Fake News Detection on Social Media: A Data Mining Perspective
TL;DR: Wang et al. as discussed by the authors presented a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets.
Abstract: Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.
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01 Jan 2022
TL;DR: In this article, the authors discuss propagation models for misinformation and review the fake news mitigation techniques, and also compose a list of datasets used in fake news-related studies, concluding with open research questions.
Abstract: Today, major online social networking websites host millions of user accounts. These websites provide a convenient platform for sharing information and opinions in the form of microblogs. However, the ease of sharing also brings ramifications in the form of fake news, misinformation, and rumors, which has become highly prevalent recently. The impact of fake news dissemination was observed in major political events like the US elections and the Jakarta elections, as well as the distortion of celebrities and companies’ reputation. Researchers have studied the propagation of fake news over social media websites and have proposed various techniques to combat fake news. In this chapter, we discuss propagation models for misinformation and review the fake news mitigation techniques. We also compose a list of datasets used in fake news-related studies. The chapter is concluded with open research questions.
6 citations
01 Jan 2020
TL;DR: A novel strategy for the characterization of the Twitter profile is presented based on the generation of an assembly of polarity, emotion, and user statistics characteristics that serve as input to a set of classifiers.
Abstract: The explosive growth of fake news on social networks has aroused great interest from researchers in different disciplines. To achieve efficient and effective detection of fake news requires scientific contributions from various disciplines, such as computational linguistics, artificial intelligence, and sociology. Here we illustrate how polarity, emotion, and user statistics can be used to detect fake profiles on Twitter’s social network. This paper presents a novel strategy for the characterization of the Twitter profile based on the generation of an assembly of polarity, emotion, and user statistics characteristics that serve as input to a set of classifiers. The results are part of our participation in the PAN 2020 in the CLEF in the task of Profiling Fake News Spreaders on Twitter.
6 citations
03 Feb 2022
TL;DR: This paper presents a new public data set of labeled true and fake news in Albanian, and performs an extensive analysis of machine learning methods for fake news detection, exploring the Albanian language related feature categories such as the lexical, syntactic, lying-detection, and psycho-linguistic features.
Abstract: Recent years have witnessed the vast increase of the phenomenon known as the fake news. Among the main reasons for this increase are the continuous growth of internet and social media usage and the real-time information dissemination opportunity offered by them. Deceiving, misleading content, such as the fake news, especially the type made by and for social media users, is becoming eminently hazardous. Hence, the fake news detection problem has become an important research topic. Despite the recent advances in fake news detection, the lack of fake news corpora for the under-resourced languages is compromising the development and the evaluation of existing approaches in these languages. To fill this huge gap, in this article, we investigate the issue of fake news detection for the Albanian language. In it, we present a new public dataset of labeled true and fake news in Albanian and perform an extensive analysis of machine learning methods for fake news detection. We performed a comprehensive feature engineering and feature selection experiments. In doing so, we explored the Albanian language-related feature categories such as the lexical, syntactic, lying-detection, and psycho-linguistic features. Each article was also modeled in four different ways: with the traditional bag-of-words (BoW) and with three distributed text representations using the state-of-the-art Word2Vec, FastText, and BERT methods. Additionally, we investigated the best combination of features and various types of classification methods. The conducted experiments and obtained results from evaluations are finally used to draw some conclusions. They shed light on the potentiality of the methods and the challenges that the Albanian fake news detection presents.
6 citations
26 Oct 2021
TL;DR: In this article, a system for automatic claim verification using Wikipedia is presented, which can predict the veracity of an input claim, and further show the evidence it has retrieved as part of the verification process.
Abstract: The rise of Internet has made it a major source of information. Unfortunately, not all information online is true, and thus a number of fact-checking initiatives have been launched, both manual and automatic, to deal with the problem. Here, we present our contribution in this regard: WhatTheWikiFact, a system for automatic claim verification using Wikipedia. The system can predict the veracity of an input claim, and it further shows the evidence it has retrieved as part of the verification process. It shows confidence scores and a list of relevant Wikipedia articles, together with detailed information about each article, including the phrase used to retrieve it, the most relevant sentences extracted from it and their stance with respect to the input claim, as well as the associated probabilities. The system supports several languages: Bulgarian, English, and Russian.
