Detecting "Clickbait" News on Social Media Using Machine Learning Algorithms

Date

2019-01-01

Author

Genc, Sura
Sürer, Elif

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

290
views

0
downloads

Clickbait, which has become very common in social media in recent years, is a technique which uses exaggerated and unreal headlines in order to manipulate people and attract them to their websites. Since the content mentioned in the title is not presented in the main text or the content of text is low-quality, clicked on links often disappoint people. In this study, we attempt to detect clickbaits in Turkish news using Twitter posts. For this purpose, headlines of news were collected from Twitter accounts of Limon Haber(1) and Spoiler Haber(2) for clickbait data and from Twitter accounts of Evrensel Newspaper(3) and Diken Newspaper(4) for non-clickbait data. Experimental results on news headlines show that using an artificial neural network, our model performs for clickbait detection with an accuracy of 0.91 with an F1-score of 0.91-which is the highest score in Turkish data sets.

Subject Keywords

Cickbait detection, Neural networks, Social media, News headlines

URI

https://hdl.handle.net/11511/30138

DOI

https://doi.org/10.1109/siu.2019.8806257

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Turkish clickbait detection in social media via machine learning algorithms Genç, Şura; Sürer, Elif; Çakır, Murat Perit; Department of Cognitive Sciences (2021-8-26) Clickbait strategy, mostly used in headlines and teaser messages, aims to attract people’s attention, and make them click on the link by using intriguing expressions with various text-related features. Clickbait, which has become very common especially in social media in recent years, is a major problem for the flow of information. Since the information promised in the clickbait headline is generally not included in the main text, clickbait headlines disappoint readers and is problematic for ethics of journ...
Event Detection via Tracking the Change in Community Structure and Communication Trends Aktunc, Riza; Karagöz, Pınar; Toroslu, Ismail Hakki (2022-01-01) Event detection is a popular research problem aiming to detect events from various data sources, such as news texts, social media postings or social interaction patterns. In this work, event detection is studied on social interaction and communication data via tracking changes in community structure and communication trends. With this aim, various community structure and communication trend based event detection methods are proposed. Additionally, a new strategy called community size range based change trac...
Developing a Twitter bot that can join a discussion using state-of-the-art architectures Çetinkaya, Yusuf Mucahit; Toroslu, İsmail Hakkı (Springer Science and Business Media LLC, 2020-07-01) Today, microblogging platforms like Twitter have become popular by spreading news and opinions that gather attention. Engaging interactions, such as likes, shares, and replies, between users are the key determinants of these platforms' news feed prioritization algorithms. These interactions attract people to ongoing debates and help inform and shape their opinions. Since being influential and attracting followers in these debates are considered as important, understanding the automation of these processes b...
Twitter use by politicians during social uprisings an analysis of Gezi park protests in Turkey KARKIN, NACİ; Yavuz, Nilay; PARLAK, İSMET; İKİZ, ÖZLEM ÖZDEŞİM (2015-06-30) Social uprisings clearly show that social media tools, especially Twitter, help news spread more than the press does recently. In some cases Twitter substitutes traditional media if censorship is enlarged to such a level that the mainstream media channels prefer not to reflect the actual volume of the protests. Twitter is also utilized by politicians during such events to reinforce "us vs. them" division, and to gain support and legitimization for their own actions. Using critical discourse analysis, this p...
Detecting social cliques for automated privacy control in online social networks Yıldız, Hakan (2012-06-01) As a result of the increasing popularity of online social networking sites, millions of people spend a considerable portion of their social life on the Internet. The information exchanged in this context has obvious privacy risks. Interestingly, concerns of social network users about these risks are related not only to adversarial activities but also to users they are directly connected to (friends). In particular, many users want to occasionally hide portions of their information from certain groups of the...

Citation Formats

S. Genc and E. Sürer, “Detecting “Clickbait” News on Social Media Using Machine Learning Algorithms,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30138.