Spatial Characteristics of Twitter Users—Toward the Understanding of Geosocial Media Production

Rzeszewski, Michał; Beluch, Łukasz

doi:10.3390/ijgi6080236

Cited by 12 publications

(8 citation statements)

References 38 publications

(32 reference statements)

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“…Based on the extraction results, we find that a large amount of Sina Weibo users posted very few geotagged social media data which is not enough for extracting their home location. Similar finding has also be proved by Rzeszewski et al [34].…”

Section: Home Location Extractionsupporting

confidence: 89%

“…Last, the captures for different feasible locations were estimated, and the optimal location for a new retail agglomeration was determined. influence the final results of our study and can be considered as outliers [34]. Based on the study of Rzeszewski et al [34], we restricted geotagged microblogs to one location per user in our case.…”

Section: Methodsmentioning

confidence: 99%

“…These microblogs will influence the final results of our study and can be considered as outliers [34]. Based on the study of Rzeszewski et al [34], we restricted geotagged microblogs to one location per user in our case. Specifically, no matter how many microblogs with the same location information one user post, we only retain one microblog for one user in one week.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

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Solving Competitive Location Problems with Social Media Data Based on Customers’ Local Sensitivities

Jiang

Wang

Dou

et al. 2019

IJGI

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Competitive location problems (CLPs) are a crucial business concern. Evaluating customers’ sensitivities to different facility attractions (such as distance and business area) is the premise for solving a CLP. Currently, the development of location-based services facilitates the use of location data for sensitivity evaluations. Most studies based on location data assumed the customers’ sensitivities to be global and constant over space. In this paper, we proposed a new method of using social media data to solve competitive location problems based on the evaluation of customers’ local sensitivities. Regular units were first designed to spatially aggregate social media data to extract samples with uniform spatial distribution. Then, geographically weighted regression (GWR) and the Huff model were combined to evaluate local sensitivities. By applying the evaluation results, the captures for different feasible locations were calculated, and the optimal location for a new retail facility could be determined. In our study, the five largest retail agglomerations in Beijing were taken as test cases, and a possible new retail agglomeration was located. The results of our study can help people have a better understanding of the spatial variation of customers’ local sensitivities. In addition, our results indicate that our method can solve competitive location problems in a cost-effective way.

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Section: Home Location Extractionsupporting

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Section: Data Collection and Preprocessingmentioning

confidence: 99%

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Solving Competitive Location Problems with Social Media Data Based on Customers’ Local Sensitivities

Jiang

Wang

Dou

et al. 2019

IJGI

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“…Moreover, female users are reported low in agreeableness while using instant messaging more often than male users is high in agreeableness, whereas male users are reported low in openness while playing more online games compared to female users are high in openness. Rzeszewski and Beluch [100] addressed the gap (representation and representativeness) in data by investigating the LBSN users, based on the spatiotemporal distribution of the content produced (demographics of the user population). While Guan et al [101] studied the concentration and significance of users' thoughts on Sina Weibo and Feng et al [102] analyzed China's city network based on users' friend relationships and check-in behavior on Sina Weibo.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

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“…Similarly, long-distance movements were explored in [23]. Furthermore, clustering methods incorporating spatial, temporal, and textual information have been widely applied to infer activity types or travel purpose and segment user groups at an aggregated scale [30,31]. Results were generally verified with travel surveys or census data [9,32], and it was concluded that working and commercially related tweets or topics gave a better estimate.…”

Section: Related Work-mining Spatial Patterns From Geo-tweetsmentioning

confidence: 99%

Profiling the Spatial Structure of London: From Individual Tweets to Aggregated Functional Zones

Zhong

Zeng

et al. 2018

IJGI

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Knowledge discovery about people and cities from emerging location data has been an active research field but is still relatively unexplored. In recent years, a considerable amount of work has been developed around the use of social media data, most of which focusses on mining the content, with comparatively less attention given to the location information. Furthermore, what aggregated scale spatial patterns show still needs extensive discussion. This paper proposes a tweet-topic-function-structure framework to reveal spatial patterns from individual tweets at aggregated spatial levels, combining an unsupervised learning algorithm with spatial measures. Two-year geo-tweets collected in Greater London were analyzed as a demonstrator of the framework and as a case study. The results indicate, at a disaggregated level, that the distribution of topics possess a fair degree of spatial randomness related to tweeting behavior. When aggregating tweets by zones, the areas with the same topics form spatial clusters but of entangled urban functions. Furthermore, hierarchical clustering generates a clear spatial structure with orders of centers. Our work demonstrates that although uncertainties exist, geo-tweets should still be a useful resource for informing spatial planning, especially for the strategic planning of economic clusters.

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Spatial Characteristics of Twitter Users—Toward the Understanding of Geosocial Media Production

Cited by 12 publications

References 38 publications

Solving Competitive Location Problems with Social Media Data Based on Customers’ Local Sensitivities

Solving Competitive Location Problems with Social Media Data Based on Customers’ Local Sensitivities

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

Profiling the Spatial Structure of London: From Individual Tweets to Aggregated Functional Zones

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