Disinformation detection on social media: An integrated approach

Abstract

The emergence of social media platforms has amplified the dissemination of false information in various forms. Social media gives rise to virtual societies by providing freedom of expression to users in a democracy. Due to the presence of echo chambers on social media, social science studies play a vital role in the spread of false news. To this aim, we provide a comprehensive framework that is adapted from several scholarly studies. The framework is capable of detecting information into various types, namely real, disinformation and satire based on authenticity as well as intention. The process highlights the use of interdisciplinary approaches derived from fundamental theories of social science and integrating them with modern computational tools and techniques. Few of these theories claim that malicious users suggest writing fabricated content in a different style to attract the audience. Stylebased methods evaluate the intention i.e., the content is written with an intent to mislead the audience or not. However, the writing style can be deceptive. Thus, it is important to involve user-oriented social information to improve model strength. Therefore, the paper used an integrated approach by combining style based and propagation-based features with a total of thirty-one features. The extracted features are divided into ten categories: relative frequency, quantity, complexity, uncertainty, sentiment, subjectivity, diversity, informality, additional, and popularity. The features have been iteratively utilized by supervised classifiers and then selected the best-correlated ones using the ANOVA test. Our experimental results have shown that the selected features are able to distinguish real from disinformation and satirical news. It has been observed that the Ensemble machine learning model outperformed other models over the developed multi-labelled corpus.

Advancing Fake News Detection: Hybrid Deep Learning With FastText and Explainable AI

“…In this study, we used FastText's unsupervised word vectors, specifically the pre-trained model cc.en.300.bin 3 . This model was developed on Common Crawl and Wikipedia using FastText's unsupervised learning technique, which integrates subword information into the training process.…”

“…Social media's freedom of expression and instant information make it very popular, especially with the younger generation. People all over the world use these platforms to get news about everything from celebrities to politics, often without questioning if the news is real or not [3]. Fake news, which is intentionally created and verifiably false information, is seen as a threat to the stability of democratic systems, diminishing public trust in government institutions, and having a profound effect on critical societal aspects such as elections, economic condi-tions, and public opinions on matters like wars [4] [5].…”

Proposing of suggestive influence detection and classification method based on fuzzy logic and feature driven analysis

“…Let's summarize most important markers, relying on investigations [3,6,8,9,12,13,4,11,14], and create logically full list of notintersecting markers:…”

“…Analyzing existing feature-based approaches and relying on the feature selection rationale applicable to specifying information warfare tools, relying on the investigations [3,6,8,9,12,13,4,11,14] and stepping on the knowledge base on hybrid warfare tools provided by State Service of Special Communications and Information Protection of Ukraine and Center for Countering Disinformation the author proposed the correspondence of markers of suggestive influence to tools of informational influence.…”

A review on fake news detection 3T’s: typology, time of detection, taxonomies

“…The freedom of expression, spontaneous and real-time information provided by social media platforms make it a popular topic of interest, especially among the younger generation. Consumers use these platforms worldwide to access news related to everything from celebrities to politics and often take for granted whether the news is authentic or not [128]. Essentially, the motive of social media platforms is to get users engaged to earn business revenues rather than providing factual information.…”