{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T10:25:51Z","timestamp":1771237551184,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T00:00:00Z","timestamp":1603929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>This work uses cognitive network science to reconstruct how experts, influential news outlets and social media perceived and reported the news \u201cCOVID-19 is a pandemic\u201d. In an exploratory corpus of 1 public speech, 10 influential news media articles on the same news and 37,500 trending tweets, the same pandemic declaration elicited a wide spectrum of perceptions retrieved by automatic language processing. While the WHO adopted a narrative strategy of mitigating the pandemic by raising public concern, some news media promoted fear for economic repercussions, while others channelled trust in contagion containment through semantic associations with science. In Italy, the first country to adopt a nationwide lockdown, social discourse perceived the pandemic with anger and fear, emotions of grief elaboration, but also with trust, a useful mechanism for coping with threats. Whereas news mostly elicited individual emotions, social media promoted much richer perceptions, where negative and positive emotional states coexisted, and where trust mainly originated from politics-related jargon rather than from science. This indicates that social media linked the pandemics to institutions and their intervention policies. Since both trust and fear strongly influence people\u2019s risk-averse behaviour and mental\/physical wellbeing, identifying evidence for these emotions is key under a global health crisis. Cognitive network science opens the way to unveiling the emotional framings of massively read news in automatic ways, with relevance for better understanding how information was framed and perceived by large audiences.<\/jats:p>","DOI":"10.3390\/systems8040038","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T09:44:53Z","timestamp":1603964693000},"page":"38","update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Cognitive Network Science Reconstructs How Experts, News Outlets and Social Media Perceived the COVID-19 Pandemic"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0003-1810-9699","authenticated-orcid":false,"given":"Massimo","family":"Stella","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK"},{"name":"Complex Science Consulting, Via Amilcare Foscarini 2, 73100 Lecce, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16598","DOI":"10.1038\/s41598-020-73510-5","article-title":"The covid-19 social media infodemic","volume":"10","author":"Cinelli","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1037\/hea0000875","article-title":"The novel coronavirus (COVID-2019) outbreak: Amplification of public health consequences by media exposure","volume":"39","author":"Garfin","year":"2020","journal-title":"Health Psychol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12435","DOI":"10.1073\/pnas.1803470115","article-title":"Bots increase exposure to negative and inflammatory content in online social systems","volume":"115","author":"Stella","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gallotti, R., Valle, F., Castaldo, N., Sacco, P., and De Domenico, M. (2020). Assessing the risks of \u201cinfodemics\u201d in response to COVID-19 epidemics. arXiv.","DOI":"10.1101\/2020.04.08.20057968"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e295","DOI":"10.7717\/peerj-cs.295","article-title":"Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media","volume":"6","author":"Stella","year":"2020","journal-title":"PeerJ Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2108423","DOI":"10.1155\/2019\/2108423","article-title":"Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics","volume":"2019","author":"Siew","year":"2019","journal-title":"Complexity"},{"key":"ref_7","unstructured":"De Deyne, S., Kenett, Y.N., Anaki, D., Faust, M., and Navarro, D. (2017). Large-Scale Network Representations of Semantics in the Mental Lexicon. Big Data in Cognitive Science, Routledge\/Taylor & Francis Group."},{"key":"ref_8","unstructured":"Fillmore, C.J., and Baker, C.F. (2001, January 3\u20134). Frame semantics for text understanding. Proceedings of the WordNet and Other Lexical Resources Workshop NAACL, Pittsburgh, PA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vitevitch, M.S. (2019). Network Science in Cognitive Psychology, Routledge.","DOI":"10.4324\/9780367853259"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104344","DOI":"10.1016\/j.cognition.2020.104344","article-title":"A brief history of risk","volume":"203","author":"Li","year":"2020","journal-title":"Cognition"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1511\/2001.28.344","article-title":"The nature of emotions","volume":"89","author":"Plutchik","year":"2001","journal-title":"Am. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3390\/bdcc4020014","article-title":"#lockdown: Network-Enhanced Emotional Profiling in the Time of COVID-19","volume":"4","author":"Stella","year":"2020","journal-title":"Big Data Cogn. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Stella, M. (2020). Social discourse and reopening after COVID-19: A post-lockdown analysis of flickering emotions and trending stances in Italy. PsyarXiv.","DOI":"10.5210\/fm.v25i11.10881"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e19447","DOI":"10.2196\/19447","article-title":"Global sentiments surrounding the COVID-19 pandemic on Twitter: Analysis of Twitter trends","volume":"6","author":"Lwin","year":"2020","journal-title":"JMIR Public Health Surveill."