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Friday Seminar 6 – Automated Conversion of Social Media into Data: Demonstration and Tutorial

Friday, April 28, 2017
3:00pm – 6:00pm
Northern Hemisphere A2


Richard Landers, Old Dominion University


Emily Grijalva, University at Buffalo, SUNY


Recent technological advances have brought the automated collection of data from social media, such as personal websites, discussion forums, Facebook, and Twitter, within the abilities of the average I-O psychologist or HR professional. In this hands-on seminar, learn the ins and outs of these big data techniques, using freely available open-source software.

Full Description:

One of the most critical skills for an I-O psychologist or HR professional to gain in this new era of big data is basic computer programming. Intended for those with no prior experience in programming, this seminar will train attendees in the skills needed to identify online data sources, automatically scavenge the Internet collecting them, algorithmically process them in order to identify and extract values for specific variables of interest, and finally place them into a dataset analyzable with our standard statistical packages.

Intended Audience:

This session is intended for a general audience at a post-graduate level; no specific content knowledge is required, however, it is recommended attendees bring a laptop.

Learning Objectives:

  • Describe the pros and cons of various social media data sources to target
  • Create a data source theory based upon your chosen data sources to be tested later
  • Collect social media data from structured API calls (e.g., Facebook, Twitter, LinkedIn)
  • Collect social media data from unstructured raw HTML sources using a web crawler and web scraper
  • Convert data collected via these methods into SPSS- or R-ready datasets

Presenter Biography

Dr. Richard N. Landers is an Associate Professor of Industrial-Organizational Psychology at Old Dominion University in Norfolk, VA, USA. He earned his PhD in Industrial-Organizational Psychology at the University of Minnesota, Twin Cities. His research program concerns the use of innovative technologies in assessment, employee selection, adult learning, and research methods, with his work appearing in Industrial and Organizational Psychology Perspectives, Journal of Applied Psychology, Computers in Human Behavior, Simulation & Gaming, Social Science Computer Review, and Psychological Methods, among others. Recent topics have included big data, game-based learning, game-based assessment, gamification, unproctored Internet-based testing, mobile devices, virtual reality, and online social media. His research and writing has been featured in Forbes, Business Insider, Science News, Popular Science, Maclean’s, and the Chronicle of Higher Education, among others. He currently serves as Associate Editor of Computers in Human Behavior, Simulation & Gaming, and the International Journal of Gaming and Computer-Mediated Simulations, and he is also part of the steering committee of the Coalition for Technology in Behavioral Science. He was Old Dominion University’s 2014 and 2015 nominee for the State Council for Higher Education in Virginia Rising Star Outstanding Faculty Award. He is also author of a statistics textbook, A Step-by-Step Introduction to Statistics for Business, and editor of both Social Media in Employee Selection and the upcoming Cambridge Handbook of Technology and Employee Behavior

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