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 , a theoretical research model proposed and analyzed using structural equations modeling in SmartPLS on a convenience sample consisting of 922 questionnaires was used to examine social media users' behavior towards environmentally friendly brands.
Abstract: Social media has triggered an increase in fake news spread about different aspects of modern lives, society, politics, societal changes, etc., and has also affected companies’ reputation and brands’ trust. Therefore, this paper is aimed at investigating why social media users share fake news about environmentally friendly brands. To examine social media users’ behavior towards environmentally friendly brands, a theoretical research model proposed and analyzed using structural equations modeling in SmartPLS on a convenience sample consisting of 922 questionnaires. Data was collected by means of a quantitative-based approach via a survey conducted among social media users from an emerging market. The results show that social media flow has a mediated impact on sharing fake news about environmentally friendly brands on social media. Considering the critical consequences of fake news, the paper argues that understanding the dissemination process of this type of bogus content on social media platforms has important theoretical and managerial implications. Understanding the psychological mechanisms that influence people’s behavior in sharing fake news about environmentally friendly brands on social networking sites (SNS) could help in better understanding the factors and the effects of this phenomenon. The originality of this research consists of proposing flow theory from positive psychology to be used as a theoretical framework to explain users’ behavior of sharing fake news about environmentally friendly brands on social media.
9 citations
24 Jul 2021
TL;DR: A novel method to learn discriminative features from tweets content, Facebook posts and followed their non-sequential propagation structure, and generated more powerful representations for identifying fake news and its propagation by constructing a social network graph.
Abstract: Nowadays, people rely mostly on social media for any kind of information sharing and also started acquiring information through social media platforms for e-news mostly related to politics via Twitter, Facebook, and YouTube. Fake news detection and identifying its propagation path are technically very challenging. In this work, we have implemented a novel method to learn discriminative features from tweets content, Facebook posts and followed their non-sequential propagation structure, and generated more powerful representations for identifying fake news and its propagation by constructing a social network graph. We proposed level order traversal up to three levels based on top-down tree structured networks for fake propagation learning and detected the neighbors of the fake news source and removed them from the network which naturally confirms the reduction of their propagation. We have considered the benchmark data set LIAR and used PolitiFact user data for our research work. The main objective of our work is to identify the propagation path of the fake news content by collecting news and verifying its authenticity using fact-checking websites, namely “
www.politifact.com
”, and creating a network among the users who have high similarity in their contents posted. Now it will be easier to trace the path if the source identified has fake content, then its neighbors can be tracked and moving forward the same idea can be iterated up to bottom levels.
9 citations
19 Jan 2022
TL;DR: A deep neural network approach that can classify fake and real news claims by exploiting ‘Convolutional Neuron Networks’ is proposed and outperforms the performance of the state-of-the-art approaches when applied to the same Arabic dataset with the highest accuracy.
Abstract: Fake news stories can polarize society, particularly during political events. They undermine confidence in the media in general. Current NLP systems are still lacking the ability to properly interpret and classify Arabic fake news. Given the high stakes involved, determining truth in social media has recently become an emerging research that is attracting tremendous attention. Our literature review indicates that applying the state-of-the-art approaches on news content address some challenges in detecting fake news’ characteristics, which needs auxiliary information to make a clear determination. Moreover, the ‘Social-context-based’ and ‘propagation-based’ approaches can be either an alternative or complementary strategy to content-based approaches. The main goal of our research is to develop a model capable of automatically detecting truth given an Arabic news or claim. In particular, we propose a deep neural network approach that can classify fake and real news claims by exploiting ‘Convolutional Neuron Networks’. Our approach attempts to solve the problem from the fact checking perspective, where the fact-checking task involves predicting whether a given news text claim is factually authentic or fake. We opt to use an Arabic balanced corpus to build our model because it unifies stance detection, stance rationale, relevant document retrieval and fact-checking. The model is trained on different well selected attributes. An extensive evaluation has been conducted to demonstrate the ability of the fact-checking task in detecting the Arabic fake news. Our model outperforms the performance of the state-of-the-art approaches when applied to the same Arabic dataset with the highest accuracy of 91%.
9 citations
TL;DR: A new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert is proposed, based on majority voting on the crowd side and weighted averaging on the third- party side, which provides slightly better results than existing aggregation models.
Abstract: Social networks play an important role in today’s society and in our relationships with others. They give the Internet user the opportunity to play an active role, e.g., one can relay certain information via a blog, a comment, or even a vote. The Internet user has the possibility to share any content at any time. However, some malicious Internet users take advantage of this freedom to share fake news to manipulate or mislead an audience, to invade the privacy of others, and also to harm certain institutions. Fake news seeks to resemble traditional media to establish its credibility with the public. Its seriousness pushes the public to share them. As a result, fake news can spread quickly. This fake news can cause enormous difficulties for users and institutions. Several authors have proposed systems to detect fake news in social networks using crowd signals through the process of crowdsourcing. Unfortunately, these authors do not use the expertise of the crowd and the expertise of a third party in an associative way to make decisions. Crowds are useful in indicating whether or not a story should be fact-checked. This work proposes a new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert. The aggregator is based on majority voting on the crowd side and weighted averaging on the third-party side. An experimentation has been conducted on 25 posts and 50 voters. A quantitative comparison with the majority vote model reveals that our aggregation model provides slightly better results due to weights assigned to accredited users. A qualitative investigation against existing aggregation models shows that the proposed approach meets the requirements or properties expected of a crowdsourcing system and a voting system.
9 citations
Proceedings Article•
01 May 2020TL;DR: In this paper, the authors proposed an annotated dataset of ≈ 50k news that can be used for building automated fake news detection systems for a low resource language like Bangla.
Abstract: Observing the damages that can be done by the rapid propagation of fake news in various sectors like politics and finance, automatic identification of fake news using linguistic analysis has drawn the attention of the research community. However, such methods are largely being developed for English where low resource languages remain out of the focus. But the risks spawned by fake and manipulative news are not confined by languages. In this work, we propose an annotated dataset of ≈ 50K news that can be used for building automated fake news detection systems for a low resource language like Bangla. Additionally, we provide an analysis of the dataset and develop a benchmark system with state of the art NLP techniques to identify Bangla fake news. To create this system, we explore traditional linguistic features and neural network based methods. We expect this dataset will be a valuable resource for building technologies to prevent the spreading of fake news and contribute in research with low resource languages. The dataset and source code are publicly available at https://github.com/Rowan1697/FakeNews.
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