# Model Generalization on COVID-19 Fake News Detection

arXiv: https://arxiv.org/abs/2101.03841 · HTML: https://mlsanity.com/paper/2101.03841/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 11 Jan 2021.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019)
- [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2012.04373/) (2020)
- [Curriculum Loss: Robust Learning and Generalization against Label Corruption](https://mlsanity.com/paper/1905.10045/) (2019)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [Data Cleansing for Models Trained with SGD](https://mlsanity.com/paper/1906.08473/) (2019)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms](https://mlsanity.com/paper/2007.07996/) (2020)
- [Part-dependent Label Noise: Towards Instance-dependent Label Noise](https://mlsanity.com/paper/2006.07836/) (2020)
- [Does Distributionally Robust Supervised Learning Give Robust Classifiers?](https://mlsanity.com/paper/1611.02041/) (2016)
- [The Adapter-Bot: All-In-One Controllable Conversational Model](https://mlsanity.com/paper/2008.12579/) (2020)
- [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020)
- [Learning Fast Adaptation on Cross-Accented Speech Recognition](https://mlsanity.com/paper/2003.01901/) (2020)
- [Meta-Transfer Learning for Code-Switched Speech Recognition](https://mlsanity.com/paper/2004.14228/) (2020)
- [Zero-Resource Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2002.05923/) (2020)
- [Misinformation Has High Perplexity](https://mlsanity.com/paper/2006.04666/) (2020)
- [A first look at COVID-19 information and misinformation sharing on Twitter](https://mlsanity.com/paper/2003.13907/) (2020)
- [No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection](https://mlsanity.com/paper/2010.06906/) (2020)
- [Efficient Estimation of Influence of a Training Instance](https://mlsanity.com/paper/2012.04207/) (2020)
- [Anatomy of an online misinformation network](https://mlsanity.com/paper/1801.06122/) (2018)
- [Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions](https://mlsanity.com/paper/2005.08820/) (2020)
- [CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management](https://mlsanity.com/paper/2005.03975/) (2020)

## Built on by (most cited first, 11 of 11)

- [Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer](https://mlsanity.com/paper/2209.08902/) (2022)
- [A Holistic Framework for Analyzing the COVID-19 Vaccine Debate](https://mlsanity.com/paper/2205.01817/) (2022)
- [Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions](https://mlsanity.com/paper/2111.04415/) (2021)
- [A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles](https://mlsanity.com/paper/2104.01791/) (2021)
- [Greenformers: Improving Computation and Memory Efficiency in Transformer Models via Low-Rank Approximation](https://mlsanity.com/paper/2108.10808/) (2021)
- [IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages](https://mlsanity.com/paper/2311.12405/) (2023)
- [Dynamically Addressing Unseen Rumor via Continual Learning](https://mlsanity.com/paper/2104.08775/) (2021)
- [Towards Answering Open-ended Ethical Quandary Questions](https://mlsanity.com/paper/2205.05989/) (2022)
- [Evaluating Generalizability of Fine-Tuned Models for Fake News Detection](https://mlsanity.com/paper/2205.07154/) (2022)
- [Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset](https://mlsanity.com/paper/2103.01196/) (2021)
- [A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models](https://mlsanity.com/paper/2208.01355/) (2022)

## Abstract (arXiv)

Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information. Considering the problematic consequences that the COVID-19 fake-news have brought, the scientific community has put effort to tackle it. To contribute to this fight against the infodemic, we aim to achieve a robust model for the COVID-19 fake-news detection task proposed at CONSTRAINT 2021 (FakeNews-19) by taking two separate approaches: 1) fine-tuning transformers based language models with robust loss functions and 2) removing harmful training instances through influence calculation. We further evaluate the robustness of our models by evaluating on different COVID-19 misinformation test set (Tweets-19) to understand model generalization ability. With the first approach, we achieve 98.13% for weighted F1 score (W-F1) for the shared task, whereas 38.18% W-F1 on the Tweets-19 highest. On the contrary, by performing influence data cleansing, our model with 99% cleansing percentage can achieve 54.33% W-F1 score on Tweets-19 with a trade-off. By evaluating our models on two COVID-19 fake-news test sets, we suggest the importance of model generalization ability in this task to step forward to tackle the COVID-19 fake-news problem in online social media platforms.