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Journal ArticleDOI

Fake News Detection on Social Media: A Data Mining Perspective

01 Sep 2017-Sigkdd Explorations (ACM)-Vol. 19, Iss: 1, pp 22-36
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.
Citations
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Journal ArticleDOI
TL;DR: In this paper , the authors analyzed the optimal approaches for detecting and tracking hate speech on social media platforms, as well as the relationship between the approaches, dataset type, size, and accuracy.
Abstract: With the proliferation of social media platforms that provide anonymity, easy access, online community development, and online debate, detecting and tracking hate speech has become a major concern for society, individuals, policymakers, and researchers. Combating hate speech and fake news are the most pressing societal issues. It is difficult to expose false claims before they cause significant harm. Automatic fact or claim verification has recently piqued the interest of various research communities. Despite efforts to use automatic approaches for detection and monitoring, their results are still unsatisfactory, and that requires more research work in the area. Fake news and hate speech messages are any messages on social media platforms that spread negativity in society about sex, caste, religion, politics, race, disability, sexual orientation, and so on. Thus, the type of massage is extremely difficult to detect and combat. This work aims to analyze the optimal approaches for this kind of problem, as well as the relationship between the approaches, dataset type, size, and accuracy. Finally, based on the analysis results of the implemented approaches, deep learning (DL) approaches have been recommended for other Ethiopian languages to increase the performance of all evaluation metrics from different social media platforms. Additionally, as the review results indicate, the combination of DL and machine learning (ML) approaches with a balanced dataset can improve the detection and combating performance of the system.

10 citations

Proceedings ArticleDOI
02 Jul 2018
TL;DR: The proposed model uses the textual features of the social-sensor cloud services, i.e., comments and meta-data, e.g., spatio-temporal information to gather the trust-rate of the service to show the performance of the proposed model with real datasets.
Abstract: We propose a new social-sensor cloud services trust model We propose to represent social media data streams, ie, images' meta-data and related posted information, as social-sensor cloud services Images' meta-data and the related posted information are abstracted as the functional and non-functional aspects of the social-sensor cloud services The trustworthiness of a social-sensor cloud service is measured based on the users' stance based trust model We use the textual features of the social-sensor cloud services, ie, comments and meta-data, eg, spatio-temporal information to gather the trust-rate of the service Analytical results are presented to show the performance of the proposed model with real datasets

10 citations

Proceedings ArticleDOI
01 Dec 2018
TL;DR: This paper proposed a multiple time-series data analysis model to detect rumors on Twitter that only uses temporal properties of the tweets, which allows quick detection of rumors.
Abstract: Rumor detection becomes increasingly important in social media. The effects of rumor propagation are dreadful in case of time-critical events, for example, during natural disasters. In this paper, we proposed a multiple time-series data analysis model to detect rumors on Twitter. Instead of checking the contents of the tweets, the proposed method only uses temporal properties of the tweets. As a result, the computational complexity measured by the training time and prediction time has been reduced significantly, which allows quick detection of rumors. Experimental results show that the proposed model combined with Gaussian Naive Bayes classifier achieved a high precision score of 94%.

10 citations

Posted Content
TL;DR: This work proposes a unifying framework that considers the key stakeholders of AF regulation, and mathematically formalizes this framework, using it to construct a data-driven, statistically sound regulatory procedure that satisfies several important criteria.
Abstract: Through the algorithmic filtering (AF) of content, social media platforms (SMPs) have the ability to influence users' perceptions and behaviors Attempts to regulate externalities of AF are often difficult to pass or enforce due to critical social, legal, financial, and user related considerations In this work, we explore this multifaceted problem by proposing a unifying framework that considers the key stakeholders of AF regulation (or self-regulation) We mathematically formalize this framework, using it to construct a data-driven, statistically sound regulatory procedure that satisfies several important criteria First, by design, it moderates the effect of AF on user learning and decision-making Second, it has desirable properties of online governance, including being normative and user-driven Third, by illustrating the regulatory procedure in linear dynamical systems, we prove that it can align social and financial interests Specifically, we identify conditions under which the regulation imposes a low cost on the SMP's reward (eg, profits) and incentivizes the SMP to increase content diversity

10 citations

Posted Content
TL;DR: In this paper, the authors focus on claims made in a political debate and study the impact of modeling the context of the claim, both on the source side and on the target side, i.e., in the fact-checking explanation document.
Abstract: Recent years have seen the proliferation of disinformation and misinformation online, thanks to the freedom of expression on the Internet and to the rise of social media. Two solutions were proposed to address the problem: (i) manual fact-checking, which is accurate and credible, but slow and non-scalable, and (ii) automatic fact-checking, which is fast and scalable, but lacks explainability and credibility. With the accumulation of enough manually fact-checked claims, a middle-ground approach has emerged: checking whether a given claim has previously been fact-checked. This can be made automatically, and thus fast, while also offering credibility and explainability, thanks to the human fact-checking and explanations in the associated fact-checking article. This is a relatively new and understudied research direction, and here we focus on claims made in a political debate, where context really matters. Thus, we study the impact of modeling the context of the claim: both on the source side, i.e., in the debate, as well as on the target side, i.e., in the fact-checking explanation document. We do this by modeling the local context, the global context, as well as by means of co-reference resolution, and reasoning over the target text using Transformer-XH. The experimental results show that each of these represents a valuable information source, but that modeling the source-side context is more important, and can yield 10+ points of absolute improvement.

10 citations

References
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Journal ArticleDOI
28 May 2015-Nature
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

Book ChapterDOI
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

Book ChapterDOI
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

Journal ArticleDOI
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

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Issue of fake news

The paper discusses the issue of fake news on social media and its potential negative impacts on individuals and society.