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Social and Behavioral Sciences Commons™
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Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
Web 2.0 Use And Knowledge Transfer: How Social Media Technologies Can Lead To Organizational Innovation, Namjoo Choi, Kuang-Yuan Huang, Aaron Palmer, Lenore Horowitz
Web 2.0 Use And Knowledge Transfer: How Social Media Technologies Can Lead To Organizational Innovation, Namjoo Choi, Kuang-Yuan Huang, Aaron Palmer, Lenore Horowitz
Information Science Faculty Publications
The concept of Web 2.0 has gained widespread prominence in recent years. The use of Web 2.0 applications on an individual level is currently extensive, and such applications have begun to be implemented by organizations in hopes of boosting collaboration and driving innovation. Despite this growing trend, only a small number of theoretical perspectives are available in the literature that discuss how such applications could be utilized to assist in innovation. In this paper, we propose a theoretical model explicating this phenomenon. We argue that organizational Web 2.0 use fosters the emergence and enhancement of informal networks, weak ties, boundary …
Online Deception In Social Media, Michail Tsikerdekis, Sherali Zeadally
Online Deception In Social Media, Michail Tsikerdekis, Sherali Zeadally
Information Science Faculty Publications
The unknown and the invisible exploit the unwary and the uninformed for illicit financial gain and reputation damage.
Multiple Account Identity Deception Detection In Social Media Using Nonverbal Behavior, Michail Tsikerdekis, Sherali Zeadally
Multiple Account Identity Deception Detection In Social Media Using Nonverbal Behavior, Michail Tsikerdekis, Sherali Zeadally
Information Science Faculty Publications
Identity deception has become an increasingly important issue in the social media environment. The case of
blocked users initiating new accounts, often called sockpuppetry, is widely known and past efforts, which have attempted to detect such users, have been primarily based on verbal behavior (e.g., using profile data or lexic al features in text). Although these methods yield a high detection accuracy rate, they are computationally inefficient for the social media environment, which often involves databases with large volumes of data. To date, little attention has been paid to detecting online decep- tion using nonverbal behavior. We present a detection …