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: It is suggested that the underlying network connections of users that share fake news are discriminative enough to support the detection of fake news.
Abstract: The buzz over the so-called "fake news" has created concerns about a degenerated media environment and led to the need for technological solutions. As the detection of fake news is increasingly considered a technological problem, it has attracted considerable research. Most of these studies primarily focus on utilizing information extracted from textual news content. In contrast, we focus on detecting fake news solely based on structural information of social networks. We suggest that the underlying network connections of users that share fake news are discriminative enough to support the detection of fake news. Thereupon, we model each post as a network of friendship interactions and represent a collection of posts as a multidimensional tensor. Taking into account the available labeled data, we propose a tensor factorization method which associates the class labels of data samples with their latent representations. Specifically, we combine a classification error term with the standard factorization in a unified optimization process. Results on real-world datasets demonstrate that our proposed method is competitive against state-of-the-art methods by implementing an arguably simpler approach.
6 citations
29 Apr 2022
TL;DR:
Abstract: During the COVID-19 pandemic, the World Health Organization provided a checklist to help people distinguish between accurate and misinformation. In controlled experiments in the United States and Germany, we investigated the utility of this ordered checklist and designed an interactive version to lower the cost of acting on checklist items. Across interventions, we observe non-trivial differences in participants’ performance in distinguishing accurate and misinformation between the two countries and discuss some possible reasons that may predict the future helpfulness of the checklist in different environments. The checklist item that provides source labels was most frequently followed and was considered most helpful. Based on our empirical findings, we recommend practitioners focus on providing source labels rather than interventions that support readers performing their own fact-checks, even though this recommendation may be influenced by the WHO’s chosen order. We discuss the complexity of providing such source labels and provide design recommendations.
6 citations
02 May 2022
TL;DR: A new method for text anonymization based on transformer based language models that circumvents most of the identified weaknesses and also of-fers a formal privacy guarantee is proposed.
Abstract: As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private mechanisms to perturb word embeddings, thus replacing individual words in a sentence. While these methods represent very important contributions, have various advantages over other techniques and do show anonymization capabilities, they have several shortcomings. In this paper, we investigate these weaknesses and demonstrate significant mathemati-cal constraints diminishing the theoretical privacy guarantee as well as major practical shortcomings with regard to the protection against deanonymization attacks, the preservation of content of the original sentences as well as the quality of the language output. Finally, we propose a new method for text anonymization based on transformer based language models fine-tuned for paraphrasing that circumvents most of the identified weaknesses and also of-fers a formal privacy guarantee. We evaluate the performance of our method via thorough experimentation and demonstrate superior performance over the discussed mechanisms.
6 citations
10 Dec 2020
TL;DR: The EPIC30M corpus as mentioned in this paper is a large-scale epidemic corpus that contains more than 30 million micro-blog posts from year 2006 to 2020, and contains a subset of 26.2 million tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 4.7 million tweets of six global epidemic outbreaks, including the 2009 H1N1 Swine flu, 2010 Haiti Choline, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Choline and 2018 Kivu
Abstract: Since the start of COVID-19, there has been several relevant corpora from various sources that were released to support research in this area. While these corpora are valuable in supporting analysis for this specific pandemic, researchers will benefit from additional benchmark corpora that contain other epidemics for better generalizability and to facilitate cross-epidemic pattern recognition and trend analysis tasks. During our research, we discover little disease related corpora in the literature that are sizable and rich enough to support such cross-epidemic analysis tasks. To address this issue, we present EPIC30M, a large-scale epidemic corpus that contains more than 30 million micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020. EPIC30M contains a subset of 26.2 million tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 4.7 million tweets of six global epidemic outbreaks, including the 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola. Furthermore, we explore and discuss the properties of this corpus with statistics of key terms and hashtags and trends analysis for each subset. Finally, we discuss the potential value and impact that EPIC30M could generate through a discussion of multiple use cases of cross-epidemic research topics that attract growing interest in recent years. These use cases span multiple research areas, such as epidemiological modeling, pattern recognition, natural language understanding and economical modeling. The corpus is publicly available at https://www.github.com/junhua/epic.
6 citations
TL;DR: A comprehensive survey of the state of art methods for detecting malicious users and bots based on different features proposed in the novel taxonomy and avert the crucial problem of fake news detection is provided.
Abstract: One of the major components of Societal Digitalization is Online social networks (OSNs). OSNs can expose people to different popular trends in various aspects of life and alter people’s beliefs, behaviors, and decisions and communication. Social bots and malicious users are the significant sources for spreading misinformation on social media and can pose serious cyber threats in society. The degree of similarity of user profiles of a cyber bot and a malicious user spreading fake news is so great that it is very difficult to differentiate both based on their attributes. Over the years, researchers have attempted to find a way to mitigate this problem. However, the detection of fake news spreaders across OSNs remains a challenge. In this paper, we have provided a comprehensive survey of the state of art methods for detecting malicious users and bots based on different features proposed in our novel taxonomy. We have also aimed to avert the crucial problem of fake news detection by discussing several key challenges and potential future research areas to help researchers who are new to this field.
6 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