Online Social Deception and Its Countermeasures for Trustworthy Cyberspace: A Survey

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 a trustworthy SNSs. In this paper, we conducted an extensive survey, covering (i) the multidisciplinary concepts 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 from existing literature. We conclude this survey paper with an in-depth discussions on the limitations of the state-of-the-art and recommend future research directions in this area.Comment: 35 pages, 8 figures, submitted to ACM Computing Survey

Attention-based LSTM network for rumor veracity estimation of tweets

YesTwitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM model is compared with several conventional machine and deep learning models. The proposed Attention-based LSTM model achieved an F1-score of 0.88 in classifying rumor and non-rumor tweets, which is better than the state-of-the-art results. The proposed system can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms

Modeling User Behavior With Interaction Networks for Spam Detection

Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from non-spammers. Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes. Learning to identify spammers in such a graph for a web-scale platform is challenging because of its structural complexity and size. In this paper, we propose SEINE (Spam DEtection using Interaction NEtworks), a spam detection model over a novel graph framework. Our graph simultaneously captures rich users' details and behavior and enables learning on a billion-scale graph. Our model considers neighborhood along with edge types and attributes, allowing it to capture a wide range of spammers. SEINE, trained on a real dataset of tens of millions of nodes and billions of edges, achieves a high performance of 80% recall with 1% false positive rate. SEINE achieves comparable performance to the state-of-the-art techniques on a public dataset while being pragmatic to be used in a large-scale production system.Comment: 6 pages, 2 figures, accepted to SIGIR 202

Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection

Much recent research has shed light on the development of the relation-dependent but content-independent framework for social spammer detection. This is largely because the relation among users is difficult to be altered when spammers attempt to conceal their malicious intents. Our study investigates the spammer detection problem in the context of multi-relation social networks, and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy. Specifically, we present the Multi-level Dependency Model (MDM). The MDM is able to exploit user's long-term dependency hidden in their relational sequences along with short-term dependency. Moreover, MDM fully considers short-term relational sequences from the perspectives of individual-level and union-level, due to the fact that the type of short-term sequences is multi-folds. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM on multi-relational social spammer detection

The Shapes of Cultures: A Case Study of Social Network Sites/Services Design in the U.S. and China

With growing popularity of the use of social network sites/services (SNSs) throughout the world, the global dominance of SNSs designed in the western industrialized countries, especially in the United Sates, seems to have become an inevitable trend. As internationalization has become a common practice in designing SNSs in the United States, is localization still a viable practice? Does culture still matter in designing SNSs? This dissertation aims to answer these questions by comparing the user interface (UI) designs of a U.S.-based SNS, Twitter, and a China-based SNS, Sina Weibo, both of which have assumed an identity of a “microblogging” service, a sub category of SNSs. This study employs the theoretical lens of the theory of technical identity, user-centered website cultural usability studies, and communication and media studies. By comparing the UI designs, or the “form,” of the two microblogging sites/services, I illustrate how the social functions of a technological object as embedded and expressed in the interface designs are preserved or changed as the technological object that has developed a relatively stable identity (as a microblogging site/service) in one culture is transferred between the “home” culture and another. The analysis in this study focuses on design elements relevant to users as members of networks, members of audience, and publishers/broadcasters. The results suggest that the designs carry disparate biases towards modes of communication and social affordances, which indicate a shift of the identity of microblogging service/site across cultures

Non-Hierarchical Networks for Censorship-Resistant Personal Communication.

The Internet promises widespread access to the world’s collective information and fast communication among people, but common government censorship and spying undermines this potential. This censorship is facilitated by the Internet’s hierarchical structure. Most traffic flows through routers owned by a small number of ISPs, who can be secretly coerced into aiding such efforts. Traditional crypographic defenses are confusing to common users. This thesis advocates direct removal of the underlying heirarchical infrastructure instead, replacing it with non-hierarchical networks. These networks lack such chokepoints, instead requiring would-be censors to control a substantial fraction of the participating devices—an expensive proposition. We take four steps towards the development of practical non-hierarchical networks. (1) We first describe Whisper, a non-hierarchical mobile ad hoc network (MANET) architecture for personal communication among friends and family that resists censorship and surveillance. At its core are two novel techniques, an efficient routing scheme based on the predictability of human locations anda variant of onion-routing suitable for decentralized MANETs. (2) We describe the design and implementation of Shout, a MANET architecture for censorship-resistant, Twitter-like public microblogging. (3) We describe the Mason test, amethod used to detect Sybil attacks in ad hoc networks in which trusted authorities are not available. (4) We characterize and model the aggregate behavior of Twitter users to enable simulation-based study of systems like Shout. We use our characterization of the retweet graph to analyze a novel spammer detection technique for Shout.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107314/1/drbild_1.pd