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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Title:Incorporating Context and Location Into Social Media Analysis: A Scalable, Cloud-Based Approach for More Powerful Data Science
Authors:Anderson, Jennings
Casas Saez, Gerard
Anderson, Kenneth
Palen, Leysia
Morss, Rebecca
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
Digital and Social Media
Data Science, Kubernetes, GeoLocation, Social Media Data Analysis, Twitter
Date Issued:08 Jan 2019
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.
Pages/Duration:10 pages
URI/DOI:http://hdl.handle.net/10125/59666
ISBN:978-0-9981331-2-6
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


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