Detecting nodal activities in dynamic social networks has strategic importance in many applications, such as online marketing campaigns and homeland security surveillance. How peerto-peer exchanges in social media can facilitate nodal activity detection is not well explored. Existing models assumes network nodes to be static in time and do not adequately consider features from social theories. This research developed and validated two theory-based models, Random Interaction Model (RIM) and Preferential Interaction Model (PIM), to characterize temporal nodal activities in social media networks of human agents. The models capture the network characteristics of randomness and preferential interaction due to community size, human bias, declining connection cost, and rising reachability. The models were compared against three benchmark models (abbreviated as EAM, TAM, and DBMM) using a social-media community consisting of 790,462 users who posted over 3,286,473 tweets and formed more than 3,055,797 links during 2013-2015. The experimental results show that both RIM and PIM outperformed EAM and TAM significantly in accuracy across different dates and time windows. Both PIM and RIM scored significantly smaller errors than DBMM did. Structural properties of social networks were found to provide a simple and yet accurate approach to predicting model performances. These results indicate the models' strong capability of accounting for user interactions in real-world social media networks and temporal activity detection. The research should provide new approaches for temporal network activity detection, develop relevant new measures, and report new findings from large social media datasets.

Effective use of online consumer reviews is hampered by uncertainty about their helpfulness. Despite emerging efforts in identifying antecedents of review helpfulness, they have largely overlooked rich semantic relationships embedded in online reviews. To address the literature gap, this study probes review text by uncovering semantic relationships among product features. We introduce three novel factors - breadth, depth, and redundancy, to gain a deep understanding of review helpfulness. Drawing on product uncertainty and information quality theories, we conceptualize and operationalize the proposed factors based on a semantic hierarchy of product features. The evaluation results on both experience and search goods lend strong support to those factors in improving both theoretical understanding and practical assessment of review helpfulness. Breadth and depth also offer new lens for explaining mixed findings about some other factors in the literature.

Under what conditions is the Internet more likely to be used maliciously for criminal activity? This study examines the conditions under which the Internet is associated with cybercriminal offenses. Using comprehensive state-level data in the United States during 2004-2010, our findings show that there is no clear empirical evidence that the Internet penetration rate is related to the number of Internet crime perpetrators; however, cybercriminal activities are contingent upon socioeconomic factors and connection speed. Specifically, a higher income, more education, a lower poverty rate, a lower unemployment rate, and a lower inequality are likely to make the Internet penetration be more positively related with cybercrime perpetrators, which are indeed different from the conditions of terrestrial crime in the real world. In addition, broadband connections are significantly and positively associated with Internet crime perpetrators, though narrowband connections are not. Taken together, cybercrime requires more than just a skilled perpetrator, and it requires an infrastructure to facilitate profiteering from the act. A relevant discussion is provided.