Internet is one of the important inventions and a large number of persons are its users. These persons use this for different purposes. There are different social media platforms that are accessible to these users. Any user can make a post or spread the news through the online platforms. These platforms do not verify the users or their posts. So some of the users try to spread fake news through these platforms. These news can be propaganda against an individual, society, organization or political party. A human being is unable to detect all these fake news. So there is a need for machine learning classifiers that can detect these fake news automatically. Use of machine learning classifiers for detecting fake news is described in this systematic literature review.
St. Joseph’s College（圣约瑟夫学院）；Queen’s University Belfast（贝尔法斯特女王大学）
Data science research into fake news has gathered much momentum in recent years, arguably facilitated by the emergence of large public benchmark datasets. While it has been well-established within media studies that gender bias is an issue that pervades news media, there has been very little exploration into the relationship between gender bias and fake news. In this work, we provide the first empirical analysis of gender bias vis-a-vis fake news, leveraging simple and transparent lexicon-based methods over public benchmark datasets. Our analysis establishes the increased prevalance of gender …
NARA Institute of Science and Technology（奈良先端科学技术大学）
The rapid increase in fake news, which causes significant damage to society, triggers many fake news related studies, including the development of fake news detection and fact verification techniques. The resources for these studies are mainly available as public datasets taken from Web data. We surveyed 118 datasets related to fake news research on a large scale from three perspectives: (1) fake news detection, (2) fact verification, and (3) other tasks; for example, the analysis of fake news and satire detection. We also describe in detail their utilization tasks and their characteristics. F…
Disinformation through fake news is an ongoing problem in our society and has become easily spread through social media. The most cost and time effective way to filter these large amounts of data is to use a combination of human and technical interventions to identify it. From a technical perspective, Natural Language Processing (NLP) is widely used in detecting fake news. Social media companies use NLP techniques to identify the fake news and warn their users, but fake news may still slip through undetected. It is especially a problem in more localised contexts (outside the United States of A…
Florida State University（佛罗里达州立大学）；Jilin University（吉林大学）
The explosive growth of fake news along with destructive effects on politics, economy, and public safety has increased the demand for fake news detection. Fake news on social media does not exist independently in the form of an article. Many other entities, such as news creators, news subjects, and so on, exist on social media and have relationships with news articles. Different entities and relationships can be modeled as a heterogeneous information network (HIN). In this paper, we attempt to solve the fake news detection problem with the support of a news-oriented HIN. We propose a novel fak…
The rise of fake news in the past decade has brought with it a host of consequences, from swaying opinions on elections to generating uncertainty during a pandemic. A majority of methods developed to combat disinformation either focus on fake news content or malicious actors who generate it. However, the virality of fake news is largely dependent upon the users who propagate it. A deeper understanding of these users can contribute to the development of a framework for identifying users who are likely to spread fake news. In this work, we study the characteristics and motivational factors of fa…
SANKEN Osaka University（大阪大学产业科学研究所）；NARA Institute of Science and Technology（奈良先端科学技术大学院大学）
Fake news provokes many societal problems; therefore, there has been extensive research on fake news detection tasks to counter it. Many fake news datasets were constructed as resources to facilitate this task. Contemporary research focuses almost exclusively on the factuality aspect of the news. However, this aspect alone is insufficient to explain "fake news," which is a complex phenomenon that involves a wide range of issues. To fully understand the nature of each instance of fake news, it is important to observe it from various perspectives, such as the intention of the false news dissemin…
The fake news epidemic makes it imperative to develop a diagnostic framework that is both parsimonious and valid to guide present and future efforts in fake news detection. This paper represents one of the very first attempts to fill a void in the research on this topic. The LeSiE (Lexical Structure, Simplicity, Emotion) framework we created and validated allows lay people to identify potential fake news without the use of calculators or complex statistics by looking out for three simple cues.
