Sean Goggins Dissertation

It’s winter so we should have a blizzard, but in Hawaii it seems to be a blizzard of visiting researchers. During the week of Jan. 9, the HICHI lab will be hosting:

  • Dr. Sean Goggins, Computer Science Department, University of Missouri
  • Dr. Raghava Rao Mukkamala, Centre for Business Data Analytics, Copenhagen Business School
  • Dr. Ravi Vatrapu, Centre for Business Data Analytics, Copenhagen Business School

Ravi was a postdoc in the HICHI lab, a researcher in the LILT lab, and a Ph.D. student of Dan Suthers in the CIS program.

We will be planning some socio-technical research for the coming year!

They are also giving talks as follows:

Monday, 1/9, 4:30, Hamilton 2K
Social Set Analysis: A Set Theoretical Approach to Big Data Analysis
Dr. Ravi Vatrapu
Details: http://www.ics.hawaii.edu/2017/01/icscis-joint-research-seminar-ravi-vatrapu-on-jan-9/

 

Thursday, 1/12, Noon, POST 318B (ICSpace)
Computational Intelligence Pipelines: Imagination and Reality
Dr. Sean Goggins
Details: http://www.ics.hawaii.edu/2017/01/ics-lunch-and-seminar-series-sean-goggins-on-112/

 

 

Thursday, 1/12, 4:30, POST 126
Multi-Dimensional Text Analytics: Concepts, Methods, Tools and Findings
Dr. Raghava Rao Mukkamala
Details: http://www.ics.hawaii.edu/2017/01/grad-seminar-guest-talk-raghava-rao-mukkamala-112/

This entry was posted in Papers & Presentations, People, Visitors on by scottrob.

A good deal of Twitter research focuses on event-detection using algorithms that rely on keywords and tweet density. We present an alternative analysis of tweets, filtering by hashtags related to the 2012 Superbowl and validated against the 2013 baseball World Series. We analyze low-volume, topically similar tweets which reference specific plays (sub-contexts) within the game at the time they occur. These communications are not explicitly linked; they pivot on keywords and do not correlate with spikes in tweets-per-minute. Such phenomena are not readily identified by current event-detection algorithms, which rely on volume to drive the analytic engine. We propose to demonstrate the effectiveness of empirically and theoretically informed approaches and use qualitative analysis and theory to inform the design of future event-detection algorithms. Specifically, we propose theories of Information Grounds and “third places” to explain sub-contexts that emerge. Conceptualizing sub-contexts as a socio-technical place advances the framing of Twitter event-detection from principally computational to deeply contextual.

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