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The current public sense of anxiety in dealing with disinformation as manifested by so-called fake news is acutely displayed by the reaction to recent events prompted by a belief in conspiracies among certain groups. A model to deal with disinformation is proposed; it is based on a demonstration of the analogous behavior of disinformation to that of wave phenomena. Two criteria form the basis to combat the deleterious effects of disinformation: the use of a refractive medium based on skepticism as the default mode, and polarization as a filter mechanism to analyze its merits based on evidence. Critical thinking is enhanced since the first one tackles the pernicious effect of the confirmation bias, and the second the tendency towards attribution, both of which undermine our efforts to think and act rationally. The benefits of such a strategy include an epistemic reformulation of disinformation as an independently existing phenomenon, that removes its negative connotations when perceived as being possessed by groups or individuals.
Over the past three years it has become evident that fake news is a danger to democracy. However, until now there has been no clear understanding of how to define fake news, much less how to model it. This paper addresses both these issues. A definit
Disinformation through fake news is an ongoing problem in our society and has become easily spread through social media. The most cost and time effective way to filter these large amounts of data is to use a combination of human and technical interve
Automatically identifying fake news from the Internet is a challenging problem in deception detection tasks. Online news is modified constantly during its propagation, e.g., malicious users distort the original truth and make up fake news. However, t
At the latest since the advent of the Internet, disinformation and conspiracy theories have become ubiquitous. Recent examples like QAnon and Pizzagate prove that false information can lead to real violence. In this motivation statement for the Works
This is a paper for exploring various different models aiming at developing fake news detection models and we had used certain machine learning algorithms and we had used pretrained algorithms such as TFIDF and CV and W2V as features for processing textual data.