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Text Mining for Behavioral and Social Sciences Research

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Chapter Description Position
1 Thumbnail image Welcome – Elizabeth Ginexi, Ph.D., NIH/OBSSR 0:00:05
2 Thumbnail image Overview of Text Analysis & Text Mining in Social Science - Brendan O'Connor, Ph.D., University of Massachusetts 0:06:23
3 Thumbnail image Leveraging Social Media for Neighborhood Research to Understand the Potential Influence of Social Environments on Health – Quynh Nguyen, Ph.D., MSPH - University of Maryland 2:45:53
4 Thumbnail image Social Media Surveillance: From Disease to Disinformation – David Broniatowski, Ph.D., FPsyS - George Washington University 3:54:39
Air date: Friday, August 9, 2019, 9:00:00 AM
Time displayed is Eastern Time, Washington DC Local
Views: Total views: 206, (14 Live, 192 On-demand)
Category: Conferences
Runtime: 05:08:17
Description: NIH OBSSR Methodology Seminar

The age of Big Data has ushered in a wave of high-volume digital information and much of it is text based. Text mining is a form of data mining that involves collecting and analyzing large volumes of textual data to reveal patterns and relationships. Techniques for mining can be used to extract key concepts, spot trends, summarize content from documents and gain semantic understanding, and index and search text for use in predictive analytics. Text mining has become an important research process with many different commercial and academic applications, and it is becoming more widely applied in social science studies.

This one-day methodology seminar sponsored by NIH OBSSR will present a basic introductory overview of principles and techniques of text mining for behavioral and social research and showcase some innovative health research examples.

Attendees of this seminar will gain a broad understanding and appreciation for some of the different tools available to collect and analyze large volumes of text data, and they will learn about some notable example applications in behavioral and social sciences health research.

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Author: Brendan O'Connor, Ph.D., University of Massachusetts Amherst, David Broniatowski, George Washington University and Quynh Nguyen, Ph.D., MSPH, University of Maryland School of Public Health
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CIT Live ID: 33304
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