Masters Theses

Author

Farheen Ali

Abstract

"Innovation in technology enables people to communicate, share information and look for their needs by just sitting in rooms and going through some clicks. While social media has played a very important role in connecting people worldwide, its potential has stretched beyond the innovative idea of connecting people through their social networks. While many thought there was no meeting point for the healthcare sector and social media, it was a surprise when research and innovations have shown that social media could lay a very significant role in the health care sector.

Research has been done in developing models that could use social media as the data source for tracking diseases. Most of these analyses are based on models that prioritize strong correlations with seasonal and pandemic kinds of diseases over the health conditions of a specific individual user.

The aim of this research is to develop a diabetes detecting tool at the individual level using a sample of Twitter IDs that have been collected from the Twitter search using the query -- 'recently diagnosed' and 'diabetes''. Based on text analysis of social media posts using Fisher's exact test, without any medical settings, this thesis investigates the feasibility of diagnosing and classifying diabetes via machine learning techniques, Naive Bayes and Random Forest classifiers. It was found that more than half (20/30 ≈ 67%) of the users in the sample mentioned being tested positive for diabetes, about 27% (8/30) of the users mentioned the symptoms and got involved in diabetes related discussions, but did not mention about being tested positive and rest 4% had no mention of symptoms or diabetes"--Abstract, page iii.

Advisor(s)

Nah, Fiona Fui-Hoon, 1966-
Chellappan, Sriram

Committee Member(s)

Siau, Keng, 1964-
Hilgers, Michael Gene

Department(s)

Business and Information Technology

Degree Name

M.S. in Information Science and Technology

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2015

Pagination

viii, 54 pages

Note about bibliography

Includes bibliographic references (pages 49-53).

Rights

© 2015 Farheen Ali, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Diabetes -- Diagnosis
Social media in medicine

Thesis Number

T 10660

Electronic OCLC #

913477145

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