key":"e_1_2_1_118_1","doi-asserted-by":"crossref","first-page":"111715","DOI":"10.1016\/j.knosys.2024.111715","article-title":"DANES: Deep neural network ensemble architecture for social and textual context-aware fake news detection","volume":"294","author":"Truica Ciprian-Octavian","year":"2024","unstructured":"Ciprian-Octavian Truica, Elena-Simona Apostol, and Panagiotis Karras. 2024. DANES: Deep neural network ensemble architecture for social and textual context-aware fake news detection. Knowledge-Based Systems 294 (2024), 111715.","journal-title":"Knowledge-Based Systems"},{"key":"e_1_2_1_119_1","first-page":"6246","article-title":"Intensity-Valued Emotions Help Stance Detection of Climate Change Twitter Data","author":"Upadhyaya Apoorva","year":"2023","unstructured":"Apoorva Upadhyaya, Marco Fisichella, and Wolfgang Nejdl. 2023. Intensity-Valued Emotions Help Stance Detection of Climate Change Twitter Data. In IJCAI. 6246-6254.","journal-title":"IJCAI."},{"key":"e_1_2_1_120_1","first-page":"3948","volume-title":"Proceedings of the ACM Web Conference","author":"Upadhyaya Apoorva","year":"2023","unstructured":"Apoorva Upadhyaya, Marco Fisichella, and Wolfgang Nejdl. 2023. A multi-task model for emotion and offensive aided stance detection of climate change tweets. In Proceedings of the ACM Web Conference 2023. 3948-3958."},{"key":"e_1_2_1_121_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrp.2020.104005"},{"key":"e_1_2_1_122_1","doi-asserted-by":"publisher","DOI":"10.1080\/10584609.2020.1744778"},{"key":"e_1_2_1_123_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_2_1_124_1","volume-title":"2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 593-594","author":"Wang Liqiang","year":"2018","unstructured":"Liqiang Wang, Yafang Wang, Gerard De Melo, and Gerhard Weikum. 2018. Five shades of untruth: Finer-grained classification of fake news. In 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 593-594."},{"key":"e_1_2_1_125_1","volume-title":"International conference on machine learning. PMLR, 6861-6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861-6871."},{"key":"e_1_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_2_1_127_1","first-page":"2501","volume-title":"Proceedings of the ACM web conference","author":"Xu Weizhi","year":"2022","unstructured":"Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu, and Liang Wang. 2022. Evidence-aware fake news detection with graph neural networks. In Proceedings of the ACM web conference 2022. 2501-2510."},{"key":"e_1_2_1_128_1","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2253-2262","author":"Yang Ruichao","year":"2022","unstructured":"Ruichao Yang, Xiting Wang, Yiqiao Jin, Chaozhuo Li, Jianxun Lian, and Xing Xie. 2022. Reinforcement subgraph reasoning for fake news detection. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2253-2262."},{"key":"e_1_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1174"},{"key":"e_1_2_1_130_1","first-page":"1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 7, CSCW1","author":"Zade Himanshu","year":"2023","unstructured":"Himanshu Zade, Megan Woodruff, Erika Johnson, Mariah Stanley, Zhennan Zhou, Minh Tu Huynh, Alissa Elizabeth Acheson, Gary Hsieh, and Kate Starbird. 2023. Tweet Trajectory and AMPS-based Contextual Cues can Help Users Identify Misinformation. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1-27."},{"key":"e_1_2_1_131_1","doi-asserted-by":"publisher","DOI":"10.1177\/01655515221087683"},{"key":"e_1_2_1_132_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40649-019-0069-y"},{"key":"e_1_2_1_133_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.03.004"},{"key":"e_1_2_1_134_1","volume-title":"Graph neural networks: A review of methods and applications. AI open 1","author":"Zhou Jie","year":"2020","unstructured":"Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI open 1 (2020), 57-81."},{"key":"e_1_2_1_135_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412880"},{"key":"e_1_2_1_136_1","volume-title":"Pacific-Asia Conference on knowledge discovery and data mining. Springer, 354-367","author":"Zhou Xinyi","year":"2020","unstructured":"Xinyi Zhou, Jindi Wu, and Reza Zafarani. 2020. : Similarity-aware multi-modal fake news detection. In Pacific-Asia Conference on knowledge discovery and data mining. Springer, 354-367."},{"key":"e_1_2_1_137_1","volume-title":"Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations. arXiv preprint arXiv:1609.09028","author":"Zubiaga Arkaitz","year":"2016","unstructured":"Arkaitz Zubiaga, Elena Kochkina, Maria Liakata, Rob Procter, and Michal Lukasik. 2016. Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations. arXiv preprint arXiv:1609.09028 (2016)."},{"key":"e_1_2_1_138_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2017.11.009"}],"container-title":["Proceedings of the ACM on Human-Computer Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3757691","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3757691","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T17:33:51Z","timestamp":1760636031000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3757691"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,16]]},"references-count":138,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,10,18]]}},"alternative-id":["10.1145\/3757691"],"URL":"https:\/\/doi.org\/10.1145\/3757691","relation":{},"ISSN":["2573-0142"],"issn-type":[{"value":"2573-0142","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,16]]},"assertion":[{"value":"2025-10-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}