A Survey of Fake News Zhou, Xinyi; Zafarani, Reza
ACM computing surveys,
09/2021, Volume:
53, Issue:
5
Journal Article
Peer reviewed
The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news detection and intervention. This survey reviews and evaluates methods ...that can detect fake news from four perspectives: the false
knowledge
it carries, its writing
style
, its
propagation
patterns, and the credibility of its
source
. The survey also highlights some potential research tasks based on the review. In particular, we identify and detail related fundamental theories across various disciplines to encourage interdisciplinary research on fake news. It is our hope that this survey can facilitate collaborative efforts among experts in computer and information sciences, social sciences, political science, and journalism to research fake news, where such efforts can lead to fake news detection that is not only efficient but, more importantly, explainable.
Failure propagation in power systems, and the possibility of becoming a cascading event, depend significantly on power system operating conditions. To make informed operating decisions that aim at ...preventing cascading failures, it is crucial to know the most probable failures based on operating conditions that are close to real-time conditions. In this paper, this need is addressed by developing a cascading failure model that is adaptive to different operating conditions and can quantify the impact of failed grid components on other components. With a three-step approach, the developed model enables predicting potential sequence of failures in a cascading failure, given system operating conditions. First, the interactions between system components under various operating conditions are quantified using the data collected offline, from a simulation-based failure model. Next, given measured line power flows, the most probable interactions corresponding to the system operating conditions are identified. Finally, these interactions are used to predict potential sequence of failures with a propagation tree model. The performance of the developed model under a specific operating condition is evaluated on both IEEE 30-bus and Illinois 200-bus systems, using various evaluation metrics such as Jaccard coefficient, F 1 score, Precision@K, and Kendall's tau.
Fake News Early Detection Zhou, Xinyi; Jain, Atishay; Phoha, Vir V. ...
Digital threats (Print),
07/2020, Volume:
1, Issue:
2
Journal Article
Peer reviewed
Open access
Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that ...aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this article, a theory-driven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level, and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.
COVID-19 has impacted all lives. To maintain social distancing and avoiding exposure, works and lives have gradually moved online. Under this trend, social media usage to obtain COVID-19 news has ...increased. Also, misinformation on COVID-19 is frequently spread on social media. In this work, we develop CHECKED, the first Chinese dataset on COVID-19 misinformation. CHECKED provides a total 2,104 verified microblogs related to COVID-19 from December 2019 to August 2020, identified by using a specific list of keywords. Correspondingly, CHECKED includes 1,868,175 reposts, 1,185,702 comments, and 56,852,736 likes that reveal how these verified microblogs are spread and reacted on Weibo. The dataset contains a rich set of multimedia information for each microblog including ground-truth label, textual, visual, temporal, and network information. Extensive experiments have been conducted to analyze CHECKED data and to provide benchmark results for well-established methods when predicting fake news using CHECKED. We hope that CHECKED can facilitate studies that target misinformation on coronavirus. The dataset is available at
https://github.com/cyang03/CHECKED
.
•We conduct the first study on the variation of friendship and popularity across sites.•As users join sites, their average number of friends converges to a value near 400.•User popularity converges ...to a mean and it cannot be increased by joining new sites.•Popularity patterns on previous sites can help determine user popularity on new sites.
Our social media experience is no longer limited to a single site. We use different social media sites for different purposes and our information on each site is often partial. By collecting complementary information for the same individual across sites, one can better profile users. These profiles can help improve online services such as advertising or recommendation across sites. To combine complementary information across sites, it is critical to understand how information for the same individual varies across sites. In this study, we aim to understand how two fundamental properties of users vary across social media sites. First, we study how user friendship behavior varies across sites. Our findings show how friend distributions for individuals change as they join new sites. Next, we analyze how user popularity changes across sites as individuals join different sites. We evaluate our findings and demonstrate how our findings can be employed to predict how popular users are likely to be on new sites they join.
The role of user profiles for fake news detection Shu, Kai; Zhou, Xinyi; Wang, Suhang ...
