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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TL;DR: In this paper , the authors investigated the motives behind the dissemination of fake news and examined its sociodemographic correlates, namely, gender, age, frequency of using social media and frequency of accessing digital news.
Abstract: This paper investigates the motives behind the dissemination of fake news and examines its sociodemographic correlates, namely, gender, age, frequency of using social media and frequency of accessing digital news. A fake news dissemination framework comprising three motives were used to fulfil the aim of the study, namely, Altruism, Attitude and Pass Time. Online questionnaires were distributed resulting in the recruitment of 869 Malaysians (18–59 years old). Linear regressions revealed all three motives to significantly and positively predict fake news dissemination. Further analysis revealed females to engage in fake news dissemination significantly less than males for Pass Time purpose, whereas younger people tend to significantly disseminate more false content for Altruistic purpose than the older cohorts. Individuals who spent less than an hour daily accessing digital news were found to significantly share more fake news for Attitude and Altruism reasons compared to those who spent more than 5 h daily. The identification of the sociodemographic correlates and unique motives for fake news dissemination is deemed beneficial to authorities such as online content regulators and policy makers in order to design more effective strategies to combat the promulgation of such harmful news.
5 citations
14 Jun 2021
TL;DR: In this paper, the authors conduct an exploratory investigation that evaluates and interprets fake new detection models, including looking into which important features that contribute to the models' prediction from the explainable machine learning perspective.
Abstract: Many research efforts recently have aimed at understanding the phenomenon of fake news, including recognizing their common features and patterns, leading to several fake news detection models based on machine learning. Yet, the real distinct strength of those models remains uncertain: some perform well only with particular data, but others are more general. Most of the models classified the fake news as a black-box without giving any explanations to users. In this work, therefore, we conduct an exploratory investigation that evaluates and interprets fake new detection models, including looking into which important features that contribute to the models’ prediction from the explainable machine learning perspective. This give us some insights on how the detection models work and their trustworthiness.
5 citations
TL;DR: A new technique that is highly effective in identifying fake news articles is presented: the first one uses only the agree and disagree classifications; the second uses a subjective opinions based model that can also handle the uncertain cases.
5 citations
01 Jan 2020
TL;DR: In this article, the notions of word-type, word-token, and lemma are defined and discussed. And the mathematical notation used in the entire book is also presented and commented.
Abstract: This second chapter exposes some useful background information that will be useful over the entire book. In particular, this chapter presents and defines precisely the notions of word-type, word-token, and lemma. The mathematical notation used in the entire book is also presented and commented. A running example (the Federalist Papers) is described and will serve to illustrate the concepts and computations done in the book. Next, the most important overall stylometric measurements are explained and numerical examples are provided. In this case, the Zipf’s Law and various vocabulary richness measures (e.g., type-token ratio (TTR), Herdan’s C, Yule’s K) are discussed. Finally, other global stylistic measurements are presented, such as lexical density (LD), percentage of big words (BW), or the mean sentence length (MSL).
5 citations
01 Jan 2020
TL;DR: In this paper, the authors proposed several approaches to detect passages written by each possible author, including the rolling delta and other ad hoc approaches, and an overview of them is included in this chapter.
Abstract: Some well-known models have been explained in the previous chapter, but various advanced approaches have been suggested. Related to the humanities, the Zeta test is focusing on terms used recurrently by one author and mainly ignored by the others. Selecting stylistic markers based on this criterion, the model builds a graph showing the similarities between text excerpts. Compression algorithms could also be applied to identify the true author of a text based on similar word frequencies. More related to the natural language processing domain, the latent Dirichlet allocation (LDA) could be applied to define the most probable author of a given document. To solve the verification problem, several dedicated approaches have been suggested and an overview of them is included in this chapter. Although we usually assume that a novel is written only by a single person, collaborative authorship is possible. To detect passages written by each possible author, the rolling Delta and other ad hoc approaches are described. As neural models constitute an important research field, three sections have been dedicated to them, with one on the basic neural approach, one focusing on word embeddings, and the third on the long short-term memory (LSTM), a well-known deep learning model. The last section is dedicated to adversarial stylometry and obfuscation, or how one can possibly program a computer to hide stylistic markers left by the original author.
5 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