Social Media Fake News Classification Using Machine Learning Algorithm (2024)

Abstract: The rise of social media has facilitated easier communication, information sharing, and current affairs updates. However, the prevalence of misleading and deceptive content, commonly referred to as fake news, poses a significant challenge. This paper focuses on the classification of fake news in Malayalam, a Dravidian language, utilizing natural language processing (NLP) techniques. To develop a model, we employed a random forest machine learning method on a dataset provided by a shared task(DravidianLangTech@EACL 2024)1. When evaluated by the separate test dataset, our developed model achieved a 0.71 macro F1 measure.

## introduction the rise in usage of social media sites has made it easier for people to communicate with one another. social media users can converse, share information, and keep up with current affairs. however, a lot of the current material that has surfaced on social media is misleading and, in certain situations, is an attempt to deceive users. this kind of stuff is frequently referred to as false news. any incorrect or deceptive information that purports to be newsworthy is referred to as fake news @xcite . customers and retailers have both been impacted by fake reviews. furthermore, in 2016, the issue of false news came to light, particularly in the wake of the previous us presidential election. since both fake reviews and fake news involve producing and disseminating incorrect information or opinions, they are closely related phenomena @xcite . the rapid expansion of internet news sources has made it exceedingly challenging to distinguish between fraudulent and true information @xcite . because of this, fake 1 https://codalab.lisn.upsaclay.fr/ competitions/16055 news is now widely spread and very difficult to evaluate and confirm. discussing the subject case by case indeed presents a significant difficulty to both the public and the government @xcite . for this reason, a system for fact-checking rumors and statements needs to be implemented, especially for those that receive thousands of views and likes before being disproved and disputed by reliable sources. similarly, humans are incapable of identifying all of these false reports. machine learning classifiers are therefore required to automatically identify these false news items @xcite . even though a range of machinelearning methods have been employed to identify and categorize false information, these methods have limitations in terms of accuracy @xcite . several factors, including imbalanced datasets, ineffective parameter tuning, and bad feature selection, might be blamed for the low accuracy @xcite . although numerous studies are in charge of taking fake news countermeasures in english, languages other than english are also taking advantage of natural language processing(nlp) to mitigate the growing challenge of their language aspect. in this regard, the dravidian language is gaining popularity in leveraging the nlp task including fake news classification, sentiment analysis, hate speech detection, and stress identification in general. in particular, this study focuses on the social media fake news classification in the malayalam language which is one of the dravidian languages. the golden standard dataset was offered to us by the task organizer as a shared task(dravidianlangtech@eacl 2024) @xcite . via codalab, we are given the datasets containing youtube comments in the malayalam language annotated for fake news detection. in this regard, we have used a random forest machine-learning model and developed a malayalam language fake news classifier as intended in the shared task competition. the rest of the paper details the related works, system or methodology descriptions,the results generated, and the recommendations for future works. a study @xcite was conducted to identify fake news in pakistani media, as it is a challenging process to classify. the popular news website scrape was served as the source of the dataset for this investigation. 344 news stories that have been manually classified as true or fake make up the generated corpus. it has employed seven distinct supervised machine learning (ml) classification methods for the result comparison, in addition to two feature extraction strategies (term frequency (tf) and term frequency-inverse document frequency (tf-idf). k-nearest neighbors (knn) provided 70% accuracy, whereas logistic regression produced 69% accuracy, making it the highest-performing classifier. to categorize and identify the bogus news on multiple social media sites, algorithms such as naive bayes, support vector machines, passive aggressive classifier, random forest, bert, lstm, and logistic regression were employed. the work is based on an isot dataset consisting of 44,898 news items that were collected from multiple sources and pre-processed using count vectorizer and tf-idf. the svm is deemed to be among the most accurate algorithms for spotting false information on social media @xcite . deep learning's growth is greatly aided by its widespread use. three types of neural network techniques: convolution filtering-based neural network approaches, sequential analysis-based neural network approaches, and attention mechanismbased neural network approaches can be distinguished from the model structure of the research work that now exist @xcite . but nearly all of them were created with scenarios in a single language in mind, without taking into account mixed-lingual contexts. the article @xcite proposed a unique false news detection model for the context of mixed languages through a multiscale transformer to completely capture the semantic information of the text, bridging this gap by extending the basic pretraining language processing model transformer into the multiscale format. additionally, it improved accuracy in mixed language contexts by roughly 2%-10% as compared to baseline models that are frequently utilized. the multi-modal transformer employing twolevel visual characteristics (mttv) was suggested as an alternative to text for the identification of false news @xcite . firstly, news texts and photos are universally modeled as sequences that may be processed by a transformer. to enhance the use of news photographs, two-level visual features global feature and entity level feature are employed. second, it creates a multi-modal transformer that expands the transformer paradigm for natural language processing, enabling complete interaction and semantic relationship capturing between multi-modal data. furthermore, it suggested a scalable classifier to address the issue of class imbalance and enhance the classification balance of fine-grained false news detection. comprehensive tests on two publicly available datasets show that our approach outperformed the state-of-the-art techniques by a significant margin. even though the majority of studies have been conducted on binary fake news classification tasks, the work @xcite addresses a more realistic scenario by assessing a corpus with unknown themes through multiclass classification, encompassing true, false, partially false, and other categories. three bert-based models: sbert, roberta, and mbert are explored; artificial data generated by chatgpt is used to improve results for class balance; and a two-step binary classification process is employed to improve outcomes. the testing results indicate that, while it is an optimal performance in comparison to past accomplishments, it still requires refinement to remain at the forefront of technology. ## system description in this section, we offer thorough information regarding the dataset and the details of experimental tools. moreover, it dives into the format of datasets, preprocessing, and experimental environmental tools to develop the proposed model. ## result and discussion the random forest based developed model classified the test dataset into two classes as they present in training data. from the ## conclusion in this particular task, we have developed a classifier to classify social media posts into two binary classes, fake and original. the model has used the random forest algorithm method. the numeric features are extracted using tf-idf techniques. the newly developed model has been evaluated with the new unseen test dataset and the results also promising for other code mixed languages.