# Is it a click bait? Let's predict using Machine Learning

arXiv: https://arxiv.org/abs/2106.07348 · HTML: https://mlsanity.com/paper/2106.07348/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?](https://mlsanity.com/paper/1703.09400/) (2017)
- [We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!](https://mlsanity.com/paper/1612.01340/) (2016)
- [Tabloids in the Era of Social Media? Understanding the Production and Consumption of Clickbaits in Twitter](https://mlsanity.com/paper/1709.02957/) (2017)
- [Clickbait Detection in Tweets Using Self-attentive Network](https://mlsanity.com/paper/1710.05364/) (2017)
- [Using Neural Network for Identifying Clickbaits in Online News Media](https://mlsanity.com/paper/1806.07713/) (2018)
- [Detecting Clickbait in Online Social Media: You Won't Believe How We Did It](https://mlsanity.com/paper/1710.06699/) (2017)
- [Identifying Clickbait Posts on Social Media with an Ensemble of Linear Models](https://mlsanity.com/paper/1710.00399/) (2017)
- [Machine Learning Based Detection of Clickbait Posts in Social Media](https://mlsanity.com/paper/1710.01977/) (2017)
- [Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models](https://mlsanity.com/paper/1710.06390/) (2017)
- [Clickbait detection using word embeddings](https://mlsanity.com/paper/1710.02861/) (2017)
- [A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features](https://mlsanity.com/paper/1710.08528/) (2017)

## Abstract (arXiv)

In this era of digitisation, news reader tend to read news online. This is because, online media instantly provides access to a wide variety of content. Thus, people don't have to wait for tomorrow's newspaper to know what's happening today. Along with these virtues, online news have some vices as well. One such vice is presence of social media posts (tweets) relating to news articles whose sole purpose is to draw attention of the users rather than directing them to read the actual content. Such posts are referred to as clickbaits. The objective of this project is to develop a system which would be capable of predicting how likely are the social media posts (tweets) relating to new articles tend to be clickbait.