Research Interest: study human behavior on the Web via an interdisciplinary approach of Computer and Social Sciences, for addressing a fundamental problem of information overload. I model individual and group behavior (e.g., intent, comprehension, attitude and engagement) in online communities to design cooperative information systems that mine crowd-generated and opensource data to address information overload for supporting organizational decision making. This research is employed into a variety of informatics problems for social good in smart communities, including emergency management, gender-based violence, public health resilience, and cyber defense.
Technical Interest: merge top-down and bottom-up data mining approaches by employing domain semantics and guidance from offline socio-behavioral knowledge into statistical methods and understand individual and group behavior from unstructured, large-scale social, open Web and IoT data.
Check relevant projects of Humanitarian & Social Informatics Lab (HSIL) research group.
Studying real-world problems
related to online human social dynamics (engagement, intent, belief, cooperation) in social network communities by leveraging computational models inspired by domain semantics and behavioral knowledge from psychology (Social Identity, Social Cohesion, etc.) My research lies across the areas of Intent Mining, Bipartite Matching, Expert Detection, Community Evolution, and User Engagement analysis using large-scale unstructured data from social media and Web. My research has key applications in improving humanitarian crisis coordination and the understanding of online user behavior.
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