WELFake: Word Embedding Over Linguistic Features for Fake News Detection

Abstract

Social media is a popular medium for the dissemination of real-time news all over the world. Easy and quick information proliferation is one of the reasons for its popularity. An extensive number of users with different age groups, gender, and societal beliefs are engaged in social media websites. Despite these favorable aspects, a significant disadvantage comes in the form of fake news, as people usually read and share information without caring about its genuineness. Therefore, it is imperative to research methods for the authentication of news. To address this issue, this article proposes a two-phase benchmark model named WELFake based on word embedding (WE) over linguistic features for fake news detection using machine learning classification. The first phase preprocesses the data set and validates the veracity of news content by using linguistic features. The second phase merges the linguistic feature sets with WE and applies voting classification. To validate its approach, this article also carefully designs a novel WELFake data set with approximately 72 000 articles, which incorporates different data sets to generate an unbiased classification output. Experimental results show that the WELFake model categorizes the news in real and fake with a 96.73% which improves the overall accuracy by 1.31% compared to bidirectional encoder representations from transformer (BERT) and 4.25% compared to convolutional neural network (CNN) models. Our frequencybased and focused analyzing writing patterns model outperforms predictive-based related works implemented using the Word2vec WE method by up to 1.73%.

General Collective Intelligence as a Platform for Computational Social Systems

“…Because of this, fake news detection algorithms [9], [10] all make an assumption about what is or is not fake news, where depending on whether the individual is conscious of their predisposition towards a given reasoning style for a given problem in a given topic, this assumption might be incorrect. In order to be applied more correctly, such algorithms would need to be identified explicitly as operating within one or more of the quadrants in figure 1.…”

Evaluation of Machine Learning Approaches for Classification of Fake News

“…In less than a generation, social media has evolved from a The transformation of direct electronic information interchange into a virtual meeting place, commerce platform, and critical 21st-century marketing instrument A "online community," according to Merriam-Webster, is defined as "a form of electronic communication (such as websites for social networking and micro blogging) through which people can form online communities to share information and ideas, to send personal messages, and to post other types of content" (such as videos). [1,[3][4][5] Throughout the remainder of this chapter, will examine the roots of social media, its relatively quick emergence as a social and economic force, and the changes it has brought about in the marketing industry. Following the introduction of blogging, social media has seen a meteoric rise in popularity.…”

“…In the context of misinformation, incorrect information is given by someone who thinks it to be true and distributes it to the public. Fake news comes in a variety of forms [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]: [1] "A satirical or parodic piece (No intention to cause harm but has potential to fool)" [2] "When the headlines, images, or captions do not reflect the substance of the article, this is known as a false connection." [3] "Content that is deceptive (Misleading use of information to frame an issue or an individual)" [4] "When actual material is presented with incorrect contextual information, this is referred to as false context."…”

“…Fake news comes in a variety of forms [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]: [1] "A satirical or parodic piece (No intention to cause harm but has potential to fool)" [2] "When the headlines, images, or captions do not reflect the substance of the article, this is known as a false connection." [3] "Content that is deceptive (Misleading use of information to frame an issue or an individual)" [4] "When actual material is presented with incorrect contextual information, this is referred to as false context." [5] "Content Created by Impersonators (When genuine sources are impersonated with false, made-up sources)" [6] "Content that has been modified (When genuine information or imagery is manipulated to deceive, as with a doctored photo)" [7] "Content that has been fabricated (New content is Khubaib Ahmed Qureshi et.al [2].…”

Exploring Author Profiling for Fake News Detection

“…To detect fake news, they combine convolutional neural networks (CNN) with bidirectional encoder representations from transformers (BERT), achieving an accuracy of 98.9% and outperforming the current state-of-the-art model. Similarly, Prateek Agrawal et al [12] pair linguistic and context-based features to develop WELFake. They implement one-hot encoding and term frequency-inverse document frequency (TF-IDF) to extract context-based features.…”