Tweet My Street: A Cross-Disciplinary Collaboration  for the Analysis of Local Twitter Data

Mearns, G; Simmonds, R; Richardson, Ranald; Turner, Mark; Watson, Paul; Missier, Paolo

doi:10.3390/fi6020378

Cited by 16 publications

(7 citation statements)

References 37 publications

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“…In most of the cases, no interaction is allowed; however, a mouse-over is provided by (Morstatter et al 2013) (to reveal date and number of tweets) and by (Chae et al 2014) (which highlights the bars belonging to the same period). Moreover, (Mearns et al 2014) allows moving a slider on the x-axis and shows the volume of terms, bursting terms, and anomalies. (3) GeoT3D ( (4) Other filters (7) I. C. Relationships (among tags, hashtags, and users) are illustrated by a network graph.…”

Section: Figmentioning

confidence: 99%

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

Bertone

Burghardt

2017

J geovis spat anal

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User-generated content is a valuable source of information whose production increases year after year. Twitter data is a form of user-generated content that is frequently adopted to comment on life activities in several contexts. Thus, scientific interest in that data has increased in recent years. This paper focusses on visual analytics approaches addressing the microblogging content exchanged through Twitter. In particular, we concentrate our interest on approaches that consider spatial and temporal aspects and provide visual support. Articles from the major conferences, journals, and digital libraries have been collected, organized and compared based on different criteria such as research questions, application focus, analytical and visual methods adopted, and interaction provided. In addition to these comparisons, opportunities and challenges are illustrated to inspire future research.

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Section: Figmentioning

confidence: 99%

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

Bertone

Burghardt

2017

J geovis spat anal

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“…Mearns et al. (2014) conducted a pilot study to investigate the future usage of Twitter to improve the conditions of streets and tested the ability to retrieve, store, and visualize geo‐tagged data as a part of community engagement in planning and public services, which would be necessary in the case of service failure and also apply to other public services (Su et al., 2019). Another group of studies examined the trend of weekly or daily emotions using SNS data (Wang et al., 2016).…”

Section: Related Literaturementioning

confidence: 99%

Happy neighborhoods: Investigating neighborhood conditions and sentiments of a shrinking city with Twitter data

2020

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Planning interventions have been applied to improve the well‐being, hereafter happiness, of residents. The happiness in shrinking cities, in particular, becomes more critical since urban decline tends to induce an unequal and uneven distribution of care under a limited budget and human resources. Using geo‐tagged Twitter, census, and geospatial data on Detroit, Michigan, which is one of the well‐known shrinking cities in the U.S., the spatial distribution of sentiments, topics of tweets appeared, and the association between neighborhood conditions and the level of happiness were examined. The outcomes indicate that people in Detroit are posting happy tweets more than negative tweets. The downtown area holds both positive and negative hotspots, which are clustered around sports arenas and bars, respectively. Neighborhoods with young and well‐educated residents, situated close to amenities (i.e., recreation facilities, colleges, and commercial areas), and less crime tend to be happier. The use of SNS data could serve as a meaningful social listening tool to reconcile the declining urban vitality of neighborhoods since people interact with those spaces. Negative sentiments are attached to specific neighborhoods with certain conditions so that regeneration efforts should take place in neighborhoods with a higher priority.

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“…Prominent examples of such initiatives include Data.gov in the U.S. and OpenKenya [113] in the Republic of Kenya (see [114] for how such data can be used). The ever-increasing popularity of social media, enabled by Web 2.0 technology, is expanding the sources and volume of social data relevant to our daily lives through applications such as Facebook, Twitter, and Instagram, and these open sources are allowing researchers to explore a vast range of topics, including opinions during elections [115], opinions on public health [116], data on disease outbreaks [117], and studies of the connections between people and places [118,119]. The relevance and management of open-source data has become more important than ever, and they are well-positioned to support the quantitative study of disasters through the use of new computational methods, such as machine learning, natural language processing, sentiment analysis, and artificial intelligence [120].…”

Section: Information Retrieval and Open Data Systemsmentioning

confidence: 99%

Computational Social Science of Disasters: Opportunities and Challenges

Burger

Kennedy

et al. 2019

Future Internet

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Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

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Tweet My Street: A Cross-Disciplinary Collaboration for the Analysis of Local Twitter Data

Cited by 16 publications

References 37 publications

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

Happy neighborhoods: Investigating neighborhood conditions and sentiments of a shrinking city with Twitter data

Computational Social Science of Disasters: Opportunities and Challenges

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