Detecting Clickbait in Online Social Media: You Won’t Believe How We Did It

Aviad Elyashar, Jorge Bendahan, Rami Puzis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a machine learning approach to detect clickbait posts published in social media. Clickbait posts are short, catchy phrases pointing into a longer online article. Users are encouraged to click on these posts to read the full article in many cases. The suggested approach differentiates between clickbait and legitimate posts based on training mainstream machine learning (ML) classifiers. The suggested classifiers are trained in various features extracted from images, linguistic, and behavioral analysis. For evaluation, we used two datasets provided by Clickbait Challenge 2017. The XGBoost classifier obtained the best performance with an AUC of 0.8, an accuracy of 0.812, a precision of 0.819, and a recall of 0.966. Finally, we found that counting the number of formal English words in the given content is helpful for clickbait detection.

Original languageEnglish
Title of host publicationCyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
EditorsShlomi Dolev, Amnon Meisels, Jonathan Katz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages377-387
Number of pages11
ISBN (Print)9783031076886
DOIs
StatePublished - 1 Jan 2022
Event6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 - Beer Sheva, Israel
Duration: 30 Jun 20221 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13301 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
Country/TerritoryIsrael
CityBeer Sheva
Period30/06/221/07/22

Keywords

  • Clickbait detection
  • Machine learning
  • Social media

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

Fingerprint

Dive into the research topics of 'Detecting Clickbait in Online Social Media: You Won’t Believe How We Did It'. Together they form a unique fingerprint.

Cite this