Research Article
A Novel Method to Detect Public Health in Online Social Network Using Graph-based Algorithm
@ARTICLE{10.4108/eai.13-7-2018.162669, author={R. Devika and S. Sinduja and V. Subramaniyaswamy}, title={A Novel Method to Detect Public Health in Online Social Network Using Graph-based Algorithm}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={5}, number={18}, publisher={EAI}, journal_a={PHAT}, year={2019}, month={5}, keywords={Online Social Network (OSN), Support Vector Machine (SVM), Min cut, Text rank, K-Means, Twitter, Tweets}, doi={10.4108/eai.13-7-2018.162669} }- R. Devika
S. Sinduja
V. Subramaniyaswamy
Year: 2019
A Novel Method to Detect Public Health in Online Social Network Using Graph-based Algorithm
PHAT
EAI
DOI: 10.4108/eai.13-7-2018.162669
Abstract
INTRODUCTION: Twitter has played an important role in the social life of people. The health-related tweets are extracted and find the spread of epidemic disease on network. It can provide as a starting place of individual data to learn the physical condition of users.
OBJECTIVES: Key objective is to develop graph-based algorithm to detect public health in online social network.
METHODS: The proposed method collect the tweets relating to general health in twitter using the min-cut algorithm. The algorithm finds the minimum cut off an undirected edge-weighted graph. The runtime of the algorithm seems to be faster than other graph algorithms. Min-cut is reliable and good in network optimization and prevents redundancy.
RESULTS: To evaluate the performance, we utilize the health dataset on the detection of epidemic disease. The proposed method using a graph-based algorithm is the best in terms of accuracy, precision, and recall. With respect to the confusion matrix, Min-cut provides the highest true positive when compared to Text rank and K-Means algorithm.
CONCLUSION: Proposed health detection method using graph-based algorithm is better than Text Rank and K-Means in all aspects.
Copyright © 2019 R.Devika et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.