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.
Citations
More filters
TL;DR: The team undertook a social experiment combined with reflective analysis to better understand the impact of ID check policies when combined with other standards policies of a typical platform and identifies grave concerns.
Abstract: In March 2019, Facebook updated its security procedures requesting ID verification for people who wish to advertise or promote political posts of adverts. The announcement received little media coverage even though it is an interesting development in the battle against fake news. This paper aims to review the current literature on different approaches in the battle against the spread of fake news, including the use of computer algorithms, artificial intelligence (AI) and introduction of ID checks.,Critical to the evaluation is consideration into ID checks as a means to combat the spread of fake news. To understand the process and how it works, the team undertook a social experiment combined with reflective analysis to better understand the impact of ID check policies when combined with other standards policies of a typical platform.,The analysis identifies grave concerns. In a wider context, standardising such policy will leave political activists in countries vulnerable to reprisal from authoritarian regimes. Other victims of the impacts include people who use fake names to protect the identity of adopted children or to protect anonymity from abusive partners.,The analysis also points to the fact that troll armies could bypass these checks rendering the use of ID checks less effective in the battle to combat fake news.
3 citations
15 Nov 2019
TL;DR: This study is based on a dataset originally drawn from the Facebook social network page of a large multinational cosmetics company, and shows that not all predictors are significant in explaining the criterion variable.
Abstract: Social Media is a source with tremendous volumes of data which is only growing by the day. Taking a cue from earlier studies on data generated from social media, this study is based on a dataset originally drawn from the Facebook social network page of a large multinational cosmetics company. A total of 500 out of 790 posts published on the social media page were analyzed through the data mining classification technique i.e. linear regression. Nine numeric attributes (variables) were regressed with one attribute considered as the criterion attribute and the rest eight as predictor attributes. The eight dependent variables were: V1 “lifetime post total reach”, V2 “lifetime post total impressions”, V3 “lifetime engaged users”, V4 “lifetime post consumers”, V5 “lifetime post consumptions”, V6 “lifetime post impressions by people who have liked your page”, V7 “lifetime post reach by people who like your page”, V8 “lifetime people who have liked your page and engaged with your post”; and the independent variable was V9 “Total interactions - the sum total of comments, likes, and shares of a post”. The results show that not all predictors are significant in explaining the criterion variable. Similarly, a correlation matrix was generated where the inter-attribute correlation among all nine attributes was calculated. The results of association drawn from correlation are different from regression which depicts the fundamental difference and approach of these two techniques. WEKA version 3.8 was the data mining software used to analyze the dataset.
3 citations
25 Apr 2022
TL;DR: A Knowledge-Aware Hierarchical Attention Network (KAHAN) that integrates information on temporal information on social media and external knowledge related to the news into the model to establish fact-based associations with entities in the news content and detected the veracity of the news by combining the three aspects of news.
Abstract: In recent years, fake news detection has attracted a great deal of attention due to the myriad amounts of misinformation. Some previous methods have focused on modeling the news content, while others have combined user comments and user information on social media. However, existing methods ignore some important clues for detecting fake news, such as temporal information on social media and external knowledge related to the news. To this end, we propose a Knowledge-Aware Hierarchical Attention Network (KAHAN) that integrates this information into the model to establish fact-based associations with entities in the news content. Specifically, we introduce two hierarchical attention networks to model news content and user comments respectively, in which news content and user comments are represented by different aspects for modeling various degrees of semantic granularity. Besides, to process the random occurrences of user comments at post-level, we further designed a time-based subevent division algorithm to aggregate user comments at subevent-level to learn temporal patterns. Moreover, News towards Entities (N-E) attention and Comments towards Entities (C-E) attention are introduced to measure the importance of external knowledge. Finally, we detected the veracity of the news by combining the three aspects of news: content, user comments, and external knowledge. We conducted extensive experiments and ablation studies on two real-world datasets and showed that our proposed method outperformed the previous methods and empirically validated each component of KAHAN1.
3 citations
01 Jan 2022
3 citations
01 Jan 2023
TL;DR: The CheckThat! 2023 edition of the Check-That! lab zooms into some of the problems and offers five tasks in seven languages (Arabic, Dutch, English, German, Italian, Spanish, and Turkish): Task 1 asks to determine whether an item, text or a text plus an image, is check-worthy; Task 2 requires to assess whether a text snippet is subjective or not; Task 3 looks for estimating the political bias of a document or a news outlet; Task 4 requires to determine the level of factuality, and Task 5 is about identifying authorities that should be trusted to verify a contended claim as mentioned in this paper .
Abstract: The five editions of the CheckThat! lab so far have focused on the main tasks of the information verification pipeline: check-worthiness, evidence retrieval and pairing, and verification. The 2023 edition of the lab zooms into some of the problems and—for the first time—it offers five tasks in seven languages (Arabic, Dutch, English, German, Italian, Spanish, and Turkish): Task 1 asks to determine whether an item, text or a text plus an image, is check-worthy; Task 2 requires to assess whether a text snippet is subjective or not; Task 3 looks for estimating the political bias of a document or a news outlet; Task 4 requires to determine the level of factuality of a document or a news outlet; and Task 5 is about identifying authorities that should be trusted to verify a contended claim.
3 citations
References
More filters
[...]
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