6 citations
TL;DR: In this paper, the authors present CoVaxLies, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed.
6 citations
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
46,982 citations
TL;DR: In this paper, the authors present a critique of expected utility theory as a descriptive model of decision making under risk, and develop an alternative model, called prospect theory, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights.
Abstract: This paper presents a critique of expected utility theory as a descriptive model of decision making under risk, and develops an alternative model, called prospect theory. Choices among risky prospects exhibit several pervasive effects that are inconsistent with the basic tenets of utility theory. In particular, people underweight outcomes that are merely probable in comparison with outcomes that are obtained with certainty. This tendency, called the certainty effect, contributes to risk aversion in choices involving sure gains and to risk seeking in choices involving sure losses. In addition, people generally discard components that are shared by all prospects under consideration. This tendency, called the isolation effect, leads to inconsistent preferences when the same choice is presented in different forms. An alternative theory of choice is developed, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights. The value function is normally concave for gains, commonly convex for losses, and is generally steeper for losses than for gains. Decision weights are generally lower than the corresponding probabilities, except in the range of low prob- abilities. Overweighting of low probabilities may contribute to the attractiveness of both insurance and gambling. EXPECTED UTILITY THEORY has dominated the analysis of decision making under risk. It has been generally accepted as a normative model of rational choice (24), and widely applied as a descriptive model of economic behavior, e.g. (15, 4). Thus, it is assumed that all reasonable people would wish to obey the axioms of the theory (47, 36), and that most people actually do, most of the time. The present paper describes several classes of choice problems in which preferences systematically violate the axioms of expected utility theory. In the light of these observations we argue that utility theory, as it is commonly interpreted and applied, is not an adequate descriptive model and we propose an alternative account of choice under risk. 2. CRITIQUE
35,067 citations
27,773 citations
09 Jan 2004
TL;DR: A theory of intergroup conflict and some preliminary data relating to the theory is presented in this article. But the analysis is limited to the case where the salient dimensions of the intergroup differentiation are those involving scarce resources.
Abstract: This chapter presents an outline of a theory of intergroup conflict and some preliminary data relating to the theory. Much of the work on the social psychology of intergroup relations has focused on patterns of individual prejudices and discrimination and on the motivational sequences of interpersonal interaction. The intensity of explicit intergroup conflicts of interests is closely related in human cultures to the degree of opprobrium attached to the notion of "renegade" or "traitor." The basic and highly reliable finding is that the trivial, ad hoc intergroup categorization leads to in-group favoritism and discrimination against the out-group. Many orthodox definitions of "social groups" are unduly restrictive when applied to the context of intergroup relations. The equation of social competition and intergroup conflict rests on the assumptions concerning an "ideal type" of social stratification in which the salient dimensions of intergroup differentiation are those involving scarce resources.
14,812 citations
TL;DR: Cumulative prospect theory as discussed by the authors applies to uncertain as well as to risky prospects with any number of outcomes, and it allows different weighting functions for gains and for losses, and two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristic curvature of the value function and the weighting function.
Abstract: We develop a new version of prospect theory that employs cumulative rather than separable decision weights and extends the theory in several respects. This version, called cumulative prospect theory, applies to uncertain as well as to risky prospects with any number of outcomes, and it allows different weighting functions for gains and for losses. Two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristic curvature of the value function and the weighting functions. A review of the experimental evidence and the results of a new experiment confirm a distinctive fourfold pattern of risk attitudes: risk aversion for gains and risk seeking for losses of high probability; risk seeking for gains and risk aversion for losses of low probability. Expected utility theory reigned for several decades as the dominant normative and descriptive model of decision making under uncertainty, but it has come under serious question in recent years. There is now general agreement that the theory does not provide an adequate description of individual choice: a substantial body of evidence shows that decision makers systematically violate its basic tenets. Many alternative models have been proposed in response to this empirical challenge (for reviews, see Camerer, 1989; Fishburn, 1988; Machina, 1987). Some time ago we presented a model of choice, called prospect theory, which explained the major violations of expected utility theory in choices between risky prospects with a small number of outcomes (Kahneman and Tversky, 1979; Tversky and Kahneman, 1986). The key elements of this theory are 1) a value function that is concave for gains, convex for losses, and steeper for losses than for gains,
13,433 citations