Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/59666

Incorporating Context and Location Into Social Media Analysis: A Scalable, Cloud-Based Approach for More Powerful Data Science

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dc.contributor.author Anderson, Jennings
dc.contributor.author Casas Saez, Gerard
dc.contributor.author Anderson, Kenneth
dc.contributor.author Palen, Leysia
dc.contributor.author Morss, Rebecca
dc.date.accessioned 2019-01-03T00:02:25Z
dc.date.available 2019-01-03T00:02:25Z
dc.date.issued 2019-01-08
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59666
dc.description.abstract Dominated by quantitative data science techniques, social media data analysis often fails to incorporate the surrounding context, conversation, and metadata that allows for more complete, accurate, and informed analysis. Here we describe the development of a scalable data collection infrastructure to interrogate massive amounts of tweets—including complete user conversations—to perform contextualized social media analysis. Additionally, we discuss the nuances of location metadata and incorporate it when available to situate the user conversations within geographic context through an interactive map. The map also spatially clusters tweets to identify important locations and movement between them, illuminating specific behavior, like evacuating before a hurricane. We share performance details, the promising results of concurrent research utilizing this infrastructure, and discuss the challenges and ethics of using context-rich datasets.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining and Machine Learning for Social Media
dc.subject Digital and Social Media
dc.subject Data Science, Kubernetes, GeoLocation, Social Media Data Analysis, Twitter
dc.title Incorporating Context and Location Into Social Media Analysis: A Scalable, Cloud-Based Approach for More Powerful Data Science
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2019.275
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


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