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

DISCRIMINATIVE TOPIC MINING FOR SOCIAL SPAM DETECTION

In the era of Social Web, there has been an explosive growth of user-contributed comments posted to various online social media. However, increasingly more misleading and deceptive user comments found at online social media have also been a great concern for consumers and merchants, and social spam have been brought to the attention by the legal circle in recent years. Social spam can cause tremendous loss to both consumers and merchants, and so there is a pressing need to design effective methodologies to detect social spam to maintain the hygiene of online social media. The main contribution of this paper is the illustration of a novel social spam detection methodology which combines word-, topic-, and user-based features to combat social spam. In particular, the proposed methodology is underpinned by the Labeled Latent Dirichlet Allocation (L-LDA) model, a kind of probabilistic generative model. A series of experiments conducted based on the social comments posted to YouTube show that our proposed methodology can achieve a detection accuracy of 91.17%. The business implication of our research is that merchants can apply our methodology to filter spam so as to extract accurate market intelligence from online social media. Moreover, social media site owners can leverage the proposed methodology to maintain the hygiene of their sites