Browsing by Author "Guo, Zhen"

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    Effect of Disinformation Propagation on Opinion Dynamics: A Game Theoretic Approach
    Guo, Zhen (IEEE, 2022-06-14)
    Disinformation can alter or manipulate our values, opinions, and rational decisions toward any life event because disinformation, such as fake news or rumors, is propagated rapidly and broadly in online social networks (OSNs). Game-theoretic models can help people maximize the benefits from dynamic social interactions. This work presents an opinion framework formulated by repeated, incomplete information games that model OSN users’ subjective opinions. The users may update their opinions using various criteria, such as uncertainty, homophily, encounter, herding, or assertion. We demonstrate how Subjective Logic, a belief model explicitly handling opinion uncertainty, can be employed to model attackers’ deception strategies, users’ opinion update models, and the influences of propagating disinformation through the interactions between users. Through extensive experiments, we investigated how an individual user’s information processing type can introduce different impacts on the extent of disinformation propagation. We compared the performance of the five different opinion update models under OSNs characterized by two real OSN datasets. We analyzed their impact on the choices of best strategies, their utilities, and network/opinion polarization. We also examined how the player’s choices of best strategies under uncertainty are different from Nash Equilibrium strategies based on correct beliefs towards their opponents’ moves.
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    Global Structure of the Mantle Transition Zone Discontinuities and Site Response Effects in the Atlantic and Gulf Coastal Plain
    Guo, Zhen (Virginia Tech, 2019-09-03)
    This thesis focuses on two different topics in seismology: imaging the global structures of the mantle transition zone discontinuities and studying the site response effects in the Atlantic and Gulf Coastal Plain. Global structures of the mantle transition zone discontinuities provide important constraints on thermal structures and dynamic processes in the mid mantle. In this thesis, global topographic structures of the 410- and 660-km discontinuities are obtained from finite-frequency tomography of SS precursors. The finite-frequency sensitivities of SS waves and precursors are calculated based on a single-scattering (Born) approximation and can be used for data selection. The new global models show a number of smaller-scale features that were absent in back-projection models. Good correlation between the mantle transition zone thickness and wave speed variations suggests dominantly thermal origins for the lateral variations in the transition zone. The high-resolution global models of the 410- and 660-km discontinuities in this thesis show strong positive correlation beneath western North America and eastern Asia subduction zones with both discontinuities occurring at greater depths. Wavespeed and anisotropy models support vertical variations in thermal structure in the mid mantle, suggesting return flows from the lower mantle occur predominantly in the vicinity of stagnant slabs and the region overlying the stagnant slabs. In oceanic regions, the two discontinuities show a weak anti-correlation, indicating the existence of a secondary global far-field return flow. The Atlantic and Gulf Coastal Plain is covered by extensive Cretaceous and Cenozoic marine sediments. In this thesis, the site response effects of sediments in the Coastal Plain region relative to the reference condition outside that region are investigated using Lg and coda spectral ratios. The high-frequency attenuation factors (kappa) in the Coastal Plain are strongly correlated with the sediment thickness. At frequencies between 0.1-2.86 Hz, the Lg spectral ratio amplitudes are modeled as functions of frequency and thickness of the sediments in the Coastal Plain. Analysis of the residuals from the stochastic ground motion prediction method suggests that incorporating the site response effects as functions of sediment thickness may improve ground motion prediction models for the Coastal Plain region.
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    Mitigating Influence of Disinformation Propagation Using Uncertainty-Based Opinion Interactions
    Guo, Zhen; Cho, Jin-Hee; Lu, Chang-Tien (IEEE, 2022-12)
    For decades, the spread of disinformation in online social networks (OSNs) has been a serious social issue. Disinformation via social media can easily mislead people's beliefs toward or against an event that may mislead their behaviors based on the misbeliefs. The game theory approaches have been proposed under dynamic settings to limit the adverse influences of disinformation. It is a challenge to expand the users' game strategies from the spreading decisions to the possible opinion updating choices. This work proposes a game-theoretic opinion framework that can formulate dynamic opinions by a belief model called Subjective Logic (SL) and provide opinion updates on five types of users' interactions on OSN platforms. The opinions are updated based on user choices and user types through the game interactions among legitimate users, attackers, and a defender in an OSN. Via the extensive simulation experiments, the effectiveness of the opinion models of five decision-makers (DMs) is analyzed in terms of users believing or disbelieving disinformation in an epidemic model with parameter optimization. Our results show that while homophily-based DMs (H-DMs) introduce the highest opinion polarization, uncertainty-based DMs (U-DMs) can effectively filter untrustworthy users propagating disinformation.
