What are we 'tweeting' about obesity? Mapping tweets with Topic Modeling and Geographic Information System.
What are we 'tweeting' about obesity? Mapping tweets with Topic Modeling and Geographic Information System.
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DOI：
10.1080/15230406.2013.776210
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发表时间：
2013
影响因子：
2.5
通讯作者：
Guha R
中科院分区：
文献类型：
--
作者：
Ghosh DD;Guha R
公共卫生相关的推文很难在像Twitter.com这样的大型会话数据集中识别。更具挑战性的是对推特中编码的空间模式进行可视化和分析。本研究有以下目标：如何使用主题建模来识别相关的公共卫生主题，如Twitter.com?上的肥胖与肥胖相关的常见问题有哪些？主题的空间模式是什么？使用来自社交网站的大型会话数据集的研究挑战是什么？肥胖被选为测试主题，以证明使用潜在狄利克雷分配（LDA）和空间分析，使用地理信息系统（GIS）的主题建模的有效性。该数据集是由从Twitter.com上提取的关于肥胖相关查询的推文（源自美国）构建的。这样的查询的示例是“食物沙漠”、“快餐”和“儿童肥胖”。这些推文也有地理参考和时间戳。三个有凝聚力和有意义的主题，如“儿童肥胖和学校”，“肥胖预防”和“肥胖和饮食习惯”从LDA模型中提取。提取的主题的GIS分析显示农村和城市地区之间，北方和南方各州之间，沿海和内陆各州之间的独特的空间格局。此外，将主题与辅助数据集（如美国人口普查和基于地理信息系统环境中推文位置的快餐店位置）相关联，为空间分析和制图开辟了新的途径。因此，本研究中使用的技术为一般计算社会科学家和具体健康研究人员提供了一个可能的工具集，以便更好地从大型对话数据集中了解健康问题。
Public health related tweets are difficult to identify in large conversational datasets like Twitter.com. Even more challenging is the visualization and analyses of the spatial patterns encoded in tweets. This study has the following objectives: How can topic modeling be used to identify relevant public health topics such as obesity on Twitter.com? What are the common obesity related themes? What is the spatial pattern of the themes? What are the research challenges of using large conversational datasets from social networking sites? Obesity is chosen as a test theme to demonstrate the effectiveness of topic modeling using Latent Dirichlet Allocation (LDA) and spatial analysis using Geographic Information System (GIS). The dataset is constructed from tweets (originating from the United States) extracted from Twitter.com on obesity-related queries. Examples of such queries are ‘food deserts’, ‘fast food’, and ‘childhood obesity’. The tweets are also georeferenced and time stamped. Three cohesive and meaningful themes such as ‘childhood obesity and schools’, ‘obesity prevention’, and ‘obesity and food habits’ are extracted from the LDA model. The GIS analysis of the extracted themes show distinct spatial pattern between rural and urban areas, northern and southern states, and between coasts and inland states. Further, relating the themes with ancillary datasets such as US census and locations of fast food restaurants based upon the location of the tweets in a GIS environment opened new avenues for spatial analyses and mapping. Therefore the techniques used in this study provide a possible toolset for computational social scientists in general and health researchers in specific to better understand health problems from large conversational datasets.
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影响因子：
3.7
作者：
Chew C;Eysenbach G
通讯作者：
Eysenbach G
影响因子：
3.8
作者：
Ghosh, Debarchana;Sterns, Anthony A.;Hamera, Edna
通讯作者：
Hamera, Edna
DOI：
10.1111/j.1083-6101.2007.00393.x
发表时间：
2007-10-01
影响因子：
7.2
作者：
Boyd, Danah M.;Ellison, Nicole B.
通讯作者：
Ellison, Nicole B.
影响因子：
--
作者：
Porter, M. F.
通讯作者：
Porter, M. F.
影响因子：
6
作者：
Blei, DM;Ng, AY;Jordan, MI
通讯作者：
Jordan, MI