Vol.1, No.3, 2019, pp.117-134, doi:10.32604/jbd.2019.08454
The Analysis of China’s Integrity Situation Based on Big Data
  • Wangdong Jiang1, Taian Yang1, *, Guang Sun1, 3, Yucai Li1, Yixuan Tang2, Hongzhang Lv1, Wenqian Xiang1
1 School of Information Management and Technology, Institute of Big Data, Hunan University of Finance and Economics, Changsha, 410205, China.
2 Housheng School of International Education, Hunan University of Finance and Economics, Changsha, 410205, China.
3 School of Engineering, The University of Alabama, Tuscaloosa, 35487, USA.
* Corresponding Author: Taian Yang. Email: .
In order to study deeply the prominent problems faced by China’s clean government work, and put forward effective coping strategies, this article analyzes the network information of anti-corruption related news events, which is based on big data technology. In this study, we take the news report from the website of the Communist Party of China (CPC) Central Commission for Discipline Inspection (CCDI) as the source of data. Firstly, the obtained text data is converted to word segmentation and stop words under preprocessing, and then the pre-processed data is improved by vectorization and text clustering, finally, after text clustering, the key words of clean government work is derived from visualization analysis. According to the results of this study, it shows that China’s clean government work should focus on ‘the four forms of decadence’ issue, and related departments must strictly crack down five categories of phenomena, such as “illegal payment of subsidies or benefits, illegal delivery of gifts and cash gift, illegal use of official vehicles, banquets using public funds, extravagant wedding ceremonies and funeral”. The results of this study are consistent with the official data released by the CCDI’s website, which also suggests that the method is feasible and effective.
Big data, anti-corruption, text clustering, visualization.
Cite This Article
. , "The analysis of china’s integrity situation based on big data," Journal on Big Data, vol. 1, no.3, pp. 117–134, 2019.
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