{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T13:43:50Z","timestamp":1747316630432,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":44,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819951765"},{"type":"electronic","value":"9789819951772"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-5177-2_1","type":"book-chapter","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T23:02:34Z","timestamp":1691017354000},"page":"3-22","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["People Still Care About Facts: Twitter Users Engage More with\u00a0Factual Discourse than Misinformation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2617-0847","authenticated-orcid":false,"given":"Luiz","family":"Giovanini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9355-4381","authenticated-orcid":false,"given":"Shlok","family":"Gilda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5021-0311","authenticated-orcid":false,"given":"Mirela","family":"Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabr\u00edcio","family":"Ceschin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prakash","family":"Shrestha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Brant","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8391-8460","authenticated-orcid":false,"given":"Juliana","family":"Fernandes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catia S.","family":"Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9","family":"Gr\u00e9gio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniela","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,3]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"155961","DOI":"10.1109\/ACCESS.2020.3019600","volume":"8","author":"MS Al-Rakhami","year":"2020","unstructured":"Al-Rakhami, M.S., Al-Amri, A.M.: Lies kill, facts save: detecting COVID-19 misinformation in Twitter. IEEE Access 8, 155961\u2013155970 (2020)","journal-title":"IEEE Access"},{"issue":"01","key":"1_CR2","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1609\/icwsm.v13i01.3208","volume":"13","author":"KK Aldous","year":"2019","unstructured":"Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. Proc. Int. AAAI Conf. Web Soc. Med. 13(01), 47\u201357 (2019)","journal-title":"Proc. Int. AAAI Conf. Web Soc. Med."},{"key":"1_CR3","unstructured":"Allport, G.W., Postman, L.: The psychology of rumor. J. Clin. Psychol. (1947)"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Apuke, O.D., Omar, B.: Fake news and COVID-19: Modelling the predictors of fake news sharing among social media users. Telemat. Inform. 101475 (2020)","DOI":"10.1016\/j.tele.2020.101475"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Avram, M., Micallef, N., Patil, S., Menczer, F.: Exposure to social engagement metrics increases vulnerability to misinformation. arXiv preprint arXiv:2005.04682 (2020)","DOI":"10.37016\/mr-2020-033"},{"key":"1_CR6","unstructured":"Bell, B., Gallagher, F.: Who is spreading COVID-19 misinformation and why. https:\/\/abcnews.go.com\/US\/spreading-covid-19-misinformation\/story?id=70615995 (May 2020). Accessed 21 Nov 2020"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Brennen, J.S., Simon, F.M., Nielsen, R.K.: Beyond (MIS) representation: Visuals in COVID-19 misinformation. Int. J. Press\/Politics (2020)","DOI":"10.1177\/1940161220964780"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Cinelli, M., et al.: The COVID-19 social media infodemic. arXiv preprint arXiv:2003.05004 (2020)","DOI":"10.1038\/s41598-020-73510-5"},{"key":"1_CR9","unstructured":"Cohen, J.: Verified Twitter users shared an all-time-high amount of fake news in 2020. https:\/\/www.pcmag.com\/news\/verified-twitter-users-shared-an-all-time-high-amount-of-fake-news-in-2020, February 2021. Accessed 4 Sept 2021"},{"issue":"3","key":"1_CR10","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1080\/00221309809595548","volume":"125","author":"DM Corey","year":"1998","unstructured":"Corey, D.M., Dunlap, W.P., Burke, M.J.: Averaging correlations: expected values and bias in combined Pearson RS and Fisher\u2019s Z transformations. J. Gener. Psychol. 125(3), 245\u2013261 (1998)","journal-title":"J. Gener. Psychol."