Socio-computational Methods to Detect and Predict Bot Activity in Novel Information Environments Navy STTR FY2015.A Sol No.: Navy STTR FY2015.A Topic No.: N15A-T020 Topic Title: Socio-computational Methods to Detect and Predict Bot Activity in Novel Information Environments Proposal No.: N15A-020-0195 Firm: Intelligent Automation, Inc. 15400 Calhoun Drive Suite 190 Rockville, Maryland 20855 Contact: Onur Savas Phone: (301) 294-4241 Abstract: Intelligent Automation, Inc. (IAI) proposes to understand social bots' behaviors, extract indicators, develop socio-computational models with predictive capabilities to detect bot activity, and implement them in a mature social media analytics software tool. Our approach will use predictive socio-computational models that exploit context, user, friends, temporal, and network features of social media users. Our models will be matured to further understand emerging sociotechnical behaviors for conflict monitoring and social bot's activities from organizational and tactical perspectives. We will exploit adaptive machine learning to efficiently refine our models as bot behaviors and social media landscape change over time. By identifying correlation of bot detection and other social media analytics (e.g., influence detection, community detection), we will enhance bot detection by (i) identifying top propaganda disseminators and polarizers, and (ii) extracting influential coordination structures within social bots. The uncertainty and trustworthiness of analytical results will also be computed, and interactive visualization will further help the analysts to drill down and filter. The models and algorithms will then be implemented and integrated with IAI's social media analytics tool that provides advanced analytics capabilities, search, and visualization in a Data Science as a Service (DSaaS) framework. Benefits: The implementation of bot detection will enable the intelligence analyst to assist in modern cybertechnological warfare. Our solution will shed further light on the mechanisms of socio-digital influence on information dissemination in the modern communication technology landscape, which is very important for understanding the modern information conflicts, especially in environments of social instability with limited observatory capabilities (war zones, revolutions etc.). The implemented algorithms will be integrated with IAI's social media analytics tool, namely Scraawl. To commercialize the technology, IAI will investigate subscription to Scraawl with bot detection capabilities to the interested parties. For the commercial side, social media sites such as Twitter and Facebook can use bot detection in form of spam filtering is an integral part of online social network services. Return