# Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social Media

arXiv: https://arxiv.org/abs/2101.03291 · HTML: https://mlsanity.com/paper/2101.03291/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 9 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [Hostility Detection Dataset in Hindi](https://mlsanity.com/paper/2011.03588/) (2020)
- [Detecting Hate Speech and Offensive Language on Twitter using Machine Learning: An N-gram and TFIDF based Approach](https://mlsanity.com/paper/1809.08651/) (2018)

## Built on by (most cited first, 6 of 6)

- [NLP-CUET@DravidianLangTech-EACL2021: Offensive Language Detection from Multilingual Code-Mixed Text using Transformers](https://mlsanity.com/paper/2103.00455/) (2021)
- [NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner](https://mlsanity.com/paper/2103.00464/) (2021)
- [A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles](https://mlsanity.com/paper/2104.01791/) (2021)
- [NLP-CUET@DravidianLangTech-EACL2021: Investigating Visual and Textual Features to Identify Trolls from Multimodal Social Media Memes](https://mlsanity.com/paper/2103.00466/) (2021)
- [Two-Stage Classifier for COVID-19 Misinformation Detection Using BERT: a Study on Indonesian Tweets](https://mlsanity.com/paper/2206.15359/) (2022)
- [A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation](https://mlsanity.com/paper/2609.00403/) (2026)

## Abstract (arXiv)

This paper illustrates a detail description of the system and its results that developed as a part of the participation at CONSTRAINT shared task in AAAI-2021. The shared task comprises two tasks: a) COVID19 fake news detection in English b) Hostile post detection in Hindi. Task-A is a binary classification problem with fake and real class, while task-B is a multi-label multi-class classification task with five hostile classes (i.e. defame, fake, hate, offense, non-hostile). Various techniques are used to perform the classification task, including SVM, CNN, BiLSTM, and CNN+BiLSTM with tf-idf and Word2Vec embedding techniques. Results indicate that SVM with tf-idf features achieved the highest 94.39% weighted f1 score on the test set in task-A. Label powerset SVM with n-gram features obtained the maximum coarse-grained and fine-grained f1 score of 86.03% and 50.98% on the task-B test set respectively.