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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21 Jun 2022
TL;DR: The results show that the word embedding pre-trained model (RoBERTa) can detect complex propaganda techniques and outperform the baseline by achieving an F1 score of 60.2%.
Abstract: The rapid evolution of technology and the Internet has enabled massive data to reach many audiences. People search for news, provide opinions, and discuss decisions online. Online news has become available in various news outlets, including websites and social networks. Consequently, several types of fake news have been raised, for instance, propaganda and rumors. Fake news leads to significant societal risks and has emerged as a major societal problem. Propaganda is spread over the Internet and appears in news articles that aim to manipulate people’s public opinion and influence their attitudes by using psychological and rhetorical methods that appeal to people’s feelings. This paper resolves one of the state-of-the-art NLP research; propaganda techniques classification. We apply the state-of-the-art pre-trained language model, RoBERTa, to detect propaganda techniques from online news articles. The model has been evaluated using a reference dataset for the SemEval-2020 Task 11. The results show that the word embedding pre-trained model (RoBERTa) can detect complex propaganda techniques and outperform the baseline by achieving an F1 score of 60.2%.
3 citations
01 Jan 2022
3 citations
01 Jan 2020
TL;DR: Three tasks run in Arabic, namely check-worthiness on tweets, evidence retrieval, and claim verification that corresponds respectively to task1, task3, and task4 are participated in, and manual sentiment features as well as named entities to detect fake news are integrated.
Abstract: Misinformation is a growing problem around the web. The spread of such a phenomenon may impact public opinions. Hence fake news detection is indispensable. The first step for fact-checking is the selection of check worthy tweets for a certain topic, then ranking sentences from related web pages according to the carried evidence. Afterward, the claim will be verified according to evident sentences. At CLEF2020 – CheckThat! lab, three tasks run in Arabic, namely check-worthiness on tweets, evidence retrieval, and claim verification that corresponds respectively to task1, task3, and task4. We participated in the three tasks. We integrated manual sentiment features as well as named entities to detect fake news. The integration of sentiment information in the first task caused result degradation since there may be an overlap between check worthy and not check worthy tweets. For the second task, we explored the effect of sentiment presence and we used cosine similarity as a similarity measure between the claim and a specific snippet. The third task is a classification task based on sentiment and linguistic features to compute the overlap and the contradiction between the claim and the detected check worthy sentences. The results of task1 and task3 leave large room for improvement, whereas the results of task 4 are promising since our system reached 0.55 of F1-measure.
3 citations
01 Jan 2021
TL;DR: This chapter explores the possible contribution of sentiment and intent information to machine learning-based stance detection on tweets by annotating a Turkish tweet dataset with sentiment and proprietary intent labels and performing stance detection experiments with SVM classifiers.
Abstract: Sentiment analysis, stance detection, and intent detection on social media texts are all significant research problems with several application opportunities. In this chapter, the authors explore the possible contribution of sentiment and intent information to machine learning-based stance detection on tweets. They first annotate a Turkish tweet dataset with sentiment and proprietary intent labels, where the dataset was already annotated with stance labels. Next, they perform stance detection experiments on the dataset using sentiment and intent labels as additional features. The experiments with SVM classifiers show that using sentiment and intent labels as additional features improves stance detection performance considerably. The final form of the dataset is made publicly available for research purposes. The findings reveal the contribution of sentiment and intent information to the solution of stance detection task on the Turkish tweet dataset employed. Yet, further studies on other datasets are needed to confirm that our findings are generalizable to other languages and on other topics.
3 citations
TL;DR: It can be concluded that in the real-life rumor control process, more resources need to be invested in reducing the rate of intentional transmission instead of being indiscriminately put on controlling all spreaders of rumors.
Abstract: The development of network technology has created various platforms and methods for information dissemination. When rumors spread in social networks, they will rapidly spread and may cause social harm. Also, there are groups in social networks that create and spread rumors for the purpose of profit, thus expanding the scope of rumors. Therefore, based on the theory of complex network propagation dynamics, the study of the propagation law of rumors and the design of effective prevention and control strategies is of practical importance and theoretical significance for understanding the propagation laws of rumors and controlling the outbreak of rumors. The spreading process of rumors on social network platforms is focused here. The intentional spreader based on the classic rumor-spreading model is introduced. First, 2SIR rumor-spreading models on homogeneous and heterogeneous networks are established, respectively. Second, the steady-state analysis was separately carried out, and the corresponding propagation critical value was obtained: in the homogeneous network, the condition for the large-scale spread of rumors is
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; in the heterogeneous network, the condition for the large-scale spread of rumors is
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. Finally, the simulation calculation and model feasibility verification were carried out on the model. The results show that the theoretical propagation threshold corresponds with the simulation results. According to the simulation results, the final influence of rumors has significantly decreased with decreasing values of
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(intentional spreading rate) instead of
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(unintentional spreading rate). It can be concluded that in the real-life rumor control process, more resources need to be invested in reducing the rate of intentional transmission instead of being indiscriminately put on controlling all spreaders of rumors.
3 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