Welcome to BaitBuster!

A Clickbait Solution Framework: Destined to save you some clicks

Got A Headline to Check? Try Our Public API

Want to Access Our API?

You need to send a POST request through this URL: https://dear.cs.olemiss.edu/baitbuster/api/cb_detection. Parameters must be submitted in the POST body.


title: string required
The headline of the article you want to check. It's a required parameter.

url: string
Link to the article you checked. It's not a required field. But to get the explanation behind clickbait decision, you need to provide the URL.

source_key: string required
It's a required field. You need to use the key "public_access".


confidence: string
The confidence behind the decision ranging from 0.0 to 1.0

decision: string
The clickbait decision. Shows "clickbait" for clickbait article and "non-clickbait" for the non-clickbait articles

matched_ngram: array of string present if the article is clickbait
List of matched n-grams(n=3) used in most clickbait headlines

similarity: text present if the article is clickbait
Presents a comparison of the similarity between the summary of the content and the corresponding headline with respect to an average non-clickbait article.

summary: text present if the article is clickbait
Presents the summary of the content

Most Viewed Clickbait Articles in Facebook By Baitbuster

Why BaitBuster?

The use of tempting headlines (clickbait) to allure readers has become a growing practice nowadays. The widespread use of clickbait risks the reader’s trust in media.......

System Overview

BaitBuster framework following the client-server system architecture model. The browser extension monitors a user’s Facebook news feed and alerts her if a post (link) contains a ......

Browser Extension

Powered by a highly accurate machine learning based clickbait classification model, BaitBuster aims to improve the web surfing experience of general users. Currently, this browser ......

Social Networking Bot

The social bot monitors which clickbait headlines are trending and publishes a brief report about them on a Facebook page through API. The goal of the bot is to discourage users from ......


  • Md Main Uddin Rony, Naeemul Hassan, Mohammad Yousuf. BaitBuster: A Clickbait Identification Framework. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), New orleans, Louisiana, USA, February, 2018. [Paper]
  • Md Main Uddin Rony, Naeemul Hassan, Mohammad Yousuf. BaitBuster: Destined to Save You Some Clicks. In Proceedings of the 2017 Computation+Journalism Symposium (C+J2017), Illinois, October, 2017. [Paper]
  • Md Main Uddin Rony, Naeemul Hassan, Mohammad Yousuf. Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?. In Proceedings of 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2017), Sydney, July, 2017. [Paper]

Contact Details


216 Weir Hall,

University of Mississippi

University, Mississippi 38677

Phone: (662) 915-5357

Email: nhassan@olemiss.edu

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Web Design: Mohammad Yousuf & Md Main Uddin