Turkish Clickbait News Detection using Explainable Artificial Intelligence

Alper Celal Akgün; Tülin İnkaya

EN

Turkish Clickbait News Detection using Explainable Artificial Intelligence

Abstract

Internet users frequently prefer digital journalism to acquire information. However, the content produced by malicious news sources leads to various issues for users. One of these issues is clickbait headlines, which are used to capture users' attention and direct them to specific content. Clickbait headlines exploit users' curiosity, causing them to navigate to targeted content and spend more time on it. Such content, which can be malicious, is one of the main problems for today's internet users. In the literature, artificial intelligence-based approaches using machine learning and deep learning models have been developed for the problem of clickbait detection. However, there is a need for studies on the explainability of artificial intelligence models developed in this field. Explainable artificial intelligence (XAI) aims to explain the transparency, understandability and decision-making processes of machine learning models. This study aims to develop explainable artificial intelligence-based models for the clickbait detection problem. In this context, a Turkish dataset compiled from different news sources was used. Initially, data preprocessing activities including feature engineering, missing data handling, stemming, normalization and term frequency-inverse document-frequency (TF-IDF) transformation were performed. Subsequently, k-nearest neighbors, Naive Bayes, logistic regression, decision tree, random forest, extreme gradient boosting (XGBoost), support vector machine and multi-layer perceptron (MLP) models were developed using the dataset. Hyperparameter optimization was applied to determine the most suitable parameter values for each model. The performances of the applied models were comparatively evaluated. Finally, to ensure the explainability of artificial intelligence models in clickbait detection, the SHAP method was used for identifying the factors affecting the classification results.

Keywords

Clickbait Detection , Natural Language Processing , SHAP , Explainable Artificial Intelligence

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Details

Primary Language

English

Subjects

Natural Language Processing

Journal Section

Research Article

Authors

Alper Celal Akgün ^*
0009-0006-5286-0020
Türkiye

Tülin İnkaya
0000-0002-6260-0162
Türkiye

Publication Date

November 21, 2024

Submission Date

October 13, 2024

Acceptance Date

November 21, 2024

Published in Issue

Year 2024 Volume: 7 Number: 1

IZ

https://izlik.org/JA76RA99YU

IEEE

[1]A. C. Akgün and T. İnkaya, “Turkish Clickbait News Detection using Explainable Artificial Intelligence”, International Journal of Data Science and Applications, vol. 7, no. 1, pp. 68–80, Nov. 2024, [Online]. Available: https://izlik.org/JA76RA99YU

AI Research and Application Center, Sakarya University of Applied Sciences, Sakarya, Türkiye.

Turkish Clickbait News Detection using Explainable Artificial Intelligence

Turkish Clickbait News Detection using Explainable Artificial Intelligence

Abstract

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Primary Language

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Publication Date

Submission Date

Acceptance Date

Published in Issue

IZ

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