# Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection

arXiv: https://arxiv.org/abs/2102.08924 · HTML: https://mlsanity.com/paper/2102.08924/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 79 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 24 of 24)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [Semi-supervised Sequence Learning](https://mlsanity.com/paper/1511.01432/) (2015)
- [MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification](https://mlsanity.com/paper/2004.12239/) (2020)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [Variational Pretraining for Semi-supervised Text Classification](https://mlsanity.com/paper/1906.02242/) (2019)
- [Hostility Detection Dataset in Hindi](https://mlsanity.com/paper/2011.03588/) (2020)
- [Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection](https://mlsanity.com/paper/1704.05973/) (2017)
- [Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function](https://mlsanity.com/paper/2009.04007/) (2020)
- [Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter](https://mlsanity.com/paper/2010.04377/) (2020)
- [GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information](https://mlsanity.com/paper/2005.11177/) (2020)
- [Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings](https://mlsanity.com/paper/1804.09088/) (2018)
- [TweetCred: Real-Time Credibility Assessment of Content on Twitter](https://mlsanity.com/paper/1405.5490/) (2014)
- [Variational Sequential Labelers for Semi-Supervised Learning](https://mlsanity.com/paper/1906.09535/) (2019)
- [Two-path Deep Semi-supervised Learning for Timely Fake News Detection](https://mlsanity.com/paper/2002.00763/) (2020)
- [Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak](https://mlsanity.com/paper/2004.14484/) (2020)
- [Multitask Learning for Blackmarket Tweet Detection](https://mlsanity.com/paper/1907.04072/) (2019)

## Built on by (most cited first, 15 of 15)

- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [PCoT: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformation](https://mlsanity.com/paper/2506.06842/) (2025)
- [A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention](https://mlsanity.com/paper/2407.19332/) (2024)
- [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)
- [The use of Data Augmentation as a technique for improving neural network accuracy in detecting fake news about COVID-19](https://mlsanity.com/paper/2205.00452/) (2022)
- [A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models](https://mlsanity.com/paper/2208.01355/) (2022)
- [LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection](https://mlsanity.com/paper/2402.08401/) (2024)
- [Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives](https://mlsanity.com/paper/2107.09768/) (2021)
- [Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake News](https://mlsanity.com/paper/2209.03162/) (2022)
- [Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts](https://mlsanity.com/paper/2402.05536/) (2024)
- [FakeZero: Real-Time, Privacy-Preserving Misinformation Detection for Facebook and X](https://mlsanity.com/paper/2510.25932/) (2025)
- [A Meta Path-based Approach for Rumor Detection on Social Media](https://mlsanity.com/paper/2301.04341/) (2023)
- [A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data](https://mlsanity.com/paper/2311.08001/) (2023)
- [CrediBench: Building Web-Scale Network Datasets for Information Integrity](https://mlsanity.com/paper/2509.23340/) (2025)
- [ExTax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies](https://mlsanity.com/paper/2605.27045/) (2026)

## Abstract (arXiv)

As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media. At the time when reliable information is vital for public health and safety, COVID-19 related fake news has been spreading even faster than the facts. During times such as the COVID-19 pandemic, fake news can not only cause intellectual confusion but can also place lives of people at risk. This calls for an immediate need to contain the spread of such misinformation on social media. We introduce CTF, the first COVID-19 Twitter fake news dataset with labeled genuine and fake tweets. Additionally, we propose Cross-SEAN, a cross-stitch based semi-supervised end-to-end neural attention model, which leverages the large amount of unlabelled data. Cross-SEAN partially generalises to emerging fake news as it learns from relevant external knowledge. We compare Cross-SEAN with seven state-of-the-art fake news detection methods. We observe that it achieves 0.95 F1 Score on CTF, outperforming the best baseline by 9%. We also develop Chrome-SEAN, a Cross-SEAN based chrome extension for real-time detection of fake tweets.