Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Kannada Dataset

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    Creation and Classification of Kannada Meme Dataset: Exploring Domain and Troll Categories
    (Springer Science and Business Media Deutschland GmbH, 2024) Kundargi, S.Y.; N, N.; Anand Kumar, M.; Chakravarthi, B.R.
    In this pioneering research, the first-ever Kannada memes dataset is established, marking a groundbreaking contribution. This dataset encompasses 2002 memes, spanning various categories such as movies, politics, sports, trolls, and non-troll memes. The classification models have been meticulously fine-tuned for memes, incorporating image-based models using DenseNet169 and text-based models with BERT for text encoding. An innovative multimodal approach combines insights from images and text, acknowledging the comprehensive nature of meme content. Throughout the study, model strengths and weaknesses are assessed, emphasizing their reliance on cutting-edge technologies like Deep Learning and Natural Language Processing. Valuable improvements are recommended, such as the implementation of oversampling techniques and regular dataset updates to enhance relevance and accuracy. This work extends beyond immediate research, contributing to the development of adaptive meme classification systems, particularly for Kannada-speaking audiences within the evolving meme culture landscape. Notably, the results indicate that multimodal models achieved the best scores for domain classification, while image-based models excelled in troll meme classification, further highlighting the significance of this approach within the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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    Frame-Level Audio Hate Speech Detection for Kannada Language
    (Institute of Electrical and Electronics Engineers Inc., 2025) Gubbi, A.V.; Pandey, G.; Koolagudi, S.G.
    Detecting hate speech in audio has become increasingly challenging due to the increasing use of internet platforms and digital communication. Through this study, we develop an audio-based speech classifier to facilitate the detection of hate speech in the Kannada language. We present an approach to classifying hate speech at the frame level by extracting audio features such as Mel-Frequency Cepstral Coefficients (MFCCs), spectral bandwidth, spectral contrast, and chroma features. Furthermore, we present a custom Kannada hate speech dataset to address the scarcity of resources for hate speech studies in the Kannada language. We collected over 40 minutes of audio samples from YouTube and X (formerly Twitter). Our experiments show that an optimized XGBoost model achieved an accuracy of 73% on the custom dataset for frame-level classification. We also propose a cascading classifiers approach with two classifiers to exploit the locality of hate speech that improves the accuracy to 77%. Finally, we benchmark the proposed model against Logistic Regression, SVM, and XGBoost models. © 2025 IEEE.