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 (i) the multidisciplinary concept of social deception; (ii) types of OSD attacks and their unique characteristics compared to other social network attacks and cybercrimes; (iii) comprehensive defense mechanisms embracing prevention, detection, and response (or mitigation) against OSD attacks along with their pros and cons; (iv) datasets/metrics used for validation and verification; and (v) 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.
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). Gametheoretic 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.
The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals’ and communities’ thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of
The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals' and communities' thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions.
Motivation: De novo motif discovery in biological sequences is always an important and computationally challenging problem. In the past 20 years, a myriad of algorithms have been proposed to solve this problem with varying success. Ensemble algorithms, which combine different individual algorithms, have been introduced in previous studies, and it has been proved that an ensemble strategy can improve the prediction accuracy. However, the performance of these tools has not yet met most people's expectation. One reason for the low performance is failure to adapt to complicated and large data sets. Another existing problem is that fewer motif finding tools are available, and many of them are not maintained. Results: I present a novel and fast tool MCAT (Motif Combining and Association Tool) for de novo motif discovery by combining six state-of-the-art motif discovery tools (MEME, BioProspector, DECOD, XXmotif, Weeder, and CMF). In addition, I developed an innovative motif combining algorithm, VoteRank, which is a position based algorithm that votes, ranks, and combines candidate motifs. By testing against DNA sequences from budding yeast, fission yeast, human, fruit fly, and mouse, I showed that MCAT is able to identify exact match motifs in DNA sequences efficiently and achieves at least 30% improvement in prediction accuracy. I am thankful to all of my group members and former colleagues, Jeff Robertson, Zhen Guo, Christy Coghlan, and Jake Martinez for helping with the MCAT project, Doaa Altarawy for her advice at the beginning of my research, Haitham Elmarakeby for the Beacon project, and Xiao Liang for her valuable ideas and encouragement during my research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.