published in Proceedings of the 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1-6, DOI: 10.23919/SOFTCOM.2018.8555759, September 13-15, 2018, Split – Supetar (Island of Brac), Croatia
Cite as
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D. Bufnea and D. Şotropa, "A Community Driven Approach for click Bait Reporting", 2018 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2018, pp. 1-6, doi: 10.23919/SOFTCOM.2018.8555759 |
Full paper
A Community Driven Approach for Click Bait Reporting
Authors
Darius Bufnea, Diana Șotropa
Department of Computer Science, Faculty of Mathematics and Computer Science,
Babeș-Bolyai University of Cluj-Napoca
Copyright
© 2018 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
Click baits are primarily used by online content publishers. Their purpose is to allure readers to click on a link and subsequently visit other articles by the same publisher, in order to increase page views and ad revenue. Most of the time click baits are used for pointing to low quality articles or thin content. The user falls into the publishers’ trap due to a misleading or incomplete title or content exaggeration. A bait article link might also appear on social network shares or within the search engines result page, the presence of such a link in 3rd party web sites having a negative impact on user experience. Hence, it is important to properly identify and report them.
In this paper we present an academic research browser extension meant to be used in the click bait reporting process. The extension offers to users the possibility to explicitly report a click bait, a series of details about the bait link being extracted and logged for further analysis. Based on all the gathered data, our goal is to obtain a community driven click bait samples database that may subsequently be used as an input for different supervised learning algorithms for click bait detection.
Key words
click bait; information retrieval; web user behavior; community driven database; fake news; SERP results; academic research plug-in.
BibTeX bib file
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@inproceedings{bufnea2018, title={A Community Driven Approach for Click Bait Reporting}, author={Bufnea, Darius and \c{S}otropa, Diana}, booktitle={Proceedings of 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), September 13-15, 2018, Split - Supetar (Island of Brac), Croatia}, year={2018} } |
References
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