Fake news Detection on Social Media Using Machine Learning

In today's digital world, the internet deeply ingrains in all facets of our lives. People highly depend on myriad of online sources for news. The proliferation of fake news is increasingly recognized as a significant peril to democracy, journalism, and the principles of freedom of expression. As the use of social media platforms such as Facebook, Twitter, and others accelerates, any news can promptly circulate among thousands of users within a remarkably short interval of time. Additionally, spammers use enticing news headlines as clickbaits to generate profits, using advertisements. To address fake news detection, certain endeavors have been put to develop tools, which prove to be helpful. Yet, it remains crucial to handle the more challenging scenarios, where trustworthy sources heedlessly propagate fake news. This calls for the development of a comprehensive end-to-end solution. The objective of this research is to perform binary classification, using Natural Language Processing and Machine Learning concepts on different news articles to categorize them into fake and real. The findings of this research demonstrate the impact of machine learning techniques in performing this task efficiently.

2021

This paper examines the implementation of natural Techniques of language recognition for 'false news' identification, that is, false news storeys that stem from unreputable storeys from sources. Using a data set and list obtained from Signal Media for OpenSources.co sources, we use the expression frequency-inverse-inverse Detection of bi-grams and probabilistic meaning free grammar (PCFG) document frequency (TF-IDF) in a corpus of articles.[1] Fast Access and Exponential Growth Social networking network data has been made available. It is difficult to analyze between false and true facts. The simple dissemination of data by sharing has contributed to a rapid rise in its falsifying. The credibility of social media networks is also at stake if there is a proliferation of the dissemination of false information. It has now become a study activity to check the data automatically so that it is classified as false or accurate by its source, content and publisher. Machine learning, along with some pitfalls, has played a critical role in the classification of results. This paper explores various approaches to machine learning to distinguish fake and fabricated news. The restriction of such methods and improvisation by the use of deep learning is also explored. [2]

IJARCCE, 2021

The problem of Fake news has evolved much faster in the recent years. Social media has dramatically changed its reach and impact as a whole. On one hand, it's low cost, and easy accessibility with rapid share of information draws more attention of people to read news from it. On the other hand, it enables wide spread of Fake news, which are nothing but false information to mislead people. As a result, automating Fake news detection has become crucial in order to maintain robust online and social media. Artificial Intelligence and Machine learning are the recent technologies to recognize and eliminate the Fake news with the help of Algorithms. In this work, Machine-learning methods are employed to detect the credibility of news based on the text content and responses given by users. A comparison is made to show that the latter is more reliable and effective in terms of determining all kinds of news. The method applied in this work is highest posterior probability of tokens in the response of two classes. It uses frequency-based features to train the Algorithms including supervised learning algorithms and classification algorithm technique. The work also highlights a wide range of features established recently in this area that gives a clearer picture for the automation of this problem. An experiment was conducted in the work to match the lists of Fake related words in the text of responses, to find out whether the response-based detection is a good measure to determine the credibility or not.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2002

In the present technology, the usage of the net is increasing day by day. Together with the rise in usage of social media platforms like Facebook, Twitter, etc. The news spread among innumerable people within a brief period. Nowadays all and sundry is using social media accounts for communication with one another like within the regions like Political events, Technological area, movie events, etc..... Some people on social media spread the fake news in an immovable way. Nowadays it's become a troublesome job to detect fake news on social media. Because the platform doesn't verify the activities of users and their posts is difficult to acknowledge that fake news is either a positive thing or a negative thing. As a personality being, it's tough to detect all this fake news. Solution for this work. Our study explores different textual properties which will be wont to distinguish fake content from real. By using those properties, we train the info by using the various Machine learning algorithms like SVM, Multinomial Naive Bayes, and Random Forest by using The TF-IDF vectorizer to judge their performance on 2 datasets. We aim to supply the user with the flexibility to classify the news as fake or real. The experimental evaluation confirms the superior performance of our proposed ensemble learner approach compared to individual learners. While comparing the three algorithms Multinomial Naive Bayes, Linear Support Vector Machine, and Random Forest. We conclude that the Support Vector Machine Algorithm gives more accuracy than the opposite two algorithms.