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aiello, L.M., Quercia, D., Zhou, K., Constantinides, M., \u0160\u0107epanovi\u0107, S., and Joglekar, S. (2020). How Epidemic Psychology Works on Social Media: Evolution of responses to the COVID-19 pandemic. arXiv.","DOI":"10.1057\/s41599-021-00861-3"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1057\/s41599-020-0507-3","article-title":"Measuring social response to different journalistic techniques on Facebook","volume":"7","author":"Schmidt","year":"2020","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e21597","DOI":"10.2196\/21597","article-title":"Collective response to the media coverage of COVID-19 Pandemic on Reddit and Wikipedia","volume":"22","author":"Gozzi","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Amancio, D.R. (2015). Probing the topological properties of complex networks modeling short written texts. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118394"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.tics.2013.04.010","article-title":"Networks in cognitive science","volume":"17","author":"Baronchelli","year":"2013","journal-title":"Trends Cogn. Sci."},{"key":"ref_20","unstructured":"Bond, F., and Foster, R. (2013, January 4\u20139). Linking and extending an open multilingual wordnet. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria."},{"key":"ref_21","unstructured":"Mohammad, S.M., and Turney, P.D. (2010, January 5). Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, Association for Computational Linguistics, Los Angeles, CA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Newman, M. (2018). Networks, Oxford University Press.","DOI":"10.1093\/oso\/9780198805090.001.0001"},{"key":"ref_23","unstructured":"K\u00fcbler-Ross, E., and Kessler, D. (2005). On Grief and Grieving, Simon and Schuster."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1037\/0022-3514.81.1.146","article-title":"Fear, anger, and risk","volume":"81","author":"Lerner","year":"2001","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_25","first-page":"110348","article-title":"Trait emotional intelligence and emotional experiences during the COVID-19 pandemic outbreak in Poland: A daily diary study","volume":"168","year":"2020","journal-title":"Personal. Individ. Differ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1016\/j.psyneuen.2012.11.015","article-title":"Hair cortisol, stress exposure, and mental health in humans: A systematic review","volume":"38","author":"Staufenbiel","year":"2013","journal-title":"Psychoneuroendocrinology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8788","DOI":"10.1073\/pnas.1320040111","article-title":"Experimental evidence of massive-scale emotional contagion through social networks","volume":"111","author":"Kramer","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Harper, C.A., Satchell, L.P., Fido, D., and Latzman, R.D. (2020). Functional fear predicts public health compliance in the COVID-19 pandemic. Int. J. Ment. Health Addict., 1\u201314.","DOI":"10.31234\/osf.io\/jkfu3"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Khemani, S. (2020). An Opportunity to Build Legitimacy and Trust in Public Institutions in the Time of Covid-19. World Bank Res. Policy Briefs, 148256.","DOI":"10.1596\/33715"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1037\/amp0000662","article-title":"Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being","volume":"75","author":"Sibley","year":"2020","journal-title":"Am. Psychol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"125","DOI":"10.3389\/fphy.2020.00125","article-title":"Measuring bot and human behavioral dynamics","volume":"8","author":"Pozzana","year":"2020","journal-title":"Front. Phys."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"L\u00fccking, A., Br\u00fcckner, S., Abrami, G., Uslu, T., and Mehler, A. (2020). Computational linguistic assessment of textbook and online learning media by means of threshold concepts in business education. arXiv.","DOI":"10.3389\/feduc.2020.578475"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Brito, A.C.M., Silva, F.N., and Amancio, D.R. (2020). A complex network approach to political analysis: Application to the Brazilian Chamber of Deputies. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0229928"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Choi, M., Aiello, L.M., Varga, K.Z., and Quercia, D. (2020, January 20\u201324). Ten Social Dimensions of Conversations and Relationships. Proceedings of the Web Conference 2020, Taipei, Taiwan.","DOI":"10.1145\/3366423.3380224"},{"key":"ref_35","unstructured":"Murray, C., Mitchell, L., Tuke, J., and Mackay, M. (2020). Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit. arXiv."},{"key":"ref_36","unstructured":"M\u00fcller, M., Salath\u00e9, M., and Kummervold, P.E. (2020). COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gencoglu, O. (2020). Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19. arXiv.","DOI":"10.3390\/make2040032"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1038\/d41586-020-00920-w","article-title":"Blocking information on COVID-19 can fuel the spread of misinformation","volume":"580","author":"Larson","year":"2020","journal-title":"Nature"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.mdpi.com\/2079-8954\/8\/4\/38\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:26:16Z","timestamp":1760178376000},"score":1,"resource":{"primary":{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.mdpi.com\/2079-8954\/8\/4\/38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,29]]},"references-count":38,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["systems8040038"],"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.3390\/systems8040038","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,29]]}}}