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    Online Social Deception and Its Countermeasures: A Survey
    Guo, Zhen; Cho, Jin-Hee; Chen, Ing-Ray; Sengupta, Srijan; Hong, Michin; Mitra, Tanushree (2021-01-05)
    We are living in an era when online communication over social network services (SNSs) have become an indispensable part of people's everyday lives. As a consequence, online social deception (OSD) in SNSs has emerged as a serious threat in cyberspace, particularly for users vulnerable to such cyberattacks. Cyber attackers have exploited the sophisticated features of SNSs to carry out harmful OSD activities, such as financial fraud, privacy threat, or sexual/labor exploitation. Therefore, it is critical to understand OSD and develop effective countermeasures against OSD for building trustworthy SNSs. In this paper, we conduct an extensive survey, covering 1) the multidisciplinary concept of social deception; 2) types of OSD attacks and their unique characteristics compared to other social network attacks and cybercrimes; 3) comprehensive defense mechanisms embracing prevention, detection, and response (or mitigation) against OSD attacks along with their pros and cons; 4) datasets/metrics used for validation and verification; and 5) legal and ethical concerns related to OSD research. Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons learned from the existing literature. We conclude our survey with in-depth discussions on the limitations of the state-of-the-art and suggest future research directions in OSD research.
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    Privacy-Preserving and Diversity-Aware Trust-based Team Formation in Online Social Networks
    Mahajan, Yash; Guo, Zhen; Cho, Jin-Hee; Chen, Ing-Ray (2023-02)
    As online social networks (OSNs) become more prevalent, a new paradigm for problem solving through crowdsourcing has emerged. By leveraging the OSN platforms, users can post a problem to be solved and then form a team to collaborate and solve the problem. A common concern in OSNs is how to form effective collaborative teams, as various tasks are completed through online collaborative networks. A team’s diversity in expertise has received high attention to producing high team performance in developing team formation (TF) algorithms. However, the effect of team diversity on performance under different types of tasks has not been extensively studied. Another important issue is how to balance the need to preserve individuals’ privacy with the need to maximize performance through active collaboration, as these two goals may conflict with each other. This research has not been actively studied in the literature. In this work, we develop a team formation (TF) algorithm in the context of OSNs that can maximize team performance and preserve team members’ privacy under different types of tasks. Our proposed PRivAcy-Diversity-Aware Team Formation framework, called PRADA-TF, is based on trust relationships between users in OSNs where trust is measured based on a user’s expertise and privacy preference levels. The PRADA-TF algorithm considers the team members’ domain expertise, privacy preferences, and the team’s expertise diversity in the process of team formation. We leverage Mechanism Design as a game-theoretic technique in which the mechanism designer plays the role of team leader in forming a team. We use two realworld datasets (i.e., Netscience and IMDb) to generate different semi-synthetic datasets for constructing trust networks using a belief model (i.e., Subjective Logic) and identifying trustworthy users as candidate team member. We evaluate the effectiveness of our proposed PRADA-TF scheme in four variants against three baseline methods in the literature. Our analysis focuses on three performance metrics used in the study of OSNs: social welfare, privacy loss, and team diversity.
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    SERI: Generative Chatbot Framework for Cybergrooming Prevention
    Wang, Pei; Guo, Zhen; Huang, Lifu; Cho, Jin-Hee (2021-11-11)
    Cybergrooming refers to a crime to lure potential victims, particularly youth, by establishing personal trust relationships with them for sexual abuse or exploitation. Although cybergrooming is recognized as one of the serious social issues, there has been a lack of proactive programs to protect the youth. In this paper, we present a generative chatbot framework, called SERI Stop cybERgroomIng), that can generate authentic conversations between a perpetrator chatbot and a potential victim chatbot. The SERI is designed to provide a safe and authentic environment for enhancing youth's sensitivity and awareness of subtle cues of cybergrooming without exposing unnecessary ethical issues caused by potentially offensive or upsetting languages. The SERI is developed as a pre-stage before the perpetrator chatbot is deployed to chatting with an actual human youth user to observe how the youth user can respond to a stranger or acquaintance asking for sensitive or private information. Hence, to evaluate the quality of the conversations generated by the SERI, we use open-source, referenced, and unreferenced metrics to assess the generated conversations automatically. In addition, we evaluated the quality of the conversation based on the human evaluation method. Our results show that the SERI can generate authentic conversations between the two chatbots compared to the original conversations from the used dataset in perplexity and MaUde scores.
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    A study of site response in the Longmen Shan and adjacent regions and site response models for the Sichuan Basin
    Guo, Zhen; Chapman, Martin C. (Frontiers, 2023-01-05)
    We investigated the regional attenuation and site responses in the Sichuan Basin and adjacent Songpan-Ganze terrane of the Tibetan Plateau using seismic data recorded at 41 stations from regional earthquakes occurring between January 2009 and October 2020. Fourier amplitude spectra of Lg waves were computed and binned into 18 frequency bins with center frequencies ranging from 0.1 Hz to 20.4 Hz. The quality factor is estimated as Q (f) = 313f (0.74) for the Sichuan Basin and Q (f) = 568f (0.34) for the Songpan-Ganze terrane, reflecting significant differences in the crustal structure beneath these two regions. Relative to the Songpan-Ganze terrane, site responses in the Sichuan Basin are characterized by strong amplification effects at frequencies lower than 6 Hz and obvious attenuation at higher frequencies (> 10 Hz). kappa(0) of stations in the Sichuan Basin show clearly geographical dependence with an average value of 0.045 s, whereas stations in the Songpan-Ganze terrane generally have smaller kappa(0) values with an average value of 0.028 s. In particular, site response and kappa(0) of stations in the Sichuan Basin are found to be dependent on the geographically variable thickness of the sedimentary deposits (sediment thickness). These units are comprised of sedimentary rock and semi-consolidated sediments, with a maximum thickness reaching approximately 10 km. Site response terms in the Sichuan Basin derived from the Lg Fourier spectra exhibit consistent patterns versus sediment thickness as frequency increases. We developed site response models as functions of sediment thickness for stations in the Sichuan Basin. The site response model derived from Lg site terms is consistent with that based on site response terms from coda amplitude spectra and horizontal to vertical (H/V) spectral ratios. The models were then incorporated in the stochastic method of ground motion predictions in the Sichuan Basin for six earthquakes occurring between October 2020 and June 2022. Residual analysis suggests that incorporating the site response models as functions of sediment thickness can improve the ground motion prediction model for the Sichuan Basin from moderate earthquakes.
