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

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dc.contributor.authorAnderson, Jennings
dc.contributor.authorCasas Saez, Gerard
dc.contributor.authorAnderson, Kenneth
dc.contributor.authorPalen, Leysia
dc.contributor.authorMorss, Rebecca
dc.date.accessioned2019-01-03T00:02:25Z
dc.date.available2019-01-03T00:02:25Z
dc.date.issued2019-01-08
dc.description.abstractDominated 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.275
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59666
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectDigital and Social Media
dc.subjectData Science, Kubernetes, GeoLocation, Social Media Data Analysis, Twitter
dc.titleIncorporating Context and Location Into Social Media Analysis: A Scalable, Cloud-Based Approach for More Powerful Data Science
dc.typeConference Paper
dc.type.dcmiText

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