The prediction of petition based on Big Data

Abstract

Purpose

The development of Big Data and online searching engine provides a good opportunity for studying petition in China. This study has constructed a set of indices for predicting petitions in China by using online searching engines and further explored the predicting role of economic, environment and public life risk perception in various petitions.

Design/methodology/approach

Based on the study of Xue and Liu (2017), this research first re-classified offline petition by human and cluster analysis in terms of social risk perception and built online searching indices of the two sets of petition by using data from “Google Trend” and “Baidu Index.” Second, it analyzed the predicting effect of social risk perception on online searching indices of petition by using Granger causality analysis. Finally, this study integrated the results and selected significant paths from social risk perception to the two sets of petition.

Findings

The study found that the re-classification made by human was more appropriate than the categories made by cluster analysis in terms of social risk perception. For the two sets of petition, the correlations between offline petition and Baidu Index of petition were both more significant than that of Google index. Moreover, economic and finance and resource and environment risk perception had a significant predicting effect on more than one kind of online searching indices of petition.

Originality/value

The results have demonstrated the important role of economic issues in China on predicting petitions of the economic kind, as well as other kinds. They have also reflected the dominant social contradictions and their relationship in modern China.

Citation

Xue, T. and Liu, H. (2019), "The prediction of petition based on Big Data", Information Discovery and Delivery, Vol. 47 No. 3, pp. 135-142. https://doi.org/10.1108/IDD-08-2018-0031