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: FaNDS as mentioned in this paper is a system based on two concepts: an inconsistency graph and energy flow that assigns each node an initial energy and then some energy is propagated along the edges until the energy distribution on all nodes converges.
Abstract: Recently, the term “fake news” has been broadly and extensively utilized for disinformation, misinformation, hoaxes, propaganda, satire, rumors, click-bait, and junk news. It has become a serious problem around the world. We present a new system, FaNDS, that detects fake news efficiently. The system is based on several concepts used in some previous works but in a different context. There are two main concepts: an Inconsistency Graph and Energy Flow. The Inconsistency Graph contains news items as nodes and inconsistent opinions between them for edges. Energy Flow assigns each node an initial energy and then some energy is propagated along the edges until the energy distribution on all nodes converges. To illustrate FaNDS we use the original data from the Fake News Challenge (FNC-1). First, the data has to be reconstructed in order to generate the Inconsistency Graph. The graph contains various subgraphs with well-defined shapes that represent different types of connections between the news items. Then the Energy Flow method is applied. The nodes with high energy are the candidates for being fake news. In our experiments, all these were indeed fake news as we checked each using several reliable web sites. We compared FaNDS to several other fake news detection methods and found it to be more sensitive in discovering fake news items.
4 citations
12 Oct 2020
TL;DR: In this article, the authors investigate how multimedia researchers can help tackle these problems to level the playing field for social media users by utilizing machine learning and multimedia content analysis techniques in a transparent manner and for the benefit of the users.
Abstract: Participation on social media platforms has many benefits but also poses substantial threats. Users often face an unintended loss of privacy, are bombarded with mis-/disinformation, or are trapped in filter bubbles due to over-personalized content. These threats are further exacerbated by the rise of hidden AI-driven algorithms working behind the scenes to shape users' thoughts, attitudes, and behaviour. We investigate how multimedia researchers can help tackle these problems to level the playing field for social media users. We perform a comprehensive survey of algorithmic threats on social media and use it as a lens to set a challenging but important research agenda for effective and real-time user nudging. We further implement a conceptual prototype and evaluate it with experts to supplement our research agenda. This paper calls for solutions that combat the algorithmic threats on social media by utilizing machine learning and multimedia content analysis techniques but in a transparent manner and for the benefit of the users.
4 citations
20 Apr 2020
TL;DR: It is found that a substantial volume of Russian content was apolitical and emotionally-neutral in nature, and web search also directed traffic to IRA generated web content, and the resultant traffic patterns are distinct from those of social media.
Abstract: The Russia-based Internet Research Agency (IRA) carried out a broad information campaign in the U.S. before and after the 2016 presidential election. The organization created an expansive set of internet properties: web domains, Facebook pages, and Twitter bots, which received traffic via purchased Facebook ads, tweets, and search engines indexing their domains. In this paper, we focus on IRA activities that received exposure through search engines, by joining data from Facebook and Twitter with logs from the Internet Explorer 11 and Edge browsers and the Bing.com search engine. We find that a substantial volume of Russian content was apolitical and emotionally-neutral in nature. Our observations demonstrate that such content gave IRA web-properties considerable exposure through search-engines and brought readers to websites hosting inflammatory content and engagement hooks. Our findings show that, like social media, web search also directed traffic to IRA generated web content, and the resultant traffic patterns are distinct from those of social media.
4 citations
02 Jul 2018
TL;DR: This work investigates automatic moderation of sensitive event-related content across the two modalities by exploiting recent advances in Deep Neural Networks (DNN) by using a combination of image classification with Convolutional Neural Networks and text classification with Recurrent Neural Networks.
Abstract: Social media enables users to spread information and opinions, including in times of crisis events such as riots, protests or uprisings. Sensitive event-related content can lead to repercussions in the real world. Therefore it is crucial for first responders, such as law enforcement agencies, to have ready access, and the ability to monitor the propagation of such content. Obstacles to easy access include a lack of automatic moderation tools targeted for first responders. Efforts are further complicated by the multimodal nature of content which may have either textual and pictorial aspects. In this work, as a means of providing intelligence to first responders, we investigate automatic moderation of sensitive event-related content across the two modalities by exploiting recent advances in Deep Neural Networks (DNN). We use a combination of image classification with Convolutional Neural Networks (CNN) and text classification with Recurrent Neural Networks (RNN). Our multilevel content classifier is obtained by fusing the image classifier and the text classifier. We utilize feature engineering for preprocessing but bypass it during classification due to our use of DNNs while achieving coverage by leveraging community guidelines. Our approach maintains a low false positive rate and high precision by learning from a weakly labeled dataset and then, by learning from an expert annotated dataset. We evaluate our system both quantitatively and qualitatively to gain a deeper understanding of its functioning. Finally, we benchmark our technique with current approaches to combating sensitive content and find that our system outperforms by 16% in accuracy.
4 citations
Posted Content•
TL;DR: There is a potential of using NLP and deep learning to improve automatic detection of fake news, but with the right set of data and features.
Abstract: With the rise of social media, it has become easier to disseminate fake news faster and cheaper, compared to traditional news media, such as television and newspapers. Recently this phenomenon has attracted lot of public attention, because it is causing significant social and financial impacts on their lives and businesses. Fake news are responsible for creating false, deceptive, misleading, and suspicious information that can greatly effect the outcome of an event. This paper presents a synopsis that explains what are fake news with examples and also discusses some of the current machine learning techniques, specifically natural language processing (NLP) and deep learning, for automatically predicting and detecting fake news. Based on this synopsis, we recommend that there is a potential of using NLP and deep learning to improve automatic detection of fake news, but with the right set of data and features.
4 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