A bhi P aw@ D ravidian L ang T ech: Fake News Detection in D ravidian Languages using Multilingual BERT (2023)

Abstract: This study addresses the challenge of detecting fake news in Dravidian languages by leveraging Google’s MuRIL (Multilingual Representations for Indian Languages) model. Drawing upon previous research, we investigate the intricacies involved in identifying fake news and explore the potential of transformer-based models for linguistic analysis and contextual understanding. Through supervised learning, we fine-tune the “muril-base-cased” variant of MuRIL using a carefully curated dataset of labeled comments and posts in Dravidian languages, enabling the model to discern between original and fake news. During the inference phase, the fine-tuned MuRIL model analyzes new textual content, extracting contextual and semantic features to predict the content’s classification. We evaluate the model’s performance using standard metrics, highlighting the effectiveness of MuRIL in detecting fake news in Dravidian languages and contributing to the establishment of a safer digital ecosystem. Keywords: fake news detection, Dravidian languages, MuRIL, transformer-based models, linguistic analysis, contextual understanding.

## introduction the proliferation of fake news has become a pressing concern in the digital age, significantly impacting the accuracy and credibility of information dissemination. detecting and combating fake news is a critical task to ensure the integrity of news sources and promote informed decision-making. while extensive research has been conducted in various languages and domains, the detection of fake news in the dravidian language poses unique challenges due to its linguistic characteristics and cultural nuances. the challenges involved in fake news detection have been widely explored in the literature. previous studies have examined different task formula-tions, datasets, and nlp solutions to address this issue @xcite . understanding the potential and limitations of existing approaches is essential for developing effective strategies in the context of the dravidian language. while existing research has made significant contributions to fake news detection, the unique linguistic characteristics and cultural context of the dravidian language necessitate dedicated efforts and language-specific approaches. this paper aims to address the challenges of fake news detection in the dravidian language by exploring methodologies, datasets, and potential avenues for future research. by leveraging insights from related work, we aim to contribute to the development of effective and culturally sensitive solutions for detecting fake news in the dravidian language space. ## related work to understand the challenges involved in fake news detection @xcite made a survey, along with describing the related tasks. they systematically review and compare the task formulations, datasets and nlp solutions that have been developed for the task, and also discuss the potentials and limitations of them. @xcite propose a new model based on capsule neural networks for detecting fake news, they also propose two architectures for different lengths of news statements. with increase in popularity of graph networks, following are some works in fake news detection. @xcite propose fang, a novel graphical social context representation and learning framework for fake news detection. unlike previous contextual models that have targeted performance, their focus is on representation learning. @xcite present adversarial active learning-based heterogeneous graph neural net-work (aa-hgnn) which employs a novel hierarchical attention mechanism to perform node representation learning in the heterogeneous information network hin. @xcite show that given a claim and a set of potential evidence sentences that form an evidence graph, kernel graph attention network (kgat) introduces node kernels, which better measure the importance of the evidence node, and edge kernels, which conduct fine-grained evidence propagation in the graph, into graph attention networks for more accurate fact verification. for more realisitc scenarios and social media @xcite aim at predicting whether a source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. they develop a neural network-based model, graph-aware co-attention networks (gcan), to achieve the goal. ensembling machine learning models are quite competant as depicted by - @xcite , @xcite . @xcite employ a voting-based approach of the three most effective techniques to decide that the given news article is fake or real. they perform experiments using several classical machine learning techniques, three types of features, unigram, bigram and trigram. @xcite use ensemble of various transformer based models for the fake news detection task in the urdu language. @xcite use transfer learning with bert algorithm. @xcite use charcnn along with roberta to obtain sentence embeddings with respect to both the word level and character level. they adopt label smoothing to improve the generalization capability of the model. @xcite prepare fake news dataset for low resource languages (tamil, kannada, gu-jarati, and malayalam); they experiment with logistic regression and bert models to perform the detection. @xcite also offer a multilingual multidomain fake news detection dataset of five languages and seven different domains for resource scarce scenarios. they propose an effective neural model based on the multilingual bidirectional encoder representations from transformer (bert) for domain-agnostic multilingual fake news classification. ## method the task is to classifying youtube comments as either original or fake news. ## conclusion in conclusion, we address the crucial issue of fake news detection in dravidian languages by har-