2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM),
08/2019
Conference Proceeding
Open access
Consuming news from social media is becoming increasingly popular. Social media appeals to users due to its fast dissemination of information, low cost, and easy access. However, social media also ...enables the widespread of fake news. Due to the detrimental societal effects of fake news, detecting fake news has attracted increasing attention. However, the detection performance only using news contents is generally not satisfactory as fake news is written to mimic true news. Thus, there is a need for an in-depth understanding on the relationship between user profiles on social media and fake news. In this paper, we study the problem of understanding and exploiting user profiles on social media for fake news detection. In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news. To exploit user profile features, we demonstrate the usefulness of these user profile features in a fake news classification task. We further validate the effectiveness of these features through feature importance analysis. The findings of this work lay the foundation for deeper exploration of user profile features of social media and enhance the capabilities for fake news detection.
A large body of research has focused on analyzing large networks and graphs. However, network and graph data is often anonymized for reasons such as protecting data privacy. Under such circumstances, ...it is difficult to verify the source of network data, which leads to questions such as: Given an anonymized graph, can we identify the network from which it is collected? Or, if one claims the graph is sampled from a certain network, can we verify this claim? The intuitive approach is to check for subgraph isomophism. However, subgraph isomophism is NP-complete; hence, infeasible for most large networks. Inspired by biometrics studies, we address these challenges by formulating two new problems: network identification and network authentication . To tackle these problems, similar to research on human fingerprints, we introduce two versions of a network identity : (1) embedding-based identity and (2) distribution-based identity. We demonstrate the effectiveness of these network identities using extensive experiments on real-world networks. Using these identities, we propose two approaches for network identification. One method uses supervised learning and can achieve an identification accuracy of 84.4 percent, and the other, which is easier to implement, relies on distances between identities and achieves an accuracy rate of 70.8 percent. For network authentication, we propose two methods to build a network authentication system. The first is a supervised learner and yields a low false accept rate and the other method, allows one to control the false reject rate with a reasonable false accept rate across networks. We demonstrate that network authentication can also be used for biometrics, authenticating users based on their touch data on phones and tablets. Our study can help identify or verify the source of network data, validate network-based research, and be used for network-based biometrics.
User Identification Across Social Media Zafarani, Reza; Tang, Lei; Liu, Huan
ACM transactions on knowledge discovery from data,
10/2015, Volume:
10, Issue:
2
Journal Article
Peer reviewed
People use various social media sites for different purposes. The information on each site is often partial. When sources of complementary information are integrated, a better profile of a user can ...be built. This profile can help improve online services such as advertising across sites. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identification problem. We provide evidence on the existence of a mapping among identities of individuals across social media sites, study the feasibility of finding this mapping, and illustrate and develop means for finding this mapping. Our studies show that effective approaches that exploit information redundancies due to users’ unique behavioral patterns can be utilized to find such a mapping. This study paves the way for analysis and mining across social networking sites, and facilitates the creation of novel online services across sites. In particular, recommending friends and advertising across networks, analyzing information diffusion across sites, and studying specific user behavior such as user migration across sites in social media are one of the many areas that can benefit from the results of this study.
Connecting users across social media sites Zafarani, Reza; Liu, Huan
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining,
08/2013
Conference Proceeding
People use various social media for different purposes. The information on an individual site is often incomplete. When sources of complementary information are integrated, a better profile of a user ...can be built to improve online services such as verifying online information. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identification problem. We introduce a methodology (MOBIUS) for finding a mapping among identities of individuals across social media sites. It consists of three key components: the first component identifies users' unique behavioral patterns that lead to information redundancies across sites; the second component constructs features that exploit information redundancies due to these behavioral patterns; and the third component employs machine learning for effective user identification. We formally define the cross-media user identification problem and show that MOBIUS is effective in identifying users across social media sites. This study paves the way for analysis and mining across social media sites, and facilitates the creation of novel online services across sites.
We report a strategy for the fabrication of a new type of electrochemical nanogap transducer. These nanogap devices are based on signal amplification by redox cycling. Using two steps of ...electron-beam lithography, vertical gold electrodes are fabricated side by side at a 70 nm distance encompassing a 20 attoliter open nanogap volume. We demonstrate a current amplification factor of 2.5 as well as the possibility to detect the signal of only 60 analyte molecules occupying the detection volume. Experimental voltammetry results are compared to calculations from finite element analysis.
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