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 Jan 2021
Abstract: The paper aims to identify lexical means of discursive modelling of the current Russian-Cuban relations (from 2000 till nowadays) in news media texts of the Cuban News Agency “Prensa Latina” (“PL”). Scientific originality of the study lies in the fact that the author for the first time considers the problem of discursive modelling of the current Russian-Cuban relations. The methods of discourse analysis and content analysis are applied, which makes it possible to reveal the Cuban journalists’ constructive vision of the RussianCuban relations under the modern geo-political conditions. The following conclusions are justified: “PL” news media texts contain lexical and stylistic means emphasizing strategic importance of the Russian-Cuban relations based on historical friendship ties.
2 citations
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TL;DR: MetaDetector as mentioned in this paper proposes an end-to-end adversarial adaptation network to transfer meta knowledge between different events, which can learn event-shared features and alleviate the negative transfer caused by the large distribution shift between events.
Abstract: The blooming of fake news on social networks has devastating impacts on society, economy, and public security. Although numerous studies are conducted for the automatic detection of fake news, the majority tend to utilize deep neural networks to learn event-specific features for superior detection performance on specific datasets. However, the trained models heavily rely on the training datasets and are infeasible to apply to upcoming events due to the discrepancy between event distributions. Inspired by domain adaptation theories, we propose an end-to-end adversarial adaptation network, dubbed as MetaDetector, to transfer meta knowledge (event-shared features) between different events. Specifically, MetaDetector pushes the feature extractor and event discriminator to eliminate event-specific features and preserve required event-shared features by adversarial training. Furthermore, the pseudo-event discriminator is utilized to evaluate the importance of historical event posts to obtain partial shared features that are discriminative for detecting fake news. Under the coordinated optimization among the four submodules, MetaDetector accurately transfers the meta-knowledge of historical events to the upcoming event for fact checking. We conduct extensive experiments on two large-scale datasets collected from Weibo and Twitter. The experimental results demonstrate that MetaDetector outperforms the state-of-the-art methods, especially when the distribution shift between events is significant. Furthermore, we find that MetaDetector is able to learn the event-shared features, and alleviate the negative transfer caused by the large distribution shift between events.
2 citations
21 Nov 2019
TL;DR: The most important finding is the statistically significant difference in the news sentiment where it has been shown that fake news articles have a more negative sentiment.
Abstract: The advent of social networks has changed how can be the thinking of the population influenced. Although the spreading of false information or false messages for personal or political benefit is certainly nothing new, current trends such as social media enable every individual to create false information easier than ever with the spread compared to the leading news portals. Fake news detection has recently attracted growing interest from the general public and researchers. The paper aims to compare basic text characteristics of fake and real news article types. We analysed two datasets that contained a total of 28 870 articles. The results were validated using the third data set consisting of 402 articles. The most important finding is the statistically significant difference in the news sentiment where it has been shown that fake news articles have a more negative sentiment. Also, an interesting result was the difference of average words per sentence. Finding statistically significant differences in individual text characteristics is a piece of important information for the future fake news classifier in terms of selecting the appropriate attributes for classification.
2 citations
TL;DR: In this article, a study of SARS-CoV-2 (schweres akutes Atemwegssyndrom-Coronavirus-Typ-2) verlangsamen, haben Bund and Bundeslander Schutzmasnahmen ergriffen, the weitreichende Folgen fur die bevolkerung haben.
Abstract: Um die Ausbreitung von SARS-CoV‑2 (schweres akutes Atemwegssyndrom-Coronavirus-Typ 2) zu verlangsamen, haben Bund und Bundeslander Schutzmasnahmen ergriffen, die weitreichende Folgen fur die Bevolkerung haben. Diese Masnahmen umfassen u. a. die zeitweise Einschrankung des Betriebs von Freizeiteinrichtungen sowie Kontakt- und Reiseeinschrankungen. Die Masnahmen rufen gemischte Reaktionen hervor, wobei Teile der Bevolkerung Empfehlungen und Vorgaben ignorieren. Ziel der vorliegenden Studie ist es, auf Basis der Beitrage in sozialen Medien die Grunde fur die Ablehnung von Schutzmasnahmen zu untersuchen. 3 soziale Netzwerke (Facebook, Twitter und Youtube-Kommentare) wurden fur den Zeitraum 02.03. bis 18.04.2020 systematisch hinsichtlich der Einstellungen zu Kontaktbeschrankungen und anderen Schutzmasnahmen mittels qualitativer Dokumenten- und Inhaltsanalyse untersucht. Insgesamt wurden 119 Beitrage in die Analyse aufgenommen und interpretiert. 6 Hauptkategorien und 4 Unterkategorien wurden im Zusammenhang mit der Ablehnung der Schutzmasnahmen identifiziert: Fehlinformationen der sozialen Medien (Verharmlosung und Zweifel an der Wirksamkeit), Misstrauen gegenuber den etablierten offentlichen Medien, Wissensdefizite und Verunsicherung, Einschrankung der Grundrechte, die Rolle der Behorden (Bevolkerungskontrolle und mangelndes Vertrauen in das Robert Koch-Institut) sowie wirtschaftliche Auswirkungen der Pandemie. Fehlinformationen in sozialen Medien und Wissensdefizite konnen zu einer Unterschatzung der Pandemie beitragen. Zudem konnen wirtschaftliche Belastungen mit der Ablehnung von Schutzvorkehrungen einhergehen. Zur Erhohung der Akzeptanz implementierter Schutzmasnahmen stellen Gesundheitsaufklarung sowie transparente und evidenzbasierte Kommunikation relevante Determinanten dar.
2 citations
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TL;DR: In this paper, the authors proposed ScoreGAN for fraud review detection that makes use of both review text and review rating scores in the generation and detection process for improving the stability and scalability of the GAN.
Abstract: The promising performance of Deep Neural Networks (DNNs) in text classification, has attracted researchers to use them for fraud review detection. However, the lack of trusted labeled data has limited the performance of the current solutions in detecting fraud reviews. The Generative Adversarial Network (GAN) as a semi-supervised method has demonstrated to be effective for data augmentation purposes. The state-of-the-art solutions utilize GANs to overcome the data scarcity problem. However, they fail to incorporate the behavioral clues in fraud generation. Additionally, state-of-the-art approaches overlook the possible bot-generated reviews in the dataset. Finally, they also suffer from a common limitation in scalability and stability of the GAN, slowing down the training procedure. In this work, we propose ScoreGAN for fraud review detection that makes use of both review text and review rating scores in the generation and detection process. Scores are incorporated through Information Gain Maximization (IGM) into the loss function for three reasons. One is to generate score-correlated reviews based on the scores given to the generator. Second, the generated reviews are employed to train the discriminator, so the discriminator can correctly label the possible bot-generated reviews through joint representations learned from the concatenation of GLobal Vector for Word representation (GLoVe) extracted from the text and the score. Finally, it can be used to improve the stability and scalability of the GAN. Results show that the proposed framework outperformed the existing state-of-the-art framework, namely FakeGAN, in terms of AP by 7\%, and 5\% on the Yelp and TripAdvisor datasets, respectively.
2 citations
References
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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