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WRM: “Using Big Data, Social Networks, and Agent-Based Modeling to Understand Information Diffusion”

May 17, 2017 @ 1:00 pm - 2:30 pm

Bill Rand
NC State University, PCOM


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 explore the application of this method to three different problems:  (1) the prediction of individual activity on social media, (2) the forecasting of optimal messaging times on social media, and (3) understanding marketing channel attribution.  We will explore this in the context of large-scale, individual level collections of consumer and user behavior.  This work illustrates the power and usefulness of an individual-level approach to modeling and understanding large datasets.

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We are going to use the NC State WebEx for the web conference. Please note that this
WebEx belongs to NC State and can not be downloaded directly from Cisco.
Also, it should work on iPhones and iPads via the WebEx App.
A good internet connection is recommended.
For better audio, please join via computer and then have the meeting call your number
or call in directly using one of the numbers below. When not speaking, please mute
your phone to avoid background noise.
When it’s time, join the WebEx meeting from here:
Audio Connection: 919-513-9329 (WolfMeeting)
Access Code: 997 998 592

Sponsored by the Laboratory for Analytic Sciences


May 17, 2017
1:00 pm - 2:30 pm
Event Category:
Event Tags:
meeting, research, wednesday, WRM


Mindy Huffman

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LAS aims to bring together a multi-disciplinary group of academic, industry, and government researchers, analysts and managers together to re-engineer the intelligence analysis process to promote predictive analysis. LAS will do this by conducting both classified and unclassified research in a variety of areas of research. The research done in this area will serve as the foundation for mission effects and integrated back into the enterprise.

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