# Hostility Detection and Covid-19 Fake News Detection in Social Media

arXiv: https://arxiv.org/abs/2101.05953 · HTML: https://mlsanity.com/paper/2101.05953/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 15 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [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)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [How Language-Neutral is Multilingual BERT?](https://mlsanity.com/paper/1911.03310/) (2019)
- [Language Models as Fact Checkers?](https://mlsanity.com/paper/2006.04102/) (2020)
- [Hostility Detection Dataset in Hindi](https://mlsanity.com/paper/2011.03588/) (2020)
- [A simple but tough-to-beat baseline for the Fake News Challenge stance detection task](https://mlsanity.com/paper/1707.03264/) (2017)
- [Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking](https://mlsanity.com/paper/2011.13253/) (2020)

## Built on by (most cited first, 2 of 2)

- [SEMI-FND: Stacked Ensemble Based Multimodal Inference For Faster Fake News Detection](https://mlsanity.com/paper/2205.08159/) (2022)
- [COVID-19 Fake News Detection Using Bidirectional Encoder Representations from Transformers Based Models](https://mlsanity.com/paper/2109.14816/) (2021)

## Abstract (arXiv)

Withtheadventofsocialmedia,therehasbeenanextremely rapid increase in the content shared online. Consequently, the propagation of fake news and hostile messages on social media platforms has also skyrocketed. In this paper, we address the problem of detecting hostile and fake content in the Devanagari (Hindi) script as a multi-class, multi-label problem. Using NLP techniques, we build a model that makes use of an abusive language detector coupled with features extracted via Hindi BERT and Hindi FastText models and metadata. Our model achieves a 0.97 F1 score on coarse grain evaluation on Hostility detection task. Additionally, we built models to identify fake news related to Covid-19 in English tweets. We leverage entity information extracted from the tweets along with textual representations learned from word embeddings and achieve a 0.93 F1 score on the English fake news detection task.