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    Text Mining-based Social-Psychological Vulnerability Analysis of Potential Victims To Cybergrooming: Insights and Lessons Learned
    Guo, Zhen; Wang, Pei; Cho, Jin-Hee; Huang, Lifu (ACM, 2023-05)
    Cybergrooming is a serious cybercrime that primarily targets youths through online platforms. Although reactive predator detection methods have been studied, proactive victim protection and crime prevention can also be achieved through vulnerability analysis of potential youth victims. Despite its significance, vulnerability analysis has not been thoroughly studied in the data science literature, while several social science studies used survey-based methods. To address this gap, we investigate humans’ social-psychological traits and quantify key vulnerability factors to cybergrooming by analyzing text features in the Linguistic Inquiry and Word Count (LIWC). Through pairwise correlation studies, we demonstrate the degrees of key vulnerability dimensions to cybergrooming from youths’ conversational features. Our findings reveal that victims have negative correlations with family and community traits, contrasting with previous social survey studies that indicated family relationships or social support as key vulnerability factors. We discuss the current limitations of text mining analysis and suggest cross-validation methods to increase the validity of research findings. Overall, this study provides valuable insights into understanding the vulnerability factors to cybergrooming and highlights the importance of adopting multidisciplinary approaches.
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    Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information
    Guo, Zhen; Zhang, Qi; An, Xinwei; Zhang, Qisheng; Josang, Audun; Kaplan, Lance M.; Chen, Feng; Jeong, Dong H.; Cho, Jin-Hee (2023-02-13)
    Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches. To this end, we propose an intent classification framework that can best identify the correct intent of fake news. We will leverage deep reinforcement learning (DRL) that can optimize the structural representation of each tweet by removing noisy words from the input sequence when appending an actor to the long short-term memory (LSTM) intent classifier. Policy gradient DRL model (e.g., REINFORCE) can lead the actor to a higher delayed reward. We also devise a new uncertainty-aware immediate reward using a subjective opinion that can explicitly deal with multidimensional uncertainty for effective decision-making. Via 600K training episodes from a fake news tweets dataset with an annotated intent class, we evaluate the performance of uncertainty-aware reward in DRL. Evaluation results demonstrate that our proposed framework efficiently reduces the number of selected words to maintain a high 95% multi-class accuracy.
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    Understanding and Combating Online Social Deception
    Guo, Zhen (Virginia Tech, 2023-05-02)
    In today's world, online communication through social network services (SNSs) has become an essential aspect of people's daily lives. As social networking sites (SNSs) have become more sophisticated, cyber attackers have found ways to exploit them for harmful activities such as financial fraud, privacy violations, and sexual or labor exploitation. Thus, it is imperative to gain an understanding of these activities and develop effective countermeasures to build SNSs that can be trusted. The existing approaches have focused on discussing detection mechanisms for a particular type of online social deception (OSD) using various artificial intelligence (AI) techniques, including machine/deep learning (ML/DL) or text mining. However, fewer studies exist on the prevention and response (or mitigation) mechanisms for effective defense against OSD attacks. Further, there have been insufficient efforts to investigate the underlying intents and tactics of those OSD attackers through their in-depth understanding. This dissertation is motivated to take defense approaches to combat OSD attacks through the in-depth understanding of the psychological-social behaviors of attackers and potential victims, which can effectively guide us to take more proactive action against OSD attacks which can minimize potential damages to the potential victims as well as be cost-effective by minimizing or saving recovery cost. In this dissertation, we examine the OSD attacks mainly through two tasks, including understanding their causes and combating them in terms of prevention, detection, and mitigation. In the OSD understanding task, we investigate the intent and tactics of false informers (e.g., fake news spreaders) in propagating fake news or false information. We understand false informers' intent more accurately based on intent-related phrases from fake news contexts to decide on effective and efficient defenses (or interventions) against them. In the OSD combating task, we develop the defense systems following two sub-tasks: (1) The social capital-based friending recommendation system to guide OSN users to choose trustworthy users to defend against phishing attackers proactively; and (2) The defensive opinion update framework for OSN users to process their opinions by filtering out false information. The schemes proposed for combating OSD attacks contribute to the prevention, detection, and mitigation of OSD attacks.