Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection.
Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection.
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DOI：
10.2196/jmir.6045
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发表时间：
2016-08-29
影响因子：
7.4
通讯作者：
Dunn AG
中科院分区：
文献类型：
--
作者：
Surian D;Nguyen DQ;Kennedy G;Johnson M;Coiera E;Dunn AG
在公共卫生监测中，测量信息如何通过在线社区进入和传播可能有助于我们了解与不良健康结果相关的决策的地理差异。我们的目的是评估使用社区结构和主题建模方法作为表征Twitter上关于人乳头瘤病毒（HPV）疫苗的意见聚类的过程。该研究检查了2013年10月至2015年10月期间收集的关于HPV疫苗的Twitter帖子（推文）。我们测试了潜在的Dirichlet分配和Dirichlet多项混合（DMM）模型，用于推断与推文相关的主题，以及社区聚集（Louvain）和随机游走（Infomap）方法的编码，以从用户的社会联系中检测社区结构。我们研究了社区结构和主题之间的对齐使用几个常见的聚类对齐措施，并介绍了一个统计措施的对齐的基础上集中的特定主题在少数社区。呈现主题的可视化以及主题与社区之间的一致性，以支持在公共卫生传播和识别有拒绝HPV疫苗安全性和有效性风险的社区的背景下对结果的解释。我们分析了来自101,519名用户的285,417条关于HPV疫苗的推文（tweet），这些用户通过4,387,524个社交连接进行连接。检查社区结构和推文主题之间的对齐，结果表明，鲁汶社区检测算法与DMM一起产生一致的较高对齐值，并且当主题数量较低时，对齐通常较高。在应用鲁汶方法和DMM与30个主题和分组语义相似的主题在一个层次结构，我们的特点是163,148（57.16%）的推文作为证据和宣传，和6244（2.19%）的推文描述的个人经历。在4548名发布体验推文的用户中，有3449名用户（75.84%）被发现在大多数推文都是关于证据和宣传的社区。使用社区检测与主题建模似乎是一个有用的方式来描述Twitter社区的意见监测在公共卫生应用程序的目的。我们的方法可能有助于识别在线社区，这些社区有可能受到有关HPV疫苗等公共卫生干预措施的负面意见的影响。
In public health surveillance, measuring how information enters and spreads through online communities may help us understand geographical variation in decision making associated with poor health outcomes. Our aim was to evaluate the use of community structure and topic modeling methods as a process for characterizing the clustering of opinions about human papillomavirus (HPV) vaccines on Twitter. The study examined Twitter posts (tweets) collected between October 2013 and October 2015 about HPV vaccines. We tested Latent Dirichlet Allocation and Dirichlet Multinomial Mixture (DMM) models for inferring topics associated with tweets, and community agglomeration (Louvain) and the encoding of random walks (Infomap) methods to detect community structure of the users from their social connections. We examined the alignment between community structure and topics using several common clustering alignment measures and introduced a statistical measure of alignment based on the concentration of specific topics within a small number of communities. Visualizations of the topics and the alignment between topics and communities are presented to support the interpretation of the results in context of public health communication and identification of communities at risk of rejecting the safety and efficacy of HPV vaccines. We analyzed 285,417 Twitter posts (tweets) about HPV vaccines from 101,519 users connected by 4,387,524 social connections. Examining the alignment between the community structure and the topics of tweets, the results indicated that the Louvain community detection algorithm together with DMM produced consistently higher alignment values and that alignments were generally higher when the number of topics was lower. After applying the Louvain method and DMM with 30 topics and grouping semantically similar topics in a hierarchy, we characterized 163,148 (57.16%) tweets as evidence and advocacy, and 6244 (2.19%) tweets describing personal experiences. Among the 4548 users who posted experiential tweets, 3449 users (75.84%) were found in communities where the majority of tweets were about evidence and advocacy. The use of community detection in concert with topic modeling appears to be a useful way to characterize Twitter communities for the purpose of opinion surveillance in public health applications. Our approach may help identify online communities at risk of being influenced by negative opinions about public health interventions such as HPV vaccines.
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影响因子：
3.7
作者：
Chew C;Eysenbach G
通讯作者：
Eysenbach G
DOI：
10.1136/bmj.g1458
发表时间：
2014-03-04
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Crowe E;Pandeya N;Brotherton JM;Dobson AJ;Kisely S;Lambert SB;Whiteman DC
通讯作者：
Whiteman DC
影响因子：
7.4
作者：
Dunn, Adam G.;Leask, Julie;Coiera, Enrico
通讯作者：
Coiera, Enrico
影响因子：
3.7
作者：
Dodds PS;Harris KD;Kloumann IM;Bliss CA;Danforth CM
通讯作者：
Danforth CM
DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
Blondel, Vincent D.;Guillaume, Jean-Loup;Lefebvre, Etienne
通讯作者：
Lefebvre, Etienne