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Can Social Media Support Public Health? Demonstrating Disease Surveillance using Big Data Analytics

2015 | Americas Conference on Information Systems | Citations: 0
Authors: Asamoah, Daniel; Sharda, Ramesh; Kumarasamy, Ashwin Thandapani

Abstract: Rapid growth of the Internet has paved the way for millions of people across the ... Expand

Abstract: Rapid growth of the Internet has paved the way for millions of people across the globe to access social media platforms such as Facebook and Twitter. These social media platforms enable people to share information instantaneously. The large volume of information shared on these platforms can be leveraged to identify outbreaks of various epidemics. This will help health professionals to provide timely intervention, which in return could help save lives and millions of dollars. Analysis of information shared on social media is complicated due to its sheer volume, varied formats and velocity of collection. We have addressed this potential problem by making use of a big data analytics platform capable of handling large quantities of streaming data. In this paper we demonstrate how data from social media can be effectively used in the surveillance of disease conditions. Collapse

Semantic filters: Apache Hive

Topics: Twitter social media Hadoop Distributed File System Apache Hadoop big data

Methods: case study cluster analysis natural language processing part of speech tagging computational algorithm
Adapting CRISP-DM Process for Social Network Analytics: Application to Healthcare

2015 | Americas Conference on Information Systems | Citations: 0
Authors: Asamoah, Daniel; Sharda, Ramesh

Abstract: One of the key limitations about research involving big data is the lack of a so ... Expand

Abstract: One of the key limitations about research involving big data is the lack of a sound methodological process that drives the conceptual and analytical questions posed to the data. In this study, we adapt the popular CRISP-DM process to analyze large volumes of unstructured data to generate analytical insights. We add specificity to the CRISP-DM methodology. Specifically, we propose “Cross Industry Standard Process for Electronic Social Network Platforms (CRISP-eSNeP)”, as an extension to the CRISP-DM methodology. Our methodology focuses on efficient pre-processing of large and unstructured electronic social network data. We illustrate our arguments by applying this methodology to understand the relationship between user influence and information characteristics as depicted on the Twitter microblogging platform. Collapse

Semantic filters: Apache Hive

Topics: Twitter social network big data Apache Hadoop analytical information system

Methods: Cross-Industry Standard Process for Data Mining case study design science qualitative content analysis parametric test

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Can Social Media Support Public Health? Demonstrating Disease Surveillance using Big Data Analytics

Adapting CRISP-DM Process for Social Network Analytics: Application to Healthcare