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

arXiv: https://arxiv.org/abs/2103.15909 · HTML: https://mlsanity.com/paper/2103.15909/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://mlsanity.com/paper/1905.00641/) (2019)
- [Bots increase exposure to negative and inflammatory content in online social systems](https://mlsanity.com/paper/1802.07292/) (2018)
- [Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text](https://mlsanity.com/paper/2005.11882/) (2020)
- [Instagram photos reveal predictive markers of depression](https://mlsanity.com/paper/1608.03282/) (2016)
- [#lockdown: network-enhanced emotional profiling at the times of COVID-19](https://mlsanity.com/paper/2005.04404/) (2020)
- [Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media](https://mlsanity.com/paper/2003.08835/) (2020)
- [Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump](https://mlsanity.com/paper/1610.01587/) (2016)
- [Quantifying the Effect of Sentiment on Information Diffusion in Social Media](https://mlsanity.com/paper/1506.06072/) (2015)
- [Human Values and Attitudes towards Vaccination in Social Media](https://mlsanity.com/paper/1904.00691/) (2019)
- [Probing the topological properties of complex networks modeling short written texts](https://mlsanity.com/paper/1412.8504/) (2014)
- [Cognitive network science for understanding online social cognitions: A brief review](https://mlsanity.com/paper/2102.12799/) (2021)
- [VaccinItaly: monitoring Italian conversations around vaccines on Twitter and Facebook](https://mlsanity.com/paper/2101.03757/) (2021)
- [From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information](https://mlsanity.com/paper/2002.01454/) (2020)
- [Networked partisanship and framing: a socio-semantic network analysis of the Italian debate on migration](https://mlsanity.com/paper/2103.04653/) (2021)

## Built on by (most cited first, 2 of 2)

- [PyPlutchik: visualising and comparing emotion-annotated corpora](https://mlsanity.com/paper/2105.04295/) (2021)
- [Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns](https://mlsanity.com/paper/2201.07538/) (2022)

## 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.