Networked Co-Production of 311 Services: Investigating the Use of Twitter in Five U.S. Cities

Gao, Xian

doi:10.1080/01900692.2017.1298126

Cited by 23 publications

(15 citation statements)

References 38 publications

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“…Data quality and data application analysis are two popular research issues. To understand the city service data categories and distribution, the citizen participant degree has been investigated to help refine policies to encourage citizen participation in e‐government (Gao, 2018). To detect the relationship between the quality of life of citizens and social‐economic and demographic factors, the distribution of 311 data points in Miami has been validated (Hagen et al., 2019).…”

Section: Related Workmentioning

confidence: 99%

“…Although many studies have been performed, these analyses and prediction models may be limited by data quality, that is, lack of data or uneven data distribution. A previous study compared 311 data and social media data (Twitter) in five cities (Chicago, New York, Philadelphia, San Francisco, and Kansas City, Missouri) (Gao, 2018) and demonstrated that the amount of city service data does not reflect the severity of the complaints owing to the uneven distribution (Xu et al., 2017). As noted in other VGI data, data quality and distribution are more sensitive to socioeconomic factors, such as education level, ethnic differences, and religion (Kitchin, 2014; Lu & Johnson, 2016).…”

Section: Related Workmentioning

confidence: 99%

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Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Jing

Zhu

et al. 2021

Transactions in GIS

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City service demand fluctuates across space and time. Although various data, such as 311 hotline data and social media data, have been used to explore the spatiotemporal patterns of city services, data uncertainty and the uneven distribution of service demand are overlooked to some extent and thus could result in bias. To overcome these shortcomings, top‐down collected city service data that fully cover urban areas are used as an emerging data source in this article. A visual analytical approach that employs a three‐dimensional model based on a space‐time cube combined with the Mann–Kendall algorithm is developed and applied in Xicheng District, Beijing, China. The results show that in comparison to other methods, the emerging data and visualization methods have more power to explain city services in terms of overall trends and micro‐scale details. For instance, city service cases demonstrate a significant downward trend. Meanwhile, the distribution of hotspots/coldspots is found to be related to the built environment and population density. For example, high‐incidence cases are located in some communities that are the key governance areas, indicating a demand to increase the staffing of grid administrators. The findings of this work can potentially benefit other cities in China and worldwide.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Jing

Zhu

et al. 2021

Transactions in GIS

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“…Innovations in information and communication technologies, more specifically the extensive proliferation of social media tools, has proofed to have the possibility of greatly contributing to "open government" by providing not only public information disseminating forums, but also stakeholder participation avenues [1]. Considering that the consequences of social media as a major source of influence grows, extant inquiry to explore who takes the lead in information sharing, and more importantly the patterns of information exchange among these users become more prevalent.…”

Section: Introductionmentioning

confidence: 99%

A Twitter Social Network Analysis: The South African Health Insurance Bill Case

Niemann-Struweg¹

2020

Lecture Notes in Computer Science

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The process of extricating relationships and interchanges via visual mapping refers to Social Network Analysis (SNA). Through social network and graph theory lenses, this study explore Twitter data shortly after the announcement of the National Health Insurance (NHI) Bill to the South African parliament. An instrumental, single case study design and SNA secured contextual and timely Twitter interchanges of 4 112 tweets of the hashtag "NHI". Given the growing call for the comprehension of social media network interactions in different contexts, this paper use the underutilized tool for social media analytics, 'Network Overview, Discovery and Exploration for Excel Pro' (NodeXL Pro) to extract and visually present knowledge from pairwise relations between actors in the #NHI social media network. The findings explain the data dispersion and network structure of the #NHI case. The results clearly identifies the influencersmostly the South African government, specific Twitter users and gatekeepers in the announcement of a highly controversial healthcare bill that will affect all South African citizens. The paper contributes theoretically by adding graph theory to the social media research field and to a less studied social media research cluster, namely social media during critical events. The practical contribution of the study is the use of NodeXL Pro, a unique SNA tool for advanced social media crawling, SNA and advanced network metric analysis.

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“…The public sector makes use of Twitter for a number of reasons, such as stakeholder communication and the sharing of information (McInnes & Hornmoen, ), calls to action (Al‐Aama, ), networking (Gao, ; Khan, Yoon, Kim, & Park, ), citizen engagement (Haro‐de‐Rosario, Sáez‐Martín, & del Carmen Caba‐Pérez, ), or disaster management (Chatfield & Reddick, ). Another study by Eom, Hwang, and Kim () shows that Twitter can enhance public service and improve government responsiveness.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of Twitter utilization in disaster management between public and private organizations

Gunawong

Thongpapanl

Ferreira

2019

Journal of Public Affairs

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Twitter has become a commonly used platform by both public and private organizations to assist with the dissemination of information related to disaster management. This research makes use of a mixed‐method approach in determining the extent and manner in which Twitter is used to disseminate disaster management information by both public and private organizations. This research found that public organizations are bound by strict regulations resulting in a lower volume and smaller variety of disaster‐related information being disseminated. The nature of an organization and the processes of decision making therein are suggested to influence the ability of an organization to successfully use Twitter as an effective tool for disaster management. Organizations in Thailand currently underutilize Twitter for disaster management, as neither public nor private organizations use Twitter as a multidirectional communication disaster management tool.

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Networked Co-Production of 311 Services: Investigating the Use of Twitter in Five U.S. Cities

Cited by 23 publications

References 38 publications

Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

A Twitter Social Network Analysis: The South African Health Insurance Bill Case

A comparative study of Twitter utilization in disaster management between public and private organizations

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