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 Jul 2020
TL;DR: This paper proposes an approach based on multiple criteria associated with news, on which the use of aggregation operators guided by linguistic quantifiers allow the modeling of the decision maker behavior into the news credibility assessment process.
Abstract: Locating Web content useful to specific user needs and tasks concerns nowadays, in many circumstances, to assess the credibility of the content itself. With the diffusion of social media and the possibility for everyone to become a content generator, the problem of assessing information credibility has become a major research issue, in particular in microblogging sites, where fake news, hoaxes and other kinds of misinformation are diffused almost without any traditional form of trusted intermediation. In this paper, we propose an approach based on multiple criteria associated with news, on which the use of aggregation operators guided by linguistic quantifiers allow the modeling of the decision maker behavior into the news credibility assessment process. The operation and the evaluation of the approach are illustrated by considering the Twitter microblogging platform.
9 citations
TL;DR: Fake news is a type of online disinformation with misleading and/or false statements that may or may not be associated with real events, intentionally designed to mislead or manipulate a specific or imagined public through the appearance of a news format with an opportunistic structure (title, image, content) to attract the reader's attention as discussed by the authors .
Abstract: Current literature on fake news is rather abundant and mainly focused on history, variety, and types, rather than processes. This review draws on current literature to build a working definition of fake news focused on its present relevance to journalism and political communication contemporary debate, distinguishing it from non-pertinent conceptual varieties and contributing to a much-needed clarification on the subject. We performed a qualitative analysis of the literature published between 2016 and 2020. Data were extracted from Web of Science and Scopus. We define fake news as a type of online disinformation with misleading and/or false statements that may or may not be associated with real events, intentionally designed to mislead and/or manipulate a specific or imagined public through the appearance of a news format with an opportunistic structure (title, image, content) to attract the reader’s attention in order to obtain more clicks and shares and, therefore, greater advertising revenue and/or ideological gain.
9 citations
01 Jan 2020
TL;DR: This paper proposes a Multi-modal Component Embedding framework (MCE) for fake news detection, which is designed to satisfy all the requirements and extensive experiments show that MCE outperforms all the baselines.
Abstract: As numerous fake news bloom and spread wildly on social media, fake news detection has recently been drawing a growing amount of attention. Single news consists of various multi-modal components (e.g., text, image, and event). Thus, a desirable model for fake news detection must satisfy two requirements: 1) it must correctly learn the reliability of each component 2) it must be capable of capturing the relationship among the components. In this paper, we propose a Multi-modal Component Embedding framework (MCE) for fake news detection, which is designed to satisfy all the requirements. It first defines a latent vector for each news article as the sum of its component latent vectors. For each component, we regard its magnitude as its reliability, and regard its directional relationship as its consistency. In this context, the magnitude of each news latent vector represents how reliable the news is. Thus, MCE learns the latent space so that the magnitude of the real news vectors becomes larger than that of the fake news vectors. During the training, a news vector becomes larger when its component vectors are reliable (i.e., large magnitude) and when its component vectors are well aligned (i.e., high consistency). By doing so, MCE can capture the complex relationship among the components as well as the reliability of each component. Our extensive experiments on two real-world datasets show that MCE outperforms all the baselines. We also provide a qualitative analysis on the embedding space to verify its capability of satisfying the requirements.
9 citations
TL;DR: This study investigates the propagation of two distinct narratives– conspiracy information and scientific information, and finds that conspiracy cascades tend to propagate in a multigenerational branching process whereas science cascades are more likely to grow in a breadth-first manner.
Abstract: With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life. Drawing on a large-scale dataset which covers more than 1.4M posts and 18M comments, we investigate the propagation of two distinct narratives--(i) conspiracy information, whose claims are generally unsubstantiated and thus referred as misinformation to some extent, and (ii) scientific information, whose origins are generally readily identifiable and verifiable--in an online social media platform. We find that conspiracy cascades tend to propagate in a multigenerational branching process while science cascades are more likely to grow in a breadth-first manner. Specifically, conspiracy information triggers larger cascades, involves more users and generations, persists longer, is more viral and bursty than science information. Content analysis reveals that conspiracy cascades contain more negative words and emotional words which convey anger, fear, disgust, surprise and trust. We also find that conspiracy cascades are more concerned with political and controversial topics. After applying machine learning models, we achieve an AUC score of nearly 90% in discriminating conspiracy from science narratives.
We find that conspiracy cascades are more likely to be controlled by a broader set of users than science cascades, imposing new challenges on the management of misinformation. Although political affinity is thought to affect the consumption of misinformation, there is very little evidence that political orientation of the information source plays a role during the propagation of conspiracy information. Our study provides complementing evidence to current misinformation research and has practical policy implications to stem the propagation and mitigate the influence of misinformation online.
9 citations
01 Jan 2022
TL;DR: Inference operators are formulated which augment the graph edges by revealing unobserved interactions between its elements, such as similarity between documents’ contents and users’ engagement patterns, resulting in improved performance in fake news detection experiments.
Abstract: Easy access, variety of content, and fast widespread interactions are some of the reasons making social media increasingly popular. However, this rise has also enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. Detecting it is an important and challenging problem to prevent large scale misinformation and maintain a healthy society. We view fake news detection as reasoning over the relations between sources, articles they publish, and engaging users on social media in a graph framework. After embedding this information, we formulate inference operators which augment the graph edges by revealing unobserved interactions between its elements, such as similarity between documents’ contents and users’ engagement patterns. Our experiments over two challenging fake news detection tasks show that using inference operators leads to a better understanding of the social media framework enabling fake news spread, resulting in improved performance.
9 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