2019

As more people become reliant on social media for their news, the spread of unreal or fake news has been more prevalent in society than ever before. Using both linguistic analysis and machine learning, we engineered a program that can classify news articles into fake or real. To create a fake news classifier, we had to split the problem into two parts: extracting features and creating a classifier. In our first prototype , we used Term Frequency-Inverse Document Frequency (TF-IDF) to extract features and the Passive-Aggressive Classifier (PAC) to classify based on the features. In our refined prototype, we decided to use a hashing vectorizer instead of TF-IDF to help our program run faster and make it more scalable to larger datasets. To test the effectiveness of each prototype, 20 trials were run on each program under different training-testing data ratios. The first prototype achieved an accuracy of 94.317284 percent with an average runtime of 4.4118 seconds under its best conditions. The refined prototype reached an accuracy of 93.25967 percent with an average runtime of 3.8138 seconds under its best conditions. While the refined prototype is less accurate than the initial prototype , there are significant speed increases. The refined prototype is only 1.1 percent less accurate, but it is also 13.5 percent faster.

Journal of ICT Standardization

In the modern era where the internet is found everywhere and there is rapid adoption of social media which has led to the spread of information that was never seen within human history before. This is due to the usage of social media platforms where consumers are creating and sharing more information where most of them are misleading with no relevance with reality. Classifying the text article automatically as misinformation is a bit challenging task. This development addresses how automated classification of text articles can be done. We use a machine learning approach for the classification of news articles. Our study involves exploring different textual properties that may be often used to distinguish fake contents from real ones. By using those properties, can train the model with different machine learning algorithms and evaluate their performances. The classifier with the best performance is used to build the classification model which predicts the reliability of the news arti...

Can Machines Learn to Detect Fake News? A Survey Focused on Social Media, 2019

Through a systematic literature review method, in this work we searched classical electronic libraries in order to find the most recent papers related to fake news detection on social medias. Our target is mapping the state of art of fake news detection, defining fake news and finding the most useful machine learning technique for doing so. We concluded that the most used method for automatic fake news detection is not just one classical machine learning technique, but instead a amalgamation of classic techniques coordinated by a neural network. We also identified a need for a domain ontology that would unify the different terminology and definitions of the fake news domain. This lack of consensual information may mislead opinions and conclusions.

Wireless Communications and Mobile Computing

Fake news, or fabric which appeared to be untrue with point of deceiving the open, has developed in ubiquity in current a long time. Spreading this kind of data undermines societal cohesiveness and well by cultivating political division and doubt in government. Since of the sheer volume of news being disseminated through social media, human confirmation has ended up incomprehensible, driving to the improvement and arrangement of robotized strategies for the recognizable proof of wrong news. Fake news publishers use a variety of stylistic techniques to boost the popularity of their works, one of which is to arouse the readers’ emotions. Due to this, text analytics’ sentiment analysis, which determines the polarity and intensity of feelings conveyed in a text, is now being utilized in false news detection methods, as either the system’s foundation or as a supplementary component. This assessment analyzes the full explanation of false news identification. The study also emphasizes char...

IEEE Intelligent Systems

A large body of recent works has focused on understanding and detecting fake news stories that are disseminated on social media. To accomplish this goal, these works explore several types of features extracted from news stories, including source and posts from social media. In addition to exploring the main features proposed in the literature for fake news detection, we present a new set of features and measure the prediction performance of current approaches and features for automatic detection of fake news. Our results reveal interesting findings on the usefulness and importance of features for detecting false news. Finally, we discuss how fake news detection approaches can be used in the practice, highlighting challenges and opportunities.

International Journal of Computer Science and Information Security (IJCSIS), Vol. 23, No. 3, May-June, 2025

Spreading false news online has become a grave threat to public stability, political life, and trust. Conventional content moderation measures prove inadequate to contain the velocity and volume with which disinformation spreads. Machine Learning (ML) has been an invaluable aid in the automation of fake news identification by learning patterns and cues from large amounts of text data. This review paper provides an overview of most of the ML methods employed for the detection of fake news, from the simple algorithms Naive Bayes, Support Vector Machines, and Decision Trees to the recent deep learning methods such as LSTM, CNN, and transformer-based models such as BERT. This work also encompasses feature extraction methods like TF-IDF, word embeddings, and stylometric features, benchmarking datasets, and evaluation metrics used in the literature. The work provides the advantages and limitations of existing methods and highlights open problems such as dataset generalizability, model explainability, and adversarial robustness Lastly, the review outlines future directions, such as the development of hybrid models, real-time detection systems, and ethics. The intention of this paper is to be a starting ground for researchers as well as practitioners in creating improved and more efficient fake news recognition systems. Index Terms—Fake News Detection, Machine Learning, Natural Language Processing, Text Classification, Deep Learning, Transformer Models, Social Media Misinformation, NLP, News Verification