# Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic

arXiv: https://arxiv.org/abs/2108.10735 · HTML: https://mlsanity.com/paper/2108.10735/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 13 of 13)

- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach](https://mlsanity.com/paper/2010.07647/) (2020)
- [DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection](https://mlsanity.com/paper/2107.10648/) (2021)
- [Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter](https://mlsanity.com/paper/2003.11906/) (2020)
- [Misinformation Detection on YouTube Using Video Captions](https://mlsanity.com/paper/2107.00941/) (2021)
- [Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation](https://mlsanity.com/paper/2101.08419/) (2021)
- [What Types of COVID-19 Conspiracies are Populated by Twitter Bots?](https://mlsanity.com/paper/2004.09531/) (2020)
- [Characterizing Discourse about COVID-19 Vaccines: A Reddit Version of the Pandemic Story](https://mlsanity.com/paper/2101.06321/) (2021)
- [VaccinItaly: monitoring Italian conversations around vaccines on Twitter and Facebook](https://mlsanity.com/paper/2101.03757/) (2021)
- [CoVaxxy: A Collection of English-language Twitter Posts About COVID-19 Vaccines](https://mlsanity.com/paper/2101.07694/) (2021)
- [Algorithmic Detection and Analysis of Vaccine-Denialist Sentiment Clusters in Social Networks](https://mlsanity.com/paper/1905.12908/) (2019)
- [Advertisers Jump on Coronavirus Bandwagon: Politics, News, and Business](https://mlsanity.com/paper/2003.00923/) (2020)

## Built on by (most cited first, 4 of 4)

- [GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection](https://mlsanity.com/paper/2202.12478/) (2022)
- [Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking](https://mlsanity.com/paper/2310.07613/) (2023)
- [Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination](https://mlsanity.com/paper/2309.00639/) (2023)
- [AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System](https://mlsanity.com/paper/2310.19834/) (2023)

## Abstract (arXiv)

In this work, we collect a moderate-sized representative corpus of tweets (200,000 approx.) pertaining Covid-19 vaccination spanning over a period of seven months (September 2020 - March 2021). Following a Transfer Learning approach, we utilize the pre-trained Transformer-based XLNet model to classify tweets as Misleading or Non-Misleading and validate against a random subset of results manually. We build on this to study and contrast the characteristics of tweets in the corpus that are misleading in nature against non-misleading ones. This exploratory analysis enables us to design features (such as sentiments, hashtags, nouns, pronouns, etc) that can, in turn, be exploited for classifying tweets as (Non-)Misleading using various ML models in an explainable manner. Specifically, several ML models are employed for prediction, with up to 90% accuracy, and the importance of each feature is explained using SHAP Explainable AI (XAI) tool. While the thrust of this work is principally exploratory analysis in order to obtain insights on the online discourse on Covid-19 vaccination, we conclude the paper by outlining how these insights provide the foundations for a more actionable approach to mitigate misinformation. The curated dataset and code is made available (Github repository) so that the research community at large can reproduce, compare against, or build upon this work.