基于ARIMA模型研究舆情传播的特点和规律——以微博平台早期数据为例

Research on the Characteristics and Laws of Public Opinion Communication Based on ARIMA Model—Taking the Early Data of Microblog Platform as an Example

社交网络舆情通过网络平台传播,是网民对社会和生活中的一些热门话题,所持有的具有较高影响力、倾向性的看法与观点的集合。其中微博作为当今最热门的社交媒体之一,实时更新的热搜榜单成为人们的日常谈资。文章利用Python软件对爬取的部分微博文本数据进行数据预处理,并有针对性地筛选数据,提取特征字段信息,从中挖掘高价值的舆情主旋律,然后建立时序分析模型,对数据特征进行归纳总结,所得结果能够清晰地表明舆情传播有三阶段:产生–扩散–消减,从而挖掘舆情传播的特点与规律,合理抓住这些导向性内容的演变时段对于信息检索、舆情控制、影视宣传等都具有重要的意义和实用价值。

Social network public opinion is spread through network platforms, and it is a collection of highly influential and tendentious views and opinions held by netizens on some hot topics in society and life. Microblog is one of the most popular social media nowadays, and the hot search list updated in real time has become people’s daily conversation. In this paper, Python software is used to pre-process some crawled microblog text data, filter the data pertinently, extract feature field infor-mation, mine high-value public opinion themes from them, and then establish a time series analysis model to summarize the data features. The results can clearly show that there are three stages of public opinion communication: generation-diffusion-reduction, so as to mine the characteristics and laws of public opinion transmission. Therefore, it is of great significance and practical value to excavate the characteristics and rules of public opinion communication and reasonably grasp the evolution period of these guiding contents for information retrieval, public opinion control and film and television propaganda.