Context-based Clickbait identification using Deep Learning

Thakur, Deepal Santosh; Kurhade, Swapnali

doi:10.1109/iccict50803.2021.9510141

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The result of this framework's architecture is 0.97 of accuracy. Thakur does other research about clickbait detection by using deep learning [8], [9] which suggests the recurrent CNN overcomes the heavy feature engineering in clickbait detection. This method has been tested and turns out that the accuracy is better than the other clickbait-detection algorithms such as LSTM [10]- [12], CNN [8], [13], [14], BERT [15], [16], or conventional machine learning algorithms [17]- [21].…”

Section: Recent Workmentioning

confidence: 99%

Identifying clickbait in online news using deep learning

2023

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Several industries use clickbait techniques as their strategy to increase the number of readers for their news. Some news companies implement catchy headlines and images in their news article links, with the expectation that the readers will be interested in reading the news and click the provided link. The majority of the news is not hoax news. However, the content might not be as grand as the catchy headlines and images provided to the readers. This research aims to explore the classification model using machine learning to identify if the headlines are classified as clickbait in online news. This research explores several machine learning techniques to classify clickbait in online news and comprehensively explain the results. Several popular machine learning techniques were implemented and explored in this research. The results demonstrate that the model trained with fast large margin provides the best accuracy and classification error (90% and 10%, respectively). Moreover, to improve the performance, bidirectional encoder representations from transformers architecture was used to model clickbait in online news. The best BERT model achieved 98.86% in the test accuracy. BERT model requires more time to train (0.9 hour) compared to machine learning (0.4 hour).

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Section: Recent Workmentioning

confidence: 99%

Identifying clickbait in online news using deep learning

2023

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“…It is the number of visits that will determine the amount of advertising revenue. That is why the survival of media companies today is highly dependent on the number of visits (Thakur & Kurhade, 2021).…”

Section: Introductionmentioning

confidence: 99%

Metajournalistic Discourse on the Commercialization of News: Resistance toward Journalistic Autonomy

Krisdinanto

2024

JKJ

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The digital evolution in the journalistic arena forces journalists to adapt their practices to the rules that come from the economic arena associated with commercialization. Reduced advertising revenue has resulted in the media tending to prioritize financial survival over journalistic principles or ethics. This research aims to describe how journalists struggle to define journalistic practices amidst the pressure of commercialization, using Bourdieu's theoretical perspective. The method used is metajournalistic discourse, using metajournalistic documents in the form of scripts written by digital journalists who broadcast on Remotivi. Metajournalistic discourse is a textual analysis method that focuses on how journalists tell stories about their journalistic practices, and these stories are assumed to shape the journalistic arena they inhabit. The findings show that journalists experience a paradox in defining and carrying out journalistic practices that are considered ideal. On the one hand, journalists appear to carry out journalistic practices in a situation with a high degree of heteronomy. On the other hand, journalists also seek to develop resistance by maintaining or developing definitions of journalistic practices or norms that are close to the autonomy pole, for example, by defining their journalistic practice with the term "journalistic jihad.” These idealized journalistic definitions or norms (which prioritize verification, accuracy, and adherence to journalistic firewalls) are shared and circulated through discourse texts to become resistance narratives.

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“…People who create these baits usually take advantage of arousing the curiosity of the viewers, but do not provide enough information to get the viewers completely satisfied. In this way, they can increase their income by audiences' clicking on hyperlinks for more content [1].…”

Section: The Introduction To Clickbaitmentioning

confidence: 99%

The Analysis of Click Bait in We Media Era

Zhou¹

2022

Advances in Social Science, Education and Humanities Research

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In this era of information overload, all kinds of information come into our vision in different forms. Among them, click bait has been developed into a new advertising method at a remarkable speed. This paper briefly introduces the click bait in the form of "Yellow Journal" for the first time in history. Although the click bait can improve brand awareness and bring profits to businesses, the click bait is also consuming people's trust in the brand at the same time, and creative ideas will be exhausted one day. Based on the reasons above, the author analyzes the click bait and puts forward some suggestions on how to identify and avoid them. Through this study, the author finds that the clickbait has deeply penetrated into people's lives, so people should timely identify all kinds of clickbait to avoid potential scam.

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Context-based Clickbait identification using Deep Learning

Cited by 4 publications

References 8 publications

Identifying clickbait in online news using deep learning

Identifying clickbait in online news using deep learning

Metajournalistic Discourse on the Commercialization of News: Resistance toward Journalistic Autonomy

The Analysis of Click Bait in We Media Era

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