},{"key":"1_CR11","unstructured":"for Countering Digital Hate, C.: The disinformation dozen: Why platforms must act on twelve leading online anti-vaxxers (2021). https:\/\/counterhate.com\/"},{"key":"1_CR12","unstructured":"Cui, L., Lee, D.: COAID: COVID-19 healthcare misinformation dataset (2020)"},{"key":"1_CR13","unstructured":"Deebani, W., Kachouie, N.N.: Ensemble Correlation Coefficient. In: International Symposium on Artificial Intelligence and Mathematics (2018)"},{"key":"1_CR14","unstructured":"Gilbert, C., Hutto, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International Conference on Weblogs and Social Media (ICWSM-2014), vol. 81 (2014)"},{"issue":"5","key":"1_CR15","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1037\/a0015141","volume":"96","author":"J Graham","year":"2009","unstructured":"Graham, J., Haidt, J., Nosek, B.A.: Liberals and conservatives rely on different sets of moral foundations. J. Pers. Soc. Psychol. 96(5), 1029\u20131046 (2009)","journal-title":"J. Pers. Soc. Psychol."},{"issue":"1","key":"1_CR16","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1348\/135532504322776889","volume":"9","author":"PA Granhag","year":"2004","unstructured":"Granhag, P.A., Andersson, L.O., Str\u00f6mwall, L.A., Hartwig, M.: Imprisoned knowledge: criminals\u2019 beliefs about deception. Leg. Criminol. Psychol. 9(1), 103\u2013119 (2004)","journal-title":"Leg. Criminol. Psychol."},{"issue":"1","key":"1_CR17","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11211-007-0034-z","volume":"20","author":"J Haidt","year":"2007","unstructured":"Haidt, J., Graham, J.: When morality opposes justice: conservatives have moral intuitions that liberals may not recognize. Soc. Justice Res. 20(1), 98\u2013116 (2007)","journal-title":"Soc. Justice Res."},{"key":"1_CR18","unstructured":"Huang, B., Carley, K.M.: Disinformation and misinformation on twitter during the novel coronavirus outbreak. arXiv preprint arXiv:2006.04278 (2020)"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Islam, A.N., Laato, S., Talukder, S., Sutinen, E.: Misinformation sharing and social media fatigue during COVID-19: an affordance and cognitive load perspective. Technol. Forecast. Soc. Change 159 (2020)","DOI":"10.1016\/j.techfore.2020.120201"},{"issue":"3","key":"1_CR20","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1002\/hbe2.202","volume":"2","author":"J Jiang","year":"2020","unstructured":"Jiang, J., Chen, E., Yan, S., Lerman, K., Ferrara, E.: Political polarization drives online conversations about COVID-19 in the united states. Human Behavi. Emerg. Technol. 2(3), 200\u2013211 (2020)","journal-title":"Human Behavi. Emerg. Technol."},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Jiang, S., Wilson, C.: Linguistic signals under misinformation and fact-checking: evidence from user comments on social media. Proc. ACM Hum. Comput. Interact. 2(CSCW), 1\u201323 (2018)","DOI":"10.1145\/3274351"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Loper, E., Bird, S.: NLTK: The natural language toolkit. arXiv preprint cs\/0205028 (2002)","DOI":"10.3115\/1118108.1118117"},{"issue":"2","key":"1_CR23","doi-asserted-by":"publisher","first-page":"458","DOI":"10.17645\/mac.v8i2.3219","volume":"8","author":"A Lovari","year":"2020","unstructured":"Lovari, A.: Spreading (dis) trust: COVID-19 misinformation and government intervention in Italy. Media Commun. 8(2), 458\u2013461 (2020)","journal-title":"Media Commun."},{"key":"1_CR24","unstructured":"Memon, S.A., Carley, K.M.: Characterizing COVID-19 misinformation communities using a novel twitter dataset. arXiv preprint arXiv:2008.00791 (2020)"},{"key":"1_CR25","unstructured":"Mitra, T., Gilbert, E.: CredBank: a large-scale social media corpus with associated credibility annotations. In: Ninth International AAAI Conference on Web and Social Media (2015)"},{"issue":"11","key":"1_CR26","doi-asserted-by":"publisher","DOI":"10.2196\/30642","volume":"7","author":"G Muric","year":"2021","unstructured":"Muric, G., Wu, Y., Ferrara, E.: COVID-19 vaccine hesitancy on social media: building a public twitter data set of antivaccine content, vaccine misinformation, and conspiracies. JMIR Public Health Surveill. 7(11), e30642 (2021)","journal-title":"JMIR Public Health Surveill."},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Paka, W.S., Bansal, R., Kaushik, A., Sengupta, S., Chakraborty, T.: Cross-sean: A cross-stitch semi-supervised neural attention model for COVID-19 fake news detection. Appl. Soft Comput. 107 (2021)","DOI":"10.1016\/j.asoc.2021.107393"},{"key":"1_CR28","unstructured":"Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of liwc2015. Technical report (2015)"},{"key":"1_CR29","unstructured":"Rid, T.: Active measures: The secret history of disinformation and political warfare. Farrar, Straus and Giroux (2020)"},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Roozenbeek, J., et al.: Susceptibility to misinformation about COVID-19 around the world. R. Soc. Open Sci. 7(10) (2020)","DOI":"10.1098\/rsos.201199"},{"key":"1_CR31","unstructured":"Schild, L., Ling, C., Blackburn, J., Stringhini, G., Zhang, Y., Zannettou, S.: \u201cgo eat a bat, chang!