mlsanityData to 7 Oct 2026

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

arXiv 2103.15909 ↗First posted 29 Mar 2021physics.soc-ph cs.CL cs.CYcited by 2

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 44 references that are in the map. First posted on arXiv 29 Mar 2021.

Builds on

14 of 44 references.

References · 14

  1. 2019RetinaFace: Single-stage Dense Face Localisation in the Wildcited by 164
  2. 2018Bots increase exposure to negative and inflammatory content in online social systemscited by 26
  3. 2020Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Textcited by 26
  4. 2016Instagram photos reveal predictive markers of depressioncited by 25
  5. 2020#lockdown: network-enhanced emotional profiling at the times of COVID-19cited by 10
  6. 2020Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social mediacited by 8
  7. 2016Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trumpcited by 7
  8. 2015Quantifying the Effect of Sentiment on Information Diffusion in Social Mediacited by 6
  9. 2019Human Values and Attitudes towards Vaccination in Social Mediacited by 6
  10. 2014Probing the topological properties of complex networks modeling short written textscited by 5
  11. 2021Cognitive network science for understanding online social cognitions: A brief reviewcited by 5
  12. 2021VaccinItaly: monitoring Italian conversations around vaccines on Twitter and Facebookcited by 3
2 more
  1. 2020From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Informationcited by 2
  2. 2021Networked partisanship and framing: a socio-semantic network analysis of the Italian debate on migrationcited by 2

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1.

2 papers.