phat 19(18): e3

Research Article

A Novel Method to Detect Public Health in Online Social Network Using Graph-based Algorithm

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  • @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
R. Devika1, S. Sinduja1, V. Subramaniyaswamy1,*
  • 1: School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India
*Contact email: vsubramaniyaswamy@gmail.com

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.