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

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.