The Geography and Importance of Localness in Geotagged Social Media

Johnson, Isaac; Sengupta, Subhasree; Schöning, Johannes; Hecht, Brent

doi:10.1145/2858036.2858122

Cited by 45 publications

(32 citation statements)

References 40 publications

(58 reference statements)

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“…After pre-processing (noises, void records, and bogus users) of 903,008 anonymized check-in records, 852,560 check-in records associated with the geographical area are picked up between January to May, 2016. Lastly, the task in the data insight stage is to analyze and investigate the features of LBSN check-in data by considering location, time, and gender and visualize data by using ArcGIS [136] to produce density maps [137] and trends [138,139]. 2016 denotes "day, month, date, time and year," m denotes "gender" and 113.854085, 23.527322 denotes geo-location.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…More generally, social media content biases have been observed across datasets. In a study of Twitter, Flickr, and Swarm, it was shown that volunteered geographic information is "local" (geotags correspond closely with the home locations of its contributors) in only about 75% of cases [27], and compounded by how localness is defined [28]. This effect influences the design of geolocation inference algorithms, which have been shown to exhibit significantly worse performance for underrepresented populations (i.e.…”

Section: Media Bias In Communication and Social Mediamentioning

confidence: 99%

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

Ali

Stratmann

Park

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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This paper investigates bias in coverage between Western and Arab media on Twitter after the November 2015 Beirut and Paris terror attacks. Using two Twitter datasets covering each attack, we investigate how Western and Arab media differed in coverage bias, sympathy bias, and resulting information propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets across four languages (English, Arabic, French, German), built a regression model to characterize sympathy, and thereafter trained a deep convolutional neural network to predict sympathy. Key findings show: (a) both events were disproportionately covered (b) Western media exhibited less sympathy, where each media coverage was more sympathetic towards the country affected in their respective region (c) Sympathy predictions supported ground truth analysis that Western media was less sympathetic than Arab media (d) Sympathetic tweets do not spread any further. We discuss our results in light of global news flow, Twitter affordances, and public perception impact.

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“…Another issue with traditional LBSNs is the uncertainty of localness. For example, LBSN users can write about a place while being physically at another place, which may violate the localness assumption of volunteered geographic information and generate inaccurate spatial data (Johnson, Sengupta, Schöning, & Hecht, ). With AR‐integrated LBSNs, the impact would be mitigated as most people are encouraged to create content in situ.…”

Section: The Fusion Of Ar and Lbsnsmentioning

confidence: 99%

Enriching the GIScience research agenda: Fusing augmented reality and location‐based social networks

Liu

Fuhrmann

2018

Transactions in GIS

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Augmented reality (AR) overlays real-world