基于百度贴吧的HIV高危人群特征分析

肖时耀; 吕慰; 陈洒然; 秦烁; 黄格; 蔡梦思; 谭跃进; 谭旭; 吕欣

doi:10.11959/j.issn.2096-0271.2019008

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基于百度贴吧的HIV高危人群特征分析

应用 | 更新时间：2024-06-03

- 基于百度贴吧的HIV高危人群特征分析
- Analysis of HIV high-risk population characteristics with Baidu Tieba data
- 大数据 2019年5卷第1期页码：2019008
- 作者机构：
  
  1. 国防科技大学系统工程学院，湖南长沙 410073
  2. 湖南益阳康雅医院肿瘤科，湖南益阳 413002
  3. 深圳信息职业技术学院软件工程学院，广东深圳 518172
- 作者简介：
  
  [ "肖时耀（1996- ），男，国防科技大学系统工程学院硕士生，主要研究领域为大数据分析。" ]
  [ "吕慰（1985- ），男，湖南益阳康雅医院肿瘤科主治医师，主要研究领域为放射医学。" ]
  [ "陈洒然（1989- ），男，国防科技大学系统工程学院博士生，主要研究领域为复杂网络理论、统计抽样、数据挖掘。" ]
  [ "秦烁（1995- ），女，国防科技大学系统工程学院硕士生，主要研究领域为复杂网络传播动力学。" ]
  [ "黄格（1991- ），女，国防科技大学系统工程学院博士生，主要研究领域为大数据、复杂网络。" ]
  [ "蔡梦思（1992- ），女，国防科技大学系统工程学院博士生，主要研究领域为社交网络、大数据。" ]
  [ "谭跃进（1958- ），男，国防科技大学系统工程学院教授，主要研究领域为复杂网络。" ]
  [ "谭旭（1981- ），男，深圳信息职业技术学院软件工程学院教授，主要研究领域为智能决策、机器学习、舆情分析。" ]
  [ "吕欣（1984- ），男，国防科技大学系统工程学院副教授，主要研究领域为大数据、复杂网络理论、应急管理。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(No.91846301);国家自然科学基金资助项目;The National Natural Science Foundation of China(No.71771213);国家自然科学基金资助项目;The National Natural Science Foundation of China(No.71790615);国家自然科学基金资助项目;The National Natural Science Foundation of China(No.71690233);中国教育部文学和社会科学基金资助项目;The MOE(Ministry of Education in China)Liberal Arts and Social Sciences Foundation(No.17YJCZH157);深圳市“鹏城学者计划”基金资助项目;The Pengcheng Scholar Funded Scheme
- DOI：10.11959/j.issn.2096-0271.2019008
  中图分类号： TP391.1
- 网络首发：2019-01，
  
  纸质出版：2019-01-15
- 稿件说明：
移动端阅览
肖时耀, 吕慰, 陈洒然, 等. 基于百度贴吧的HIV高危人群特征分析[J]. 大数据, 2019,5(1):2019008.

Shiyao XIAO, Wei LYU, Saran CHEN, et al. Analysis of HIV high-risk population characteristics with Baidu Tieba data[J]. Big data research, 2019, 5(1): 2019008.
肖时耀, 吕慰, 陈洒然, 等. 基于百度贴吧的HIV高危人群特征分析[J]. 大数据, 2019,5(1):2019008. DOI： 10.11959/j.issn.2096-0271.2019008.

Shiyao XIAO, Wei LYU, Saran CHEN, et al. Analysis of HIV high-risk population characteristics with Baidu Tieba data[J]. Big data research, 2019, 5(1): 2019008. DOI： 10.11959/j.issn.2096-0271.2019008.

摘要

对百度贴吧“恐艾吧”中在线高危人群的帖子内容、线上活动时间规律进行了分析，利用LDA话题模型，对比分析了有无HIV感染者参与的主贴讨论的话题之间的差异，使用基于关键词的机器学习方法区分了在“恐艾吧”中发布话题的用户的性取向，计算不同性取向人群中HIV的流行率。研究结果说明，使用在线数据挖掘的技术和方法比传统方法更加高效，可以作为高危人群研究的重要补充。此外，基于机器学习对人群性取向进行智能判别，对于公共卫生管理部门监测疫情在不同人群中的发展状况有重要意义。

Abstract

The textual content and temporal pattern of online activities for users gathered in the “Fear of HIV Bar” of Baidu Tieba were analyzed. LDA topic model was used to analyze the main differences between topics discussed among HIV-infected people and non-HIV-infected people. A machine learning method based on key words was used to distinguish the sexual orientation of users who start a discussion in “Fear of HIV Bar”

and calculate the epidemic rate of HIV among groups with different sexual orientations. The techniques used in this paper can be supplemented as an important tool for high-risk populations research. In addition

this paper can be applied to assess the epidemic of HIV in populations with different sexual orientations by using machine learning technique to intelligently classify the sexual orientation of a user

which is of great significance for the public health agencies.

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references

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