A New Language Independent Strategy for Clickbait Detection

published in Proceedings of the 28^th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1-6, DOI: 10.23919/SoftCOM50211.2020.9238342, September 17-19, 2020, Hvar, Croatia.

Cite as

Full paper

A New Language Independent Strategy for Clickbait Detection

Authors

Claudia Ioana Coste, Darius Bufnea, Virginia Niculescu
Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University of Cluj-Napoca, Romania

Copyright

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Abstract

Clickbait is a bad habit of today’s web publishers, which resort to such a technique in order to deceive web visitors and increase publishers’ page views and advertising revenue. Clickbait incidence is also an indicator for fake news and so, clickbait detection represents a mean in the fight against spreading false information. Recently, both the research community and the big actors on the WWW scene such as social networks and search engines, turn their attention towards this negative phenomenon that is more and more present in our everyday browsing experience. The detection techniques are usually based on intelligent classifiers, features selection being also of great importance. This paper aims to bring its own contributions in clickbait analysis and detection by presenting a new language independent strategy for clickbait detection that considers only general features that are non language specific. This approach is justified by the need for a higher level of abstractization in the clickbait detection, allowing its usability for articles written in different languages. A complex experiment on a real sample dataset was conducted and the obtained results are compared with the most relevant previous work results.

Key words

clickbait detection; features; intelligent classifier; natural language; accuracy.

BibTeX bib file

softcom2020-clickbait.bib

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