nlpt malayalm@ D ravidian L ang T ech : Fake News Detection in M alayalam using Optimized XLM - R o BERT a Model (2023)

Abstract: The paper demonstrates the submission of the team nlpt_malayalm to the Fake News Detection in Dravidian Languages-DravidianLangTech@LT-EDI-2023. The rapid dissemination of fake news and misinformation in today’s digital age poses significant societal challenges. This research paper addresses the issue of fake news detection in the Malayalam language by proposing a novel approach based on the XLM-RoBERTa base model. The objective is to develop an effective classification model that accurately differentiates between genuine and fake news articles in Malayalam. The XLM-RoBERTa base model, known for its multilingual capabilities, is fine-tuned using the prepared dataset to adapt it specifically to the nuances of the Malayalam language. A thorough analysis is also performed to identify any biases or limitations in the model’s performance. The results demonstrate that the proposed model achieves a remarkable macro-averaged F-Score of 87% in the Malayalam fake news dataset, ranking 2nd on the respective task. This indicates its high accuracy and reliability in distinguishing between real and fake news in Malayalam.

## introduction fake news and misinformation proliferation have become pervasive problems in today's digital age. the rapid spread of false information through social media platforms and online news sources has the potential to mislead and deceive individuals, leading to harmful consequences for individuals, communities, and even entire nations. in recent years, numerous research efforts have focused on developing effective solutions for fake news detection in various languages @xcite . however, even though millions of people worldwide and in the indian state of kerala speak malayalam, a dearth of research is specifically focused on it. as a result, there is a pressing need to address this gap and develop robust models tailored for detecting fake news in malayalam. this research paper aims to tackle the challenge of fake news detection in malayalam by proposing an innovative approach based on the xlm-roberta base model. the xlm-roberta @xcite model is chosen due to its exceptional performance in multilingual tasks and ability to capture contextual information effectively. by leveraging the power of this model, we aim to develop a classification system capable of accurately identifying fake news articles written in malayalam. the contributions of this research paper include the following: ## related work detecting fake news and misinformation has garnered significant attention from researchers in recent years. numerous studies have explored various approaches and techniques to address this challenging problem. this section provides an overview of related work in fake news detection, focusing on methods applied to different languages and the specific challenges associated with detecting fake news in malayalam. @xcite used the xlm-r model for offensive language identification in dravidian languages. yasaswini et al. (2021) used the transfer learning technique to identify offensive language in dravidian languages. to obtain optimal results, they used various classifiers like xlmr, mbert, cnn, and ulmfit. ghanghor et al. (2021) used mbert and xlm-r-based models with sauce loss and class weights for identifying the offensive language in dravidian languages. chen and kong (2021) used mbert with the textcnn model to identify offensive language in dravidian languages. k et al. (2021) used the cnn-bilstm hybrid model in shared tasks for identifying offensive language. saha et al. (2021) proposed transformer-based ensembling strategies for identifying offensive language in dravidian languages. garcía-díaz et al. (2022) proposed a knowledge integration method using fasttext, bert, and xlm-robereta word embeddings and linguistic features. palanikumar et al. (2022) used machine learning models and muril for transliteration as data augmentation for abuse detection in tamil. s n et al. (2022) used tf-idf with a random kitchen algorithm for abusive comment detection in tamil. lekshmiammal et al. (2022) used muril to identify offensive span in tamil. biradar and saumya (2022) used indicbert and svm classifier for identifying abusive comments in dravidian code mixed data. raja et al. (2023) used xlm-r and mbert transformer models with adaptive fine-tuning for fake news detection in dravidian languages. transformer-based models have emerged as powerful tools for various nlp tasks, including fake news detection. these models leverage self-attention mechanisms to capture global dependencies in text, enabling them to learn contextual representations effectively. transformer architectures such as bert have demonstrated remarkable success in various nlp tasks. ## dataset description the data sources are diverse social media platforms like facebook, twitter, etc. the objective of the shared task is to classify the given social media post as either fake or original news. the competition organizers have released the dataset for fake news detection in dravidian languages in the malayalam language s et al. @xcite . the dataset's train, validation, and test set distributions are depicted in table1. we see that the given dataset is almost balanced foot_0 . it consists of transliterated, code-mixed, emojis, and malayalam-scripted data. ## system description in this section, we present the model architecture for detecting fake news in malayalam. we experimented with three different transformer-based models to classify the text as fake or real. we used mul-tilingualbert (mbert) @xcite and @xcite as the base models, and we leveraged the optimized xlm-roberta (xlm-r) as the proposed model, a transformerbased language model known for its effectiveness in various nlp tasks, including text classification. ## results and evaluation in this section, we present the results and evaluation of the proposed fake news detection model for the malayalam language. we analyze the model's performance based on the evaluation metrics and provide insights into its effectiveness in distinguishing between real and fake news. ## conclusion in this research paper, we propose a fake news detection model for the malayalam language. the model utilizes the xlm-roberta architecture and is finetuned on a dataset of real and fake news articles in malayalam. we demonstrated through extensive experimentation and evaluation that the proposed model achieves promising results in detecting fake news in malayalam. the model achieved an impressive macro f1 score of 87%, indicating its ability to balance precision and recall. we compared the proposed model with existing approaches such as mbert and distilbert to fake news detection and discussed its performance, strengths, and limitations. the use of the xlm-roberta model, combined with finetuning, proved advantageous in capturing contextual information and handling the linguistic nuances of the malayalam language.