# Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-case

arXiv: https://arxiv.org/abs/2012.07517 · HTML: https://mlsanity.com/paper/2012.07517/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English](https://mlsanity.com/paper/2101.02359/) (2021)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset](https://mlsanity.com/paper/2103.01196/) (2021)
- [NLP Techniques for Water Quality Analysis in Social Media Content](https://mlsanity.com/paper/2112.11441/) (2021)
- [Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives](https://mlsanity.com/paper/2107.09768/) (2021)
- [From Fake to Hyperpartisan News Detection Using Domain Adaptation](https://mlsanity.com/paper/2308.02185/) (2023)
- [A Roadmap for Multilingual, Multimodal Domain Independent Deception Detection](https://mlsanity.com/paper/2405.03920/) (2024)
- [NDGGNET-A Node Independent Gate based Graph Neural Networks](https://mlsanity.com/paper/2205.05348/) (2022)
- [Enriching GNNs with Text Contextual Representations for Detecting Disinformation Campaigns on Social Media](https://mlsanity.com/paper/2410.19193/) (2024)
- [Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions](https://mlsanity.com/paper/2412.03600/) (2024)
- [DISHONEST: Dissecting misInformation Spread using Homogeneous sOcial NEtworks and Semantic Topic classification](https://mlsanity.com/paper/2412.09578/) (2024)

## Abstract (arXiv)

The paper presents our solutions for the MediaEval 2020 task namely FakeNews: Corona Virus and 5G Conspiracy Multimedia Twitter-Data-Based Analysis. The task aims to analyze tweets related to COVID-19 and 5G conspiracy theories to detect misinformation spreaders. The task is composed of two sub-tasks namely (i) text-based, and (ii) structure-based fake news detection. For the first task, we propose six different solutions relying on Bag of Words (BoW) and BERT embedding. Three of the methods aim at binary classification task by differentiating in 5G conspiracy and the rest of the COVID-19 related tweets while the rest of them treat the task as ternary classification problem. In the ternary classification task, our BoW and BERT based methods obtained an F1-score of .606% and .566% on the development set, respectively. On the binary classification, the BoW and BERT based solutions obtained an average F1-score of .666% and .693%, respectively. On the other hand, for structure-based fake news detection, we rely on Graph Neural Networks (GNNs) achieving an average ROC of .95% on the development set.