Recently, the emergence of social media has opened the way for online harassment in the form of hate speech and offensive language. An automated approach is needed to detect hate and offensive content from social media, which is indispensable. This task is challenging in the case of social media posts or comments in low-resourced CodeMix languages. This paper investigates the efficacy of various multilingual transformer-based embedding models with machine learning classifiers for detecting hate speech and offensive language (HOS) content in social media posts in CodeMix Dravidian languages that belong to the low-resource language group. Experiments were conducted on six sets of openly available datasets in Kannada-English, Malayalam-English and Tamil-English languages. The objective is to identify a single pre-trained embedding model that commonly works well for HOS tasks in the above mentioned languages. For this, a comprehensive study of various multilingual transformer embedding models, such as BERT, DistilBERT, LaBSE, MuRIL, XLM, IndicBERT, and FNET for HOS detection was conducted. Our experiments revealed that MuRIL pre-trained embedding performed consistently well for all six datasets using Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. In a set of experiments conducted on six datasets, the highest accuracy results for each dataset are as follows: DravidianLangTech 2021 achieved 96% accuracy for Malayalam, 72% accuracy for Tamil, and 66% accuracy for Kannada. For HASOC 2021 Tamil, the accuracy reached 76%, and for HASOC 2021 Malayalam, it reached 68%. Additionally, HASOC 2020 demonstrated an accuracy of 92% for Malayalam. Moreover, we performed an in-depth error analysis and a comparative study, presenting a tabulated summary of our work compared to other top-performing studies. In addition, we employed a cost-sensitive learning approach to address the class imbalance problem in the dataset, in which minority classes get higher classification weights than the majority classes. The weights were initialized and fine-tuned to obtain the best balance between all the classes. The results showed that incorporating the cost-sensitive learning strategy avoided class bias in the trained model. In addition to the aforementioned points, a significant contribution of our research presented in this paper is introducing a novel annotated test set for Malayalam-English CodeMix. This new dataset serves as an extension to our existing data, known as the Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages (HASOC) 2021 Malayalam-English dataset.

Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus and Indian languages are morphologically rich. In this paper, we propose a neural machine translation (NMT) system for four language pairs: English–Malayalam, English–Hindi, English–Tamil, and English–Punjabi. We also collected sentences from different sources and cleaned them to make four parallel corpora for each of the language pairs, and then used them to model the translation system. The encoder network in the NMT architecture was designed with long short-term memory (LSTM) networks and bi-directional recurrent neural networks (Bi-RNN). Evaluation of the obtained models was performed both automatically and manually. For automatic evaluation, the bilingual evaluation understudy (BLEU) score was used, and for manual evaluation, three metrics such as adequacy, fluency, and overall ranking were used. Analysis of the results showed the presence of lengthy sentences in English–Malayalam, and the English–Hindi corpus affected the translation. Attention mechanism was employed with a view to addressing the problem of translating lengthy sentences (sentences contain more than 50 words), and the system was able to perceive long-term contexts in the sentences.

Detecting hate speech on social media platforms is vital to mitigate technology-facilitated violence. Extensive research has targeted widely spoken languages like English, but there is a notable gap in studying hate speech detection in low-resource languages like Tamil. Additionally, with social media platforms now supporting various modalities, including text, speech, and video, effective techniques for hate speech detection in multimodal formats, especially videos, are crucial. However, detecting hate speech in Tamil presents unique challenges due to its morphology and code-mixing nature. This article presents a comprehensive approach for detecting hate speech in Tamil, with a focus on multimodal data. We introduce a new dataset, the MultimodAl Tamil Hate dataset, comprising videos along with their audio and textual transcripts, annotated with four categories of hate speech: offensive, sexist, racist, and casteist. To classify hate speech categories, we leverage transformer-based models. Through a series of experiments, we evaluated the performance of each modality individually and explored their fusion using a multimodal approach. BERT-based models were used for textual analysis to extract informative features, the TimeSformer model was employed for video modality, and Wav2Vec2 was used for audio modality. Specifically, we attained 81.82% accuracy and a 68.65% F1-score for the text modality, 63.63% accuracy and a 50.60% F1-score for the audio modality, and 45.45% accuracy and a 33.64% F1-score for the video modality. By integrating optimal combinations of models from each modality and employing machine learning classifiers, we achieved an accuracy of 81.82% and an F1-score of 66.67% in our hate speech classification task. Our research findings highlight the effectiveness of employing a multimodal approach for hate speech detection in Tamil, showcasing its efficacy in curbing the dissemination of hateful content on social media platforms.