Cognitive networks identify the content of English and Italian popular posts about COVID-19 vaccines: Anticipation, logistics, conspiracy and loss of trust

Monitoring social discourse about COVID-19 vaccines is key to understanding how large populations perceive vaccination campaigns. We focus on 4765 unique popular tweets in English or Italian about COVID-19 vaccines between 12/2020 and 03/2021. One popular English tweet was liked up to 495,000 times, stressing how popular tweets affected cognitively massive populations. We investigate both text and multimedia in tweets, building a knowledge graph of syntactic/semantic associations in messages including visual features and indicating how online users framed social discourse mostly around the logistics of vaccine distribution. The English semantic frame of "vaccine" was highly polarised between trust/anticipation (towards the vaccine as a scientific asset saving lives) and anger/sadness (mentioning critical issues with dose administering). Semantic associations with "vaccine," "hoax" and conspiratorial jargon indicated the persistence of conspiracy theories and vaccines in massively read English posts (absent in Italian messages). The image analysis found that popular tweets with images of people wearing face masks used language lacking the trust and joy found in tweets showing people with no masks, indicating a negative affect attributed to face covering in social discourse. A behavioural analysis revealed a tendency for users to share content eliciting joy, sadness and disgust and to like less sad messages, highlighting an interplay between emotions and content diffusion beyond sentiment. With the AstraZeneca vaccine being suspended in mid March 2021, "Astrazeneca" was associated with trustful language driven by experts, but popular Italian tweets framed "vaccine" by crucially replacing earlier levels of trust with deep sadness. Our results stress how cognitive networks and innovative multimedia processing open new ways for reconstructing online perceptions about vaccines and trust.

[1]  Nicola Perra,et al.  Non-pharmaceutical interventions during the COVID-19 pandemic: A review , 2020, Physics Reports.

[2]  Michael S. Vitevitch.  Can Network Science Connect Mind, Brain, and Behavior? , 2019 .

[3]  Luciano da Fontoura Costa,et al.  Paragraph-based representation of texts: A complex networks approach , 2019, Inf. Process. Manag..

[4]  Massimo Stella.  Cognitive network science for understanding online social cognitions: A brief review , 2021, Topics in cognitive science.

[5]  Emilio Ferrara,et al.  Social Bots Distort the 2016 US Presidential Election Online Discussion , 2016, First Monday.

[6]  J. Russell,et al.  The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology , 2005, Development and Psychopathology.

[7]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[8]  Tolga Uslu,et al.  From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information , 2020, Complex..

[9]  Shui Yu,et al.  Analysis and Insights for Myths Circulating on Twitter During the COVID-19 Pandemic , 2020, IEEE Open Journal of the Computer Society.

[10]  Flaviano Morone,et al.  Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump , 2016, Scientific Reports.

[11]  Emilio Ferrara,et al.  Quantifying the Effect of Sentiment on Information Diffusion in Social Media , 2015, PeerJ Comput. Sci..

[12]  Joel Dyer,et al.  Public risk perception and emotion on Twitter during the Covid-19 pandemic , 2020, Appl. Netw. Sci..

[13]  Onur Varol,et al.  Journalists on Twitter: self-branding, audiences, and involvement of bots , 2019, Journal of Computational Social Science.

[14]  Stefanos Zafeiriou,et al.  RetinaFace: Single-stage Dense Face Localisation in the Wild , 2019, ArXiv.

[15]  Stefano Ceri,et al.  VaccinItaly: monitoring Italian conversations around vaccines on Twitter , 2021, ArXiv.

[16]  Sandra González-Bailón,et al.  Bots are less central than verified accounts during contentious political events , 2021, Proceedings of the National Academy of Sciences.

[17]  Miriam R. L. Petruck.  FRAME SEMANTICS , 1996 .

[18]  Yoed N. Kenett,et al.  Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics , 2018, Complex..

[19]  Daniele Quercia,et al.  How Epidemic Psychology Works on Social Media: Evolution of responses to the COVID-19 pandemic , 2020, ArXiv.

[20]  P. Hyland,et al.  Psychological characteristics associated with COVID-19 vaccine hesitancy and resistance in Ireland and the United Kingdom , 2021, Nature Communications.

[21]  Thomas T. Hills,et al.  Bad News Has Wings: Dread Risk Mediates Social Amplification in Risk Communication , 2018, Risk analysis : an official publication of the Society for Risk Analysis.

[22]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[23]  Brigitta Dóczi.  An Overview of Conceptual Models and Theories of Lexical Representation in the Mental Lexicon , 2019, The Routledge Handbook of Vocabulary Studies.

[24]  Kyriaki Kalimeri,et al.  Human Values and Attitudes towards Vaccination in Social Media , 2019, WWW.

[25]  Saif M. Mohammad,et al.  Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text , 2016, ArXiv.

[26]  Massimo Stella,et al.  #lockdown: network-enhanced emotional profiling at the times of COVID-19 , 2020, ArXiv.

[27]  Massimo Stella,et al.  Forma mentis networks map how nursing and engineering students enhance their mindsets about innovation and health during professional growth , 2020, PeerJ Comput. Sci..

[28]  Jingwen Zhang,et al.  Feeling angry: the effects of vaccine misinformation and refutational messages on negative emotions and vaccination attitude , 2020, Journal of health communication.

[29]  Karin Ackermann,et al.  The Nature Of Emotion Fundamental Questions , 2016 .

[30]  Emilio Ferrara,et al.  Bots increase exposure to negative and inflammatory content in online social systems , 2018, Proceedings of the National Academy of Sciences.

[31]  Fabio Saracco,et al.  Networked partisanship and framing: A socio-semantic network analysis of the Italian debate on migration , 2021, PloS one.

[32]  Massimo Stella,et al.  Forma mentis networks quantify crucial differences in STEM perception between students and experts , 2019, PloS one.

[33]  Amy Beth Warriner,et al.  Norms of valence, arousal, and dominance for 13,915 English lemmas , 2013, Behavior Research Methods.

[34]  Diego R. Amancio,et al.  Probing the Topological Properties of Complex Networks Modeling Short Written Texts , 2014, PloS one.

[35]  Peter D. Turney,et al.  Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon , 2010, HLT-NAACL 2010.

[36]  Thomas T. Hills.  The Dark Side of Information Proliferation , 2018, Perspectives on psychological science : a journal of the Association for Psychological Science.

[37]  Christopher M. Danforth,et al.  Instagram photos reveal predictive markers of depression , 2016, EPJ Data Science.

[38]  M. Pompili,et al.  Effects of the lockdown on the mental health of the general population during the COVID-19 pandemic in Italy: Results from the COMET collaborative network , 2020, European Psychiatry.