Recent years have witnessed remarkable progress towards computational fake news detection. To mitigate its negative impact, we argue that it is critical to understand what user attributes potentially cause users to share fake news. The key to this causal-inference problem is to identify confounders -- variables that cause spurious associations between treatments (e.g., user attributes) and outcome (e.g., user susceptibility). In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. Learning such…
Fake news is an age-old phenomenon, widely assumed to be associated with political propaganda published to sway public opinion. Yet, with the growth of social media, it has become a lucrative business for Web publishers. Despite many studies performed and countermeasures proposed, unreliable news sites have increased in the last years their share of engagement among the top performing news sources. Stifling fake news impact depends on our efforts in limiting the (economic) incentives of fake news producers. In this paper, we aim at enhancing the transparency around these exact incentives, and …
Large numbers of people use Social Networking Services (SNS) for easy access to various news, but they have more opportunities to obtain and share ``fake news'' carrying false information. Partially to combat fake news, several fact-checking sites such as Snopes and PolitiFact have been founded. Nevertheless, these sites rely on time-consuming and labor-intensive tasks. Moreover, their available languages are not extensive. To address these difficulties, we propose a new fake news collection system based on rule-based (unsupervised) frameworks that can be extended easily for various languages.…
Many people use social networking services (SNSs) to easily access various news. There are numerous ways to obtain and share ``fake news,'' which are news carrying false information. To address fake news, several studies have been conducted for detecting fake news by using SNS-extracted features. In this study, we attempt to use temporal features generated from SNS posts by using a point process algorithm to identify fake news from real news. Temporal features in fake news detection have the advantage of robustness over existing features because it has minimal dependence on fake news propagato…
The availability and interactive nature of social media have made them the primary source of news around the globe. The popularity of social media tempts criminals to pursue their immoral intentions by producing and disseminating fake news using seductive text and misleading images. Therefore, verifying social media news and spotting fakes is crucial. This work aims to analyze multi-modal features from texts and images in social media for detecting fake news. We propose a Fake News Revealer (FNR) method that utilizes transform learning to extract contextual and semantic features and contrastiv…
The wide spread of fake news in social networks is posing threats to social stability, economic development and political democracy etc. Numerous studies have explored the effective detection approaches of online fake news, while few works study the intrinsic propagation and cognition mechanisms of fake news. Since the development of cognitive science paves a promising way for the prevention of fake news, we present a new research area called Cognition Security (CogSec), which studies the potential impacts of fake news to human cognition, ranging from misperception, untrusted knowledge acquisi…
Fake news can have a significant negative impact on society because of the growing use of mobile devices and the worldwide increase in Internet access. It is therefore essential to develop a simple mathematical model to understand the online dissemination of fake news. In this study, we propose a point process model of the spread of fake news on Twitter. The proposed model describes the spread of a fake news item as a two-stage process: initially, fake news spreads as a piece of ordinary news; then, when most users start recognizing the falsity of the news item, that itself spreads as another …
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 lear…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Renmin University of China（中国人民大学）；Illinois Institute of Technology（伊利诺伊理工学院）
Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotions of news comments aroused in the crowd (i.e., social emotion) should not be ignored. Furthermore, it remains to be explored whether there exists a relationship between publisher emotion and social emotion (i.e., dual emotion), and how the dual emotion appears in fake news. In t…
Laguna Blanca School（拉古纳布兰卡学校）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
Fake news has altered society in negative ways in politics and culture. It has adversely affected both online social network systems as well as offline communities and conversations. Using automatic machine learning classification models is an efficient way to combat the widespread dissemination of fake news. However, a lack of effective, comprehensive datasets has been a problem for fake news research and detection model development. Prior fake news datasets do not provide multimodal text and image data, metadata, comment data, and fine-grained fake news categorization at the scale and breadt…
LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendation
Media Synthesis and Forensics Lab, Institute of Computing Technology, Chinese Academy of Sciences（媒体合成与取证实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co, we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to unco…