\u201d: an early look on the emergence of Sinophobic behavior on web communities in the face of COVID-19. arXiv preprint arXiv:2004.04046 (2020)"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Schroeder, D.T., Pogorelov, K., Schaal, F., Filkukova, P., Langguth, J.: Wico graph: a labeled dataset of twitter subgraphs based on conspiracy theory and 5g-corona misinformation tweets. In: ICAART 2021 : 13th International Conference on Agents and Artificial Intelligence. OSF Preprints (2021)","DOI":"10.5220\/0010262802570266"},{"key":"1_CR33","doi-asserted-by":"crossref","unstructured":"Shahi, G.K., Dirkson, A., Majchrzak, T.A.: An exploratory study of COVID-19 misinformation on twitter. Online Soc. Netw. Med. 22 (2021)","DOI":"10.1016\/j.osnem.2020.100104"},{"key":"1_CR34","unstructured":"Sharma, K., Seo, S., Meng, C., Rambhatla, S., Liu, Y.: COVID-19 on social media: Analyzing misinformation in twitter conversations. arXiv preprint arXiv:2003.12309 (2020)"},{"issue":"1","key":"1_CR35","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1145\/3137597.3137600","volume":"19","author":"K Shu","year":"2017","unstructured":"Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. SIGKDD Explor. Newsl. 19(1), 22\u201336 (2017)","journal-title":"SIGKDD Explor. Newsl."},{"key":"1_CR36","doi-asserted-by":"crossref","unstructured":"Silva, M., Giovanini, L., Fernandes, J., Oliveira, D., Silva, C.S.: What makes disinformation ads engaging? a case study of Facebook ads from the Russian active measures campaign. J. Interact. Advert. 1\u201320 (2023)","DOI":"10.1080\/15252019.2023.2173991"},{"key":"1_CR37","unstructured":"Singh, L., et al.: A first look at COVID-19 information and misinformation sharing on twitter. arXiv preprint arXiv:2003.13907 (2020)"},{"key":"1_CR38","doi-asserted-by":"crossref","unstructured":"Swami, V., Barron, D.: Analytic thinking, rejection of coronavirus (COVID-19) conspiracy theories, and compliance with mandated social-distancing: Direct and indirect relationships in a nationally representative sample of adults in the united kingdom. OSF Preprints (2020)","DOI":"10.31219\/osf.io\/nmx9w"},{"key":"1_CR39","doi-asserted-by":"crossref","unstructured":"Tagliabue, F., Galassi, L., Mariani, P.: The \u201cpandemic\u201d of disinformation in covid-19. SN Compr. Clin. Med. 2, 1287\u20131289 (2020)","DOI":"10.1007\/s42399-020-00439-1"},{"key":"1_CR40","doi-asserted-by":"crossref","unstructured":"Vo, N., Lee, K.: Learning from fact-checkers: analysis and generation of fact-checking language. In: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019)","DOI":"10.1145\/3331184.3331248"},{"issue":"6380","key":"1_CR41","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1126\/science.aap9559","volume":"359","author":"S Vosoughi","year":"2018","unstructured":"Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146\u20131151 (2018)","journal-title":"Science"},{"key":"1_CR42","unstructured":"Wineburg, S., McGrew, S., Breakstone, J., Ortega, T.: Evaluating information: the cornerstone of civic online reasoning. Stanford Digital Repository. Accessed 8 Jan 2018 (2016)"},{"key":"1_CR43","unstructured":"Yang, K.C., Torres-Lugo, C., Menczer, F.: Prevalence of low-credibility information on twitter during the COVID-19 outbreak. arXiv preprint arXiv:2004.14484 (2020)"},{"issue":"5","key":"1_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3395046","volume":"53","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Zafarani, R.: A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput. Surv. 53(5), 1\u201340 (2020)","journal-title":"ACM Comput. Surv."}],"container-title":["Lecture Notes in Computer Science","Security and Privacy in Social Networks and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-5177-2_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T23:05:14Z","timestamp":1691017514000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-5177-2_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819951765","9789819951772"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-5177-2_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"3 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SocialSec","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Security and Privacy in Social Networks and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canterbury","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"socialsec2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/nss-socialsec2023.cyber.kent.ac.uk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}