James Caverlee

TL;DR: A probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, which can overcome the sparsity of geo-enabled features in these services and enable new location-based personalized information services, the targeting of regional advertisements, and so on.

TL;DR: It is found that LSS users follow the “Levy Flight” mobility pattern and adopt periodic behaviors; while geographic and economic constraints affect mobility patterns, so does individual social status; and Content and sentiment-based analysis of posts associated with checkins can provide a rich source of context for better understanding how users engage with these services.

TL;DR: It is found that the deployed social honeypots identify social spammers with low false positive rates and that the harvested spam data contains signals that are strongly correlated with observable profile features (e.g., content, friend information, posting patterns, etc.).

TL;DR: This paper presents the first long-term study of social honeypots for tempting, profiling, and filtering content polluters in social media, and evaluates a wide range of features to investigate the effectiveness of automatic content polluter identification.

TL;DR: It is found that in the author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centraly measures but does significantly correlates with other measures.