# Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives

arXiv: https://arxiv.org/abs/2107.09768 · HTML: https://mlsanity.com/paper/2107.09768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 25 of 25)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020)
- [ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research](https://mlsanity.com/paper/2006.05557/) (2020)
- [MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation](https://mlsanity.com/paper/2011.04088/) (2020)
- [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020)
- [Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset](https://mlsanity.com/paper/2008.00791/) (2020)
- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [Evaluating Deep Learning Approaches for Covid19 Fake News Detection](https://mlsanity.com/paper/2101.04012/) (2021)
- [ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection](https://mlsanity.com/paper/2010.08768/) (2020)
- [Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection](https://mlsanity.com/paper/2102.08924/) (2021)
- [g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection](https://mlsanity.com/paper/2012.11967/) (2020)
- [Transformer based Automatic COVID-19 Fake News Detection System](https://mlsanity.com/paper/2101.00180/) (2021)
- [No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection](https://mlsanity.com/paper/2010.06906/) (2020)
- [Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-case](https://mlsanity.com/paper/2012.07517/) (2020)
- [CHECKED: Chinese COVID-19 Fake News Dataset](https://mlsanity.com/paper/2010.09029/) (2020)
- [A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03545/) (2021)
- [Jointly Embedding Entities and Text with Distant Supervision](https://mlsanity.com/paper/1807.03399/) (2018)
- [Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak](https://mlsanity.com/paper/2004.14484/) (2020)
- [Constraint 2021: Machine Learning Models for COVID-19 Fake News Detection Shared Task](https://mlsanity.com/paper/2101.03717/) (2021)
- [Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets](https://mlsanity.com/paper/2104.05321/) (2021)

## Built on by (most cited first, 1 of 1)

- [Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation](https://mlsanity.com/paper/2210.13865/) (2022)

## Abstract (arXiv)

During the COVID-19 pandemic, social media platforms were ideal for communicating due to social isolation and quarantine. Also, it was the primary source of misinformation dissemination on a large scale, referred to as the infodemic. Therefore, automatic debunking misinformation is a crucial problem. To tackle this problem, we present two COVID-19 related misinformation datasets on Twitter and propose a misinformation detection system comprising network-based and content-based processes based on machine learning algorithms and NLP techniques. In the network-based process, we focus on social properties, network characteristics, and users. On the other hand, we classify misinformation using the content of the tweets directly in the content-based process, which contains text classification models (paragraph-level and sentence-level) and similarity models. The evaluation results on the network-based process show the best results for the artificial neural network model with an F1 score of 88.68%. In the content-based process, our novel similarity models, which obtained an F1 score of 90.26%, show an improvement in the misinformation classification results compared to the network-based models. In addition, in the text classification models, the best result was achieved using the stacking ensemble-learning model by obtaining an F1 score of 95.18%. Furthermore, we test our content-based models on the Constraint@AAAI2021 dataset, and by getting an F1 score of 94.38%, we improve the baseline results. Finally, we develop a fact-checking website called Checkovid that uses each process to detect misinformative and informative claims in the domain of COVID-19 from different perspectives.