"Advancements in Fake News Detection: A Comparative Study of Machine and Deep Learning Methods"

In the contemporary landscape of information dissemination, the detection of fake news has emerged as a crucial undertaking due to the rapid proliferation of misinformation across various online channels. This study undertakes a comprehensive examination of fake news detection techniques, encompassing both traditional machine learning and advanced deep learning methods. We explore the efficacy of diverse feature extraction methods coupled with supervised learning methods. Through experiments conducted on established benchmark datasets, we assess the performance of these approaches in terms of classification report, while also scrutinizing their computational efficiency and scalability. Our findings offer valuable insights into the strengths and limitations of each method for fake news detection, thereby furnishing researchers and practitioners with guidance for formulating effective strategies to combat misinformation across online media platforms

Empirical Analysis of Machine Learning algorithms in Fake News detection

Social media is the finest venue for thinking and expressing in the modern world. And this is the best place to share information about your identity, culture, religion, and customs. It entails an immediate information interchange that covers news from every industry. These days, social media has a big impact on how we live and how society functions. Currently, social media is the best medium for expressing your thoughts. Social media has also evolved into a channel for disseminating information about nearby events. how the locals in the other place are made aware of what is going on there. People benefit from this through learning about various cultures. However, some evil people use social media to spread their lies, which affects society and our everyday lives. Furthermore, fake news spreads like a forest fire if it is not dealt with promptly. And this bogus news offends certain individuals and occasionally sparks riots in public places. We need instruments in the modern day that can confirm any news, whether it is real or fraudulent. The current work considers a variety of machine-learning techniques for detecting false news, including Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). The performance evaluation was then conducted using several criteria, including F-1 score, recall, accuracy, and precision. The empirical investigation shows DT has the greatest accuracy level at 100%

Engage students in news writing

The technologies evolution impacts how information is produced and consumed by users. Nonetheless, with the spread of information content available on most online news platforms, the misinformation increases alongside the less credible content. In this scope, the present research aims to develop a technological ecosystem to promote students’ writing ability. The system will help students, search for credible content to create school newspapers. Thus, in this article, the architecture of the solution for news writing tool for the Portuguese language is presented. This paper aims to introduce a constructive approach that presents the system architecture that will support the development of a news creation tool.publishe

Detecting Turkish fake news via text mining to protect brand integrity

Fake news has been in our lives as part of the media for years. With the recent spread of digital news platforms, it affects not only traditional media but also online media as well. Therefore, while companies seek to increase their own brand awareness, they should also protect their brands against fake news spread on social networks and traditional media. This study discusses a solution that accurately classifies the Turkish news published online as real and fake. For this purpose, a machine learning model is trained with tagged news. Initially, the headlines were analyzed within the scope of this study that are collected from Turkish online sources. As a next step, in addition to the headlines of these news, news contexts are also used in the analysis. Analysis are done with unigrams and bigrams. The results show 95% success for the headlines and 80% for the texts for correctly classifying the fake Turkish news articles. This is the first study in the literature that introduces an ML model that can accurately identify fake news in Turkish language

Applying Ensemble Machine Learning Techniques for Fake News Identification

The sharing of information has entered an unprecedented era in human history due to emergence of the Internet With the widespread use of social media sites like Facebook and Twitter. As these platforms enjoy extensive use, users are generating and disseminating a wealth of information, some of which is inaccurate and devoid of factual basis. Detecting false or misleading information within textual content poses a significant challenge. Before arriving at a judgment regarding the accuracy of an article, it is imperative to consider various factors within a specific domain. This paper proposes an Ensemble method for the identification of fraudulent news stories. We leverage different textual features found in both authentic and fake news articles. Our dataset comprises 72,134 news articles, with 35,028 being genuine and 37,106 being false, categorized as binary 0s and 1s. To evaluate our approach, we employed well-known machine learning classifiers including Logistic Regression (LR), Decision Tree, AdaBoost, XGBoost, Random Forest, Extra Trees, SGD, SVM, and Naive Bayes. To enhance the precision of our findings, we devised a multi-model system for identifying fake news the Ensemble approach and the aforementioned classifiers. Experimental analysis conclusively demonstrates that our suggested ensemble learning technique surpasses the performance of individual learners

Using machine learning in social media systems

Διπλωματική εργασία - Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.Nowadays, vast use of Internet and especially, social media, as primary sources of information on everything is happening around the world, has unfortunately facilitated the spreading of fake news at the same time. Thus, everyone can alter real news and publish it on a news website or their social media account, or even invent news and promote it as real, misinforming and even disorienting in this way the public. For this reason, it is crucial to find ways to detect fake news as fast as possible, since fake news dissemination can sometimes be proved destructive, mainly as far as political and social issues are concerned, which have the stronger impact on people’s lives. Classification algorithms use is one way researchers have found in order to deal with this serious problem. In this thesis, we are going to present such a solution, which deploys Data Science and Machine Learning, in order to build a classifier for fake news detection. More specifically, after studying various articles concerning fake news classification, we are going to implement and evaluate our own classifier in a kernel created in Kaggle platform

Linguistic Features and Bi-LSTM for Identification of Fake News

With the spread of Internet technologies, the use of social media has increased exponentially. Although social media has many benefits, it has become the primary source of disinformation or fake news. The spread of fake news is creating many societal and economic issues. It has become very critical to develop an effective method to detect fake news so that it can be stopped, removed or flagged before spreading. To address the challenge of accurately detecting fake news, this paper proposes a solution called Statistical Word Embedding over Linguistic Features via Deep Learning (SWELDL Fake), which utilizes deep learning techniques to improve accuracy. The proposed model implements a statistical method called “principal component analysis” (PCA) on fake news textual representations to identify significant features that can help identify fake news. In addition, word embedding is employed to comprehend linguistic features and Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify news as true or fake. We used a benchmark dataset called SWELDL Fake to validate our proposed model, which has about 72,000 news articles collected from different benchmark datasets. Our model achieved a classification accuracy of 98.52% on fake news, surpassing the performance of state-of-the-art deep learning and machine learning models