Analysis of public health information from social media

1 online resource (47 pages) : PDF

2016

University of North Carolina at Charlotte

Social networking sites have become an integral part of daily life in the modern world. They can serve as an important, dynamic data source providing collective intelligence and awareness of health related issues. Analysis of huge volumes of unstructured data from social media for useful information could be a challenging job due to different factors. This work focuses on collecting data from various publicly available sources, applying data cleansing methods on collected data and analyze the extracted data. This study deals with the creation of methods to scrape data from different social media websites, followed by preparing and cleaning of data involving tasks like stemming of words. Finally, this study applies ontology analysis to find the co-occurrences of keywords of interests to measure associative strength between them. It further computes the corresponding support and confidence intervals to form rules. The summary of the procedures to extract data and preparing them for analysis is produced as results of this work. Our findings suggest that such analysis requires engagement of domain expert from the launch point of the research. This is due to the fact that enormous number of experiments needs to be executed and analyzed to be able to extract useful information . Therefore we can conclude that social media websites such as Facebook are very critical sources of data for analysis of social aspects of health, especially outbreak. We also observe a wide range of discussion about the diseases that occur, across different regions of the globe. These observations can help public health officials to identify and follow social impact on disease outbreaks, so that publicly appropriate actions can be recommended. We also find that social media can be used as a source of information for health related research as well as to discover patterns that are more interesting to researchers across higher education institutions and universities.

masters theses

Information technology

M.S.

Information Technology

Dorodchi, Mohsen

Mayes, Lawerence
Janies, Daniel
Lipford, Heather

Thesis (M.S.)--University of North Carolina at Charlotte, 2016.

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ThalambeduKumaresan_uncc_0694N_11293

http://hdl.handle.net/20.500.13093/etd:1709