# The COVID-19 Social Media Infodemic

arXiv: https://arxiv.org/abs/2003.05004 · HTML: https://mlsanity.com/paper/2003.05004/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 30 of 64)

- [Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative](https://mlsanity.com/paper/2502.08942/) (2025)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [The simulation of judgment in LLMs](https://mlsanity.com/paper/2502.04426/) (2025)
- [Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection](https://mlsanity.com/paper/2109.12986/) (2021)
- [Model Generalization on COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03841/) (2021)
- [Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection](https://mlsanity.com/paper/2111.07819/) (2021)
- [Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications](https://mlsanity.com/paper/2206.14358/) (2022)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [Demystifying Misconceptions in Social Bots Research](https://mlsanity.com/paper/2303.17251/) (2023)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning](https://mlsanity.com/paper/2110.13710/) (2021)
- [MM-Claims: A Dataset for Multimodal Claim Detection in Social Media](https://mlsanity.com/paper/2205.01989/) (2022)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [Generative AI collective behavior needs an interactionist paradigm](https://mlsanity.com/paper/2601.10567/) (2026)
- [Cognitive network science for understanding online social cognitions: A brief review](https://mlsanity.com/paper/2102.12799/) (2021)
- [A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar](https://mlsanity.com/paper/2109.11372/) (2021)
- [Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron](https://mlsanity.com/paper/2303.00135/) (2023)
- [Rhythm of Opinion: A Hawkes-Graph Framework for Dynamic Propagation Analysis](https://mlsanity.com/paper/2504.15072/) (2025)
- [Online Hate: Behavioural Dynamics and Relationship with Misinformation](https://mlsanity.com/paper/2105.14005/) (2021)
- [Mining Adverse Drug Reactions from Unstructured Mediums at Scale](https://mlsanity.com/paper/2201.01405/) (2022)
- [ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination](https://mlsanity.com/paper/2201.06496/) (2022)
- [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)
- [Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling](https://mlsanity.com/paper/2310.07078/) (2023)
- [Which linguistic cues make people fall for fake news? A comparison of cognitive and affective processing](https://mlsanity.com/paper/2312.03751/) (2023)
- [Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation](https://mlsanity.com/paper/2507.07307/) (2025)
- [How Pandemic Spread in News: Text Analysis Using Topic Model](https://mlsanity.com/paper/2102.04205/) (2021)
- [How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings](https://mlsanity.com/paper/2102.07836/) (2021)
- [The Arc of the Data Scientific Universe](https://mlsanity.com/paper/2102.10050/) (2021)
- [QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Media](https://mlsanity.com/paper/2204.03506/) (2022)
- [Towards a Unified Framework for Adaptable Problematic Content Detection via Continual Learning](https://mlsanity.com/paper/2309.16905/) (2023)

## Abstract (arXiv)

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction numbers R0 for each social media platform. Moreover, we characterize information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.