@inproceedings{f41dc8f658464abda1df097f6145839c,
title = "Detecting Clickbait in Online Social Media: You Won{\textquoteright}t Believe How We Did It",
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.",
keywords = "Clickbait detection, Machine learning, Social media",
author = "Aviad Elyashar and Jorge Bendahan and Rami Puzis",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 ; Conference date: 30-06-2022 Through 01-07-2022",
year = "2022",
month = jan,
day = "1",
doi = "10.1007/978-3-031-07689-3\_28",
language = "English",
isbn = "9783031076886",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "377--387",
editor = "Shlomi Dolev and Amnon Meisels and Jonathan Katz",
booktitle = "Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings",
address = "Germany",
}