# Social Networks Analysis to Retrieve Critical Comments on Online Platforms

arXiv: https://arxiv.org/abs/2102.10495 · HTML: https://mlsanity.com/paper/2102.10495/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 21 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Method for Commonsense Reasoning](https://mlsanity.com/paper/1806.02847/) (2018)
- [Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter](https://mlsanity.com/paper/1707.03778/) (2017)
- [Is this pofma? Analysing public opinion and misinformation in a COVID-19 Telegram group chat](https://mlsanity.com/paper/2010.10113/) (2020)

## Abstract (arXiv)

Social networks are rich source of data to analyze user habits in all aspects of life. User's behavior is decisive component of a health system in various countries. Promoting good behavior can improve the public health significantly. In this work, we develop a new model for social network analysis by using text analysis approach. We define each user reaction to global pandemic with analyzing his online behavior. Clustering a group of online users with similar habits, help to find how virus spread in different societies. Promoting the healthy life style in the high risk online users of social media have significant effect on public health and reducing the effect of global pandemic. In this work, we introduce a new approach to clustering habits based on user activities on social media in the time of pandemic and recommend a machine learning model to promote health in the online platforms.