| 王磊.社会化阅读平台中信息采纳的预测研究[J].情报工程,2020,6(6):065-083 |
| 社会化阅读平台中信息采纳的预测研究 |
| Prediction Study of Information Adoption in Social Reading Platforms |
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| DOI：10.3772/j.issn.2095-915X.2020.06.007 |
| 中文关键词: 社会化阅读；微信公众平台；信息采纳 |
| 英文关键词: Social reading; WeChat public platform; information adoption |
| 基金项目:安徽省高等学校图书情报工作委员会基金项目重点课题“ 社会化阅读环境下读者信息采纳预测研究”（TGW18A01），中央高校基本科研业务费资助项目(JZ2018HGBZ0114)。 |
| 作者 | 单位 | | 王磊 | 合肥工业大学图书馆 合肥 230009 |
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| 中文摘要: |
| 伴随着文本数字化趋势以及社交媒体演进，以分享、互动为核心的社会化阅读已成为研究热点。然而，目前尚缺少从信息特征维度进行社会化阅读平台中信息采纳的研究，因此本文尝试开展了基于阅读类微信公众平台信息特征的信息采纳预测研究。首先，利用清博大数据平台及Python 语言编写的网页爬虫实施阅读类微信公众平台信息采集。其次，对收集到的信息按照信息采纳理论提取信息特征，并按照设定好的编码规则进行编码。最后，利用机器学习技术中的支持向量机算法进行模型训练与预测研究。实证结果表明，训练好的模型根据编码后的信息特征能有效的预测阅读类微信公众平台中信息采纳与否。 |
| 英文摘要: |
| With the trend of texts digitization and the evolution of social media, the social reading with sharing and interaction as its core, has become a research hotspot. Nevertheless, there is still a lack of researches on information adoption in social reading platforms from the dimension of information characteristics. Therefore, this paper attempts to carry out a research on information adoption prediction based on the information characteristics of reading-themed WeChat public platforms. Firstly, use “qingbo big data platform” and web crawler written by Python language to implement information collection. Secondly, extract information characteristics from the collected information according to the information adoption theory, and then encode them in accordance with the coding rules. Finally, we use support vector machine (SVM) algorithm, which belongs to the technology of machine learning, to train the models for the subsequent prediction study. The results show that according to these encoded information
characteristics, the trained model can effectively and accurately predict whether or not the information in the reading-themed WeChat public platforms will be adopted, which provides a new research perspective for information adoption in the social reading platforms. |
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