Nowadays fake news are heavily discussed in public and political debates. Even though the phenomenon of intended false information is rather old, misinformation reaches a new level with the rise of the internet and participatory platforms. Due to Facebook and Co., purposeful false information - often called fake news - can be easily spread by everyone. Because of a high data volatility and variety in content types (text, images,...) debunking of fake news is a complex challenge. This is especially true for automated approaches, which are prone to fail validating the veracity of the information…
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences（中国科学院智能信息处理重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones, existing methods observe the language patterns of the news post and "zoom in" to verify its content with knowledge sources or check its readers' replies. However, these methods neglect the information in the external news environment where a fake news post is created and disseminated. The news environment represents recent mainstream media opinion and public attention, which is an important inspiration of fake news fabrication because fake news is often …
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）
Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present liar: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking re…
Texas A&M University（德州农工大学）；The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
The spread of digital disinformation (aka "fake news") is arguably one of the most significant threats on the Internet which can cause individual and societal harm of large scales. The susceptibility to fake news attacks hinges on whether Internet users perceive a fake news article/snippet to be legitimate after reading it. In this paper, we attempt to garner an in-depth understanding of users' susceptibility to text-centric fake news attacks via a neuro-cognitive methodology. We investigate the neural underpinnings relevant to fake/real news through EEG. We run an experiment with human users …
Fake news that manipulates political elections, strikes financial systems, and even incites riots is more viral than real news online, resulting in unstable societies and buffeted democracy. The easier contagion of fake news online can be causally explained by the greater anger it carries. The same results in Twitter and Weibo indicate that this mechanism is independent of the platform. Moreover, mutations in emotions like increasing anger will progressively speed up the information spread. Specifically, increasing the occupation of anger by 0.1 and reducing that of joy by 0.1 will produce nea…
Nowadays, artificial intelligence algorithms are used for targeted and personalized content distribution in the large scale as part of the intense competition for attention in the digital media environment. Unfortunately, targeted information dissemination may result in intellectual isolation and discrimination. Further, as demonstrated in recent political events in the US and EU, malicious bots and social media users can create and propagate targeted `fake news' content in different forms for political gains. From the other direction, fake news detection algorithms attempt to combat such prob…
Fake news on Social Media undermines democratic institutions and processes. Especially since 2016, researchers from many disciplines have focussed on ways to address the phenomenon. Much of the research focus to date has been on identification and understanding the nature of the phenomenon in and between social networks and of a rather reactive nature. We propose interventions that focus on individual user empowerment, and social media structural change that is prophylactic (pre exposure), rather than therapeutic (post exposure) with the goal of reducing the population exposed to fake news. We…
In this paper, we demonstrate Untrue News, a new search engine for fake stories. Untrue News is easy to use and offers useful features such as: a) a multi-language option combining fake stories from different countries and languages around the same subject or person; b) an user privacy protector, avoiding the filter bubble by employing a bias-free ranking scheme; and c) a collaborative platform that fosters the development of new tools for fighting disinformation. Untrue News relies on Elasticsearch, a new scalable analytic search engine based on the Lucene library that provides near real-time…
The emergence of "Fake News" and misinformation via online news and social media has spurred an interest in computational tools to combat this phenomenon. In this paper we present a new "Related Fact Checks" service, which can help a reader critically evaluate an article and make a judgment on its veracity by bringing up fact checks that are relevant to the article. We describe the core technical problems that need to be solved in building a "Related Fact Checks" service, and present results from an evaluation of an implementation.
FakingRecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Fake news, false or misleading information presented as news, has a significant impact on many aspects of society, such as in politics or healthcare domains. Due to the deceiving nature of fake news, applying Natural Language Processing (NLP) techniques to the news content alone is insufficient. The multi-level social context information (news publishers and engaged users in social media) and temporal information of user engagement are important information in fake news detection. The proper usage of this information, however, introduces three chronic difficulties: 1) multi-level social contex…
