TY  - JOUR
T1  - Turkish Clickbait News Detection using Explainable Artificial Intelligence
AU  - Akgün, Alper Celal
AU  - İnkaya, Tülin
PY  - 2024
DA  - November
Y2  - 2024
JF  - International Journal of Data Science and Applications
PB  - Sakarya University of Applied Sciences
WT  - DergiPark
SN  - 3062-3014
SP  - 68
EP  - 80
VL  - 7
IS  - 1
LA  - en
AB  - 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.
KW  - Clickbait Detection
KW  - Natural Language Processing
KW  - SHAP
KW  - Explainable Artificial Intelligence
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L1  - https://dergipark.org.tr/en/download/article-file/4284992
ER  -