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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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Book ChapterDOI
13 Sep 2021
TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end model based on deep neural networks, which concatenates image features with the joint text and sentiment features to produce a reliable, fused classification.
Abstract: Reputed by their low-cost, easy-access, real-time and valuable information, social media also wildly spread unverified or fake news. Rumors can notably cause severe damage on individuals and the society. Therefore, rumor detection on social media has recently attracted tremendous attention. Most rumor detection approaches focus on rumor feature analysis and social features, i.e., metadata in social media. Unfortunately, these features are data-specific and may not always be available. In contrast, post contents (including images or videos) play an important role and can indicate the diffusion purpose of a rumor. Furthermore, rumor classification is also closely related to opinion mining and sentiment analysis. Yet, to the best of our knowledge, exploiting images and sentiments is little investigated. Considering the available multimodal features from microblogs, notably, we propose in this paper an end-to-end model called deepMONITOR that is based on deep neural networks, by utilizing all three characteristics: post textual and image contents, as well as sentiment. deepMONITOR concatenates image features with the joint text and sentiment features to produce a reliable, fused classification. We conduct extensive experiments on two large-scale, real-world datasets. The results show that deepMONITOR achieves a higher accuracy than state-of-the-art methods.

6 citations

Journal ArticleDOI
22 Apr 2021
TL;DR: In this article, the authors used machine learning stratification approaches in the frame of text analytics, and compared their efficiency in stratifying coronavirus tweets of different lengths using Naive Bayes method, 91% accuracy was achieved for short tweets and using logistic regression classification method, 74% accuracy is achieved for long tweets.
Abstract: In present modern era, the outbreak of COVID-19 pandemic has created informational crisis. The public sentiments collected from different reflexions (hashtags, comments, tweets, posts of twitter) are measured accordingly, ensuring different policy decisions and messaging are incorporated. The implementation demonstrates intuition in to the advancement of fear sentiment eventually as COVID-19 approaches maximum levels in the world, by making use of detailed textual analysis with the help of required text data visualization. In addition, technical outline of machine learning stratification approaches are provided in the frame of text analytics, and comparing their efficiency in stratifying coronavirus tweets of different lengths. Using Naive Bayes method, 91% accuracy is achieved for short tweets and using logistic regression classification method, 74% accuracy is achieved for short tweets.

6 citations

Proceedings ArticleDOI
21 Jun 2021
TL;DR: In this article, a content-agnostic ML classifier was proposed to automatically detect fake news websites (i.e., F1 score up to 0.942 and AUC of ROC up to0.976) that are not yet included in manually curated blacklists.
Abstract: Over the past decade, we have witnessed the rise of misinformation on the Internet, with online users constantly falling victims of fake news. A multitude of past studies have analyzed fake news diffusion mechanics and detection and mitigation techniques. However, there are still open questions about their operational behavior such as: How old are fake news websites? Do they typically stay online for long periods of time? Do such websites synchronize with each other their up and down time? Do they share similar content through time? Which third-parties support their operations? How much user traffic do they attract, in comparison to mainstream or real news websites? In this paper, we perform a first of its kind investigation to answer such questions regarding the online presence of fake news websites and characterize their behavior in comparison to real news websites. Based on our findings, we build a content-agnostic ML classifier for automatic detection of fake news websites (i.e., F1 score up to 0.942 and AUC of ROC up to 0.976) that are not yet included in manually curated blacklists.

6 citations

Journal ArticleDOI
TL;DR: The proposed method to detect influential news in online communities is conduced by using the Three-Way Decisions approach based on Probabilistic Rough Sets to perform a tri-partitioning of online users.

6 citations

Posted Content
TL;DR: This paper proposes a novel deep reinforcement learning-based method to control the output of GPT-2 with respect to a given news topic and considers realistic news as news that cannot be easily detected by a fake news classifier.
Abstract: Nowadays, there exist powerful language models such as OpenAI's GPT-2 that can generate readable text and can be fine-tuned to generate text for a specific domain. Considering GPT-2, it cannot directly generate synthetic news with respect to a given topic and the output of the language model cannot be explicitly controlled. In this paper, we study the novel problem of topic-preserving synthetic news generation. We propose a novel deep reinforcement learning-based method to control the output of GPT-2 with respect to a given news topic. When generating text using GPT-2, by default, the most probable word is selected from the vocabulary. Instead of selecting the best word each time from GPT-2's output, an RL agent tries to select words that optimize the matching of a given topic. In addition, using a fake news detector as an adversary, we investigate generating realistic news using our proposed method. In this paper, we consider realistic news as news that cannot be easily detected by a fake news classifier. Experimental results demonstrate the effectiveness of the proposed framework on generating topic-preserving news content than state-of-the-art baselines.

6 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.