W ord W izards@ D ravidian L ang T ech 2024:Fake News Detection in D ravidian Languages using Cross-lingual Sentence Embeddings (2024)

Abstract: The proliferation of fake news in digital media has become a significant societal concern, impacting public opinion, trust, and decision-making. This project focuses on the development of machine learning models for the detection of fake news. Leveraging a dataset containing both genuine and deceptive news articles, the proposed models employ natural language processing techniques, feature extraction and classification algorithms. This paper provides a solution to Fake News Detection in Dravidian Languages - DravidianLangTech 2024. There are two sub tasks: Task 1 - The goal of this task is to classify a given social media text into original or fake. We propose an approach for this with the help of a supervised machine learning model – SVM (Support Vector Machine). The SVM classifier achieved a macro F1 score of 0.78 in test data and a rank 11. The Task 2 is classifying fake news articles in Malayalam language into different categories namely False, Half True, Mostly False, Partly False and Mostly True.We have used Naive Bayes which achieved macro F1-score 0.3517 in test data and a rank 6.

## introduction fake news refers to false or misleading information presented as genuine news. this misinformation is often disseminated through traditional media, social media platforms, or other online channels. the intent behind fake news is typically to deceive and manipulate public opinion, influence political processes, or generate click-throughs for financial gain. in 2017, fake news has been seen to have influenced the us elections and the british brexit vote, and locally in south africa finance minister pravin gordhan, newspaper editors and journalists have become targets for fake news peddlers. in other instances breaking news on social media has turned out to be false and based on hoaxes and hearsay @xcite . following this, the task of fake news detection in dravidian language dravidianlangtech 2024 @xcite aims to classify a set of social media texts and news articles using principles of feature extraction and machine learning. the subsequent sections of this paper are structured as follows: section 2 reviews relevant literature identified through a thorough literature survey. section 3 presents an overview of the dataset, while section 4 elaborates on the methodology employed for the task at hand. section 5 delves into the discussion of results, and section 6 concludes the paper. ## related works in recent years, there has been significant research in the domain of fake news detection, with a predominant focus on examining and identifying hoaxes within their primary dissemination channel: social media. the prevalent approach involves assessing the likelihood of a particular post being false by analyzing its inherent characteristics, such as likes, followers, shares, etc. this analysis typically employs traditional machine learning methods like classification trees, svm, and similar techniques @xcite . in natural language processing (nlp), there are many ways to approach the subject, and researchers have documented several methods in well-known literature. recently, with advancements in turning image and speech into text, researchers have worked on creating and testing models that are both fast and accurate. in the cited paper @xcite , authors employed artificial intelligence, natural language processing and machine learning for binary classification of news articles as fake or original. they assessed website authenticity using a two-step pro-cess: static search (training naive bayes, random forest, and logistic regression) and dynamic search (with three search fields). in this work (adiba et al., 2020), naive bayes classifier, a bayesian approach of machine learning algorithm has been applied to identify the fake news. the researchers showed how the wealth of corpora can assist algorithm to improve the performance. the dataset collected from an open-source, has been used to classify whether the news is authenticated or not. initially,they achieved classification accuracy about 87 percent which is higher than previously reported accuracy and then 92 percent by the same naive bayes algorithm with enriched corpora. in the cited paper @xcite , authors utilized python's scikit-learn library for tokenization, feature extraction, and vectorization, employing tools like count vectorizer and tiff vectorizer. they conducted feature selection methods based on confusion matrix results to determine the most fitting features for achieving the highest precision. the study revealed that many research papers favored the naive bayes algorithm, achieving prediction precision within the range of 70-76 percent. qualitative analysis, relying on sentiment analysis, titles, and word frequency repetition, was commonly employed in these studies. ## dataset the dataset of task 1 is a list of social media comments in english and malayalam with the labels either fake or original. the sources of data are various social media platforms such as twitter and facebook. figure 1 describes the data distribution of this task. the dataset of task 2 consists of news articles in malayalam with the labels -false, half true, mostly false, partly false and mostly true. 4 methodology 4.1 preprocessing preprocessing of given data involves cleaning, transforming, and organizing raw data into a format that is suitable for analysis or model training. the goal of data preprocessing is to improve the quality and usability of the data. countvectorizer converts a collection of text documents into a matrix of token counts. it is used to transform a corpus of text to a vector of term/token counts. the vocabulary of known words is formed, which is subsequently used for encoding unseen text later. this method was utilized for task-2. a particular limitation faced here is that of the data set being in the malayalam language, for which comparatively less resources are available. thus a lot preprocessing techniques like lemmatisation and stemming could not be performed. the model may struggle to capture the subtleties of language use in different contexts. ## conclusion in this research paper, we present a solution to the fake news detection in dravidian languages-dravidianlangtech 2024. our approach involves classifying the text data into categories such as original and fake and false, half true, mostly false, partly false and mostly true. we leverage a language-agnostic sentence embedder known as labse and machine learning models, svm (support vector machine) and naive bayes. the models demonstrated good f1-scores and accuracy signifying their effectiveness in accurately identifying deceitful news from original news as the rankings are shown in these findings underscore the potential of natural language processing (nlp) in automating fake news detection. this contribution has great implications in the society and politics since people are not influenced by fake news.