For June's Social Data Analytics- DC, we're very happy to partner with our friends at Data Science DC (http://www.meetup.com/Data-Science-DC/events/222556386/) to present two speakers from the University of Maryland working at the cutting edge of the analysis of behavior in social media. Hadi Amiri will talk about predicting interactions between brands and consumers, while Bill Rand will talk about fitting agent-based and other models to Twitter data.
You can sign up for this event either here or at DSDC's Meetup page, but please don't register in both places.
• 6:30pm -- Networking, Empanadas, and Refreshments
• 7:00pm -- Introduction, Announcements, Give-aways
• 7:15pm -- Presentations and Discussion
• 8:30pm -- Data Drinks (Tonic, 2036 G St NW)
Brandtology in Social Media
User generated contents in social media platforms provide important and timely indicators on the spontaneous and often genuine views of users, fans, and customers on a wide range of topics. It is thus invaluable to obtain actionable insights from such live streaming contents. In this talk, I introduce Brandtology in social media: the science of studying brands, customers, and the interaction between the two in the context of social networks. I will talk about two the issues of predictability in Brandtology namely churn prediction and emerging topic detection.
Hadi Amiri (http://www.umiacs.umd.edu/~hadi/index.html) is currently a Postdoc at the University of Maryland, Institute for Advanced Computer Studies (UMIACS). He is affiliated with the Computational Linguistics & Information Processing (CLIP) lab. His primary research interests are in the areas of Social Media Analysis and Natural Language Processing, and his current work centers on understanding exposition in the context of social networks. He received his PhD from the National University of Singapore in 2013 and worked as Research Scientist at the Institute for Infocomm Research (I2R) from[masked]. Follow Hadi on Twitter @amirieb (https://twitter.com/amirieb).
Using Big Data and Agent-Based Modeling to Understand and Predict Social Media Diffusion
With the increasing abundance of `digital footprints' left by human interactions in online environments, e.g., social media and app use, the ability to model such behavior has become increasingly possible. Many approaches have been proposed, however, most previous model frameworks are fairly restrictive, and often the models are not directly compared on a diverse collection of human behavior. We will
explore a new modeling approach that enables the creation of models directly from data with no previous restrictions on the data. We will examine this modeling framework in the context of predictive and descriptive abilities on a heterogeneous catalog of human behavior collected from fifteen thousand users on Twitter. We find that despite
the popularity of exogenous drive-type models, for explaining digitally-mediated human behavior, most users are better modeled using self- or socially-driven models. Our work highlights the importance of a flexible modeling approach when attempting to explain and predict
human behavior in digital environments.
William Rand (http://www.rhsmith.umd.edu/directory/william-rand) examines the use of computational modeling techniques, like agent-based modeling, geographic information systems, social network analysis, and machine learning, to help understand and analyze complex systems, such as the diffusion of innovation, organizational learning, and economic markets. He serves as the Director of the
Center for Complexity in Business, the first academic research center focused solely on the application of complex systems techniques to business applications and management science. Over the course of his research experience, he has used computer models to help understand a large variety of complex systems, such as the evolution of cooperation, suburban sprawl, traffic patterns, financial systems, land-use and land-change in urban systems, and many other phenomena. He has recently received research awards from DARPA, Google, WPP, the
National Science Foundation and the Marketing Science Institute. Follow Bill on Twitter @billrand (https://twitter.com/billrand).
This event is sponsored by the GWU Department of Decision Sciences (http://business.gwu.edu/about-us/departments/decision-sciences/), Statistics.com (http://bit.ly/12YljkP), Elder Research (http://datamininglab.com/), Booz Allen Hamilton (https://www.boozallen.com/consulting/strategic-innovation/nextgen-analytics-data-science), Twitter (http://www.twitter.com), the Big Boulder Initiative (http://www.bbi.org), and Pearson/InformIT (http://www.informit.com/). (Would your organization like to sponsor too? Please get in touch!)