Browsing by Author "Chittaragi, N."

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    Fake News Detection Using Machine Learning Algorithms
    (Association for Computing Machinery, 2022) Imbwaga, J.L.; Chittaragi, N.; Koolagudi, S.G.
    There has been an exponential growth in users sharing news and information in real-time on various social media platforms worldwide. However, few of the users share fake and misleading news for various reasons. The reasons for sharing fake news may not be limited to financial, personal, and/or political gain. Since users cannot determine or censor the type of content that appears on their respective platforms, fake news can pose significant and detrimental effects on an individual and society at large. In this regard, we have proposed the work with the primary objective of development of a fake news detection system by applying supervised machine learning algorithms on an annotated (labeled) dataset. The dataset was selected from Kaggle, consisting of fake news with 23503 entries and true news with 21418 entries. An overall better accuracies are observed with tree-based decision tree classifiers and a gradient boosting ensemble algorithm. © 2022 ACM.
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    Hate Speech Detection in Audio Using SHAP - An Explainable AI
    (Springer Science and Business Media Deutschland GmbH, 2024) L Imbwaga, J.; Chittaragi, N.; G Koolagudi, S.
    Hate speech detection is a process of recognition of communication media such as text, audio, and/or video, if it contains hatred and/or encourages violence towards a person or a community of people. This is usually based on prejudice against ’protected characteristics’ such as their ethnicity, gender, sexual orientation, religion, age and so on. Complex and sophisticated classifiers based hate speech detection systems are available in the literature. However, the characteristics exhibited by explainable artificial intelligence techniques demonstrated versatile capabilities. This potential is due to the complex classifiers presenting themselves as black-box in nature hence limiting the social acceptability and usability of the developed systems. In this study, video datasets for English and Kiswahili languages were manually collected from YouTube, converted to audio, and used to detect hate speech. Ensemble based classification algorithms have been used for implementation of hate speech detection system. Random Forest classifier recorded an accuracy of 95.8% for English language while for Kiswahili language, Extreme Gradient Boosting classifier achieved an accuracy of 91.8%. To explain the results achieved by these classifiers, in terms of how specific audio-based features contributed to the overall detection of hate speech, SHapley Additive exPlanations technique (SHAP) is used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.