Duan Guimin, Liao Xin, Yu Weiping, Li Guihua
School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
School of Public Affairs and Law, Southwest Jiaotong University, Chengdu, Sichuan, China.
J Med Internet Res. 2020 May 26;22(5):e13294. doi: 10.2196/13294.
For the last decade, doctor-patient contradiction in China has remained prominent, and workplace violence toward medical staff still occurs frequently. However, little is known about the types and laws of propagation of violence against medical staff online.
By using a self-organizing map (SOM), we aimed to explore the microblog propagation law for violent incidents in China that involve medical staff, to classify the types of incidents and provide a basis for rapidly and accurately predicting trends in public opinion and developing corresponding measures to improve the relationship between doctors and patients.
For this study, we selected 60 cases of violent incidents in China involving medical staff that led to heated discussions on the Sina microblog from 2011 to 2018, searched the web data of the microblog using crawler software, recorded the amount of new tweets every 2 hours, and used the SOM neural network to cluster the number of tweets. Polynomial and exponential functions in MATLAB software were applied to predict and analyze the data.
Trends in the propagation of online public opinion regarding the violent incidents were categorized into 8 types: bluff, waterfall, zigzag, steep, abrupt, wave, steep slope, and long slope. The communications exhibited different characteristics. The prediction effect of 4 types of incidents (ie, bluff, waterfall, zigzag, and steep slope) was good and accorded with actual spreading trends.
Our study found that the more serious the consequences of a violent incident, such as a serious injury or death, the more attention it drew on the microblog, the faster was its propagation speed, and the longer was its duration. In these cases, the propagation types were mostly steep slope, long slope, and zigzag. In addition, the more serious the consequences of a violent incident, the higher popularity it exhibited on the microblog. The popularity within a week was significantly higher for acts resulting from patients' dissatisfaction with treatments than for acts resulting from nontherapeutic incidents.
在过去十年中，中国医患矛盾一直较为突出，针对医护人员的职场暴力事件仍频繁发生。然而，针对医护人员暴力事件在网上的传播类型及规律却鲜为人知。
通过使用自组织映射（SOM），旨在探究中国涉及医护人员暴力事件的微博传播规律，对事件类型进行分类，并为快速准确预测舆情趋势及制定改善医患关系的相应措施提供依据。
本研究选取2011年至2018年在中国导致新浪微博热议的60起涉及医护人员的暴力事件，使用爬虫软件搜索微博网络数据，每2小时记录新发布推文数量，并使用SOM神经网络对推文数量进行聚类。应用MATLAB软件中的多项式和指数函数对数据进行预测和分析。
网络舆情对暴力事件的传播趋势分为8种类型：虚张声势型、瀑布型、锯齿型、陡峭型、突发型、波浪型、陡坡型和长坡型。传播呈现出不同特征。4种事件类型（即虚张声势型、瀑布型、锯齿型和陡坡型）的预测效果良好，与实际传播趋势相符。
我们的研究发现，暴力事件后果越严重，如重伤或死亡，在微博上受到的关注就越多，传播速度越快，持续时间越长。在这些情况下，传播类型大多为陡坡型、长坡型和锯齿型。此外，暴力事件后果越严重，在微博上的热度越高。患者对治疗不满导致的行为在一周内的热度显著高于非治疗事件导致的行为。
