Beyond Tech@ D ravidian L ang T ech2024 : Fake News Detection in D ravidian Languages Using Machine Learning (2024)

Abstract: In the digital age, identifying fake news is essential when fake information travels quickly via social media platforms. This project employs machine learning techniques, including Random Forest, Logistic Regression, and Decision Tree, to distinguish between real and fake news. With the rise of news consumption on social media, it becomes essential to authenticate information shared on platforms like YouTube comments. The research emphasizes the need to stop spreading harmful rumors and focuses on authenticating news articles. The proposed model utilizes machine learning and natural language processing, specifically Support Vector Machines, to aggregate and determine the authenticity of news. To address the challenges of detecting fake news in this paper, describe the Machine Learning (ML) models submitted to ‘Fake News Detection in Dravidian Languages” at DravidianLangTech@EACL 2024 shared task. Four different models, namely: Naive Bayes, Support Vector Machine (SVM), Random forest, and Decision tree.

## introduction people are increasingly choosing to search for and consume news from social media rather than traditional news sources as more and more of our lives are spent communicating online via social media platforms @xcite . @xcite fake news propagators have an opportunity to intentionally sway people's attitudes, beliefs, and trust by disseminating fake information. rumors and false information typically travel quickly, harming specific relationships and social ties. moreover, negative understanding, public scrutiny, and social distancing can also result in worry and emotional torment. @xcite it is now necessary to relate to and filter out comparable false news automatically in order to lessen the harm and pain that fake news causes associations and communities. the internet and social media have made it much easier and more straightforward to obtain news information. it is true gilda (2017) that there are a lot of websites that easily generate fake news. they usually use social media to boost their online presence and increase their impact. dummy news websites pose as authoritative sources on topics (often political) in an attempt to sway public opinion. @xcite fake information may be a global problem as well as a global task. many experts think ai and machine literacy might potentially be used to address the problem of fake news. the paper is mainly concentrated on classifying whether a piece of news is fake or not. in this paper, problem and system description describes the dataset and how the dataset is preprocessed. the methodology uses classification algorithms to find the accuracy of models in classifying real and fake news in the given dataset and it also describes the algorithms. at last, the result gives the best model and its accuracy. ## literature review ahmad and lokeshkumar (2019) investigated text mining for the identification of false news. the dataset is initially preprocessed and relative algorithms are applied. smitha and bharath (2020) have taken data from many websites. the collected data are split into test and train and then the dataset is preprocessed, the preprocessed data are given to the ml algorithm after performing feature extraction. the study albahr and albahar (2020) looks at random forests, naive bayes, and decision trees. the liar dataset, a popular dataset for identifying false news, was used for the experiment. to enhance the effectiveness of machine learning algorithms in identifying false news, they have employed nlp techniques. a variety of classification techniques, such as svm, bounded decision trees, random forests, gradient boosting, and stochastic gradient descent, were employed byshaikh and patil (2020). according to the gilda (2017), tf-idf of bi-grams fed into a stochastic gradient descent model can identify non-credible sources with an accuracy of 77.2%. sharma et al. (2020) ## problem and system description identifying and minimizing bogus news on social media is the aim of this collaborative effort on fake news identification. ## methodology the methodology investigates a number of machine learning strategies and pre-processing techniques for the identification of fake news. the naive bayes classifier, svm, random forest, and decision tree method are a few well-known classifiers that have been studied. the several steps taken while processing a text in order to classify it. ## conclusion finally, it should be noted that when fake news spreads, it attempts to alter people's perceptions and attitudes about utilizing digital technologies. there are two possible outcomes when individuals fall for fake news: initially, people begin to think that their preconceived notions about a given subject are accurate. our fraudulent news detection system was developed to stop this problem by evaluating user-submitted information and classifying it as real or fraudulent. several machine learning and natural language processing (nlp) approaches must be used to do this.