research-article

Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?

Authors:

Md Main Uddin Rony,

Naeemul Hassan,

Mohammad YousufAuthors Info & Claims

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

Pages 232 - 239

https://doi.org/10.1145/3110025.3110054

Published: 31 July 2017 Publication History

Abstract

The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started following this practice. Although the wide-spread practice of clickbait makes the reader's reliability on media vulnerable, a large scale analysis to reveal this fact is still absent. In this paper, we analyze 1.67 million Facebook posts created by 153 media organizations to understand the extent of clickbait practice, its impact and user engagement by using our own developed clickbait detection model. The model uses distributed sub-word embeddings learned from a large corpus. The accuracy of the model is 98.3%. Powered with this model, we further study the distribution of topics in clickbait and non-clickbait contents.

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Cited By

Özsalih A(2024)Sosyal Medyadaki Haberlerin Başlıklarındaki Duygusal Kelimelerin Haber Tüketimine EtkileriSelçuk İletişim10.18094/josc.137724117:1(39-68)Online publication date: 15-Apr-2024
https://doi.org/10.18094/josc.1377241
Adrian FHandradika NPrasojo AGunawan ASetiawan K(2024)Clickbait Detection on Online News Headlines Using Naive Bayes and LSTM2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)10.1109/AIMS61812.2024.10512986(1-6)Online publication date: 21-Feb-2024
https://doi.org/10.1109/AIMS61812.2024.10512986
Bajaj AVishwakarma D(2024)Non-Alpha-Num: a novel architecture for generating adversarial examples for bypassing NLP-based clickbait detection mechanismsInternational Journal of Information Security10.1007/s10207-024-00861-923:4(2711-2737)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10207-024-00861-9
Show More Cited By

Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information retrieval
  2. Information systems applications

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cover image ACM Conferences

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 31 July 2017

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ASONAM '17

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ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

Sydney, Australia

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

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https://doi.org/10.18094/josc.1377241
Adrian FHandradika NPrasojo AGunawan ASetiawan K(2024)Clickbait Detection on Online News Headlines Using Naive Bayes and LSTM2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)10.1109/AIMS61812.2024.10512986(1-6)Online publication date: 21-Feb-2024
https://doi.org/10.1109/AIMS61812.2024.10512986
Bajaj AVishwakarma D(2024)Non-Alpha-Num: a novel architecture for generating adversarial examples for bypassing NLP-based clickbait detection mechanismsInternational Journal of Information Security10.1007/s10207-024-00861-923:4(2711-2737)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10207-024-00861-9
Calderon PRizoiu M(2024)What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture HawkesMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70362-1_9(142-160)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70362-1_9
Kemm R(2023)The Linguistic and Typological Features of Clickbait in Youtube Video TitlesSocial Communication10.2478/sc-2022-00078:1(66-80)Online publication date: 20-Jan-2023
https://doi.org/10.2478/sc-2022-0007
Maleki NPadmanabhan BDutta K(2023)The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment AnalysisJournal of Medical Internet Research10.2196/4430725(e44307)Online publication date: 11-May-2023
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KÜÇÜKVARDAR M(2023)Tık Odaklı Habercilik Çerçevesinde Ekonomi Haberlerinin İncelenmesiExamination of Economic News in the Framework of Clickbait JournalismTürkiye İletişim Araştırmaları Dergisi10.17829/turcom.1194831(145-168)Online publication date: 1-Jun-2023
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Chou YLee HTsai JLiang EChang Y(2023)I Like Their Autonomy and Closeness to Me: Uncovering the Perceived Appeal of Social-Media InfluencersProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580898(1-19)Online publication date: 19-Apr-2023
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Yadav KBansal N(2023)A Comparative Study on Clickbait Detection using Machine Learning Based Methods2023 International Conference on Disruptive Technologies (ICDT)10.1109/ICDT57929.2023.10150475(661-665)Online publication date: 11-May-2023
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Rahman SDas ASharif OHoque M(2023)Identification of Deceptive Clickbait Youtube Videos Using Multimodal FeaturesIntelligent Computing and Optimization10.1007/978-3-031-50327-6_21(199-208)Online publication date: 16-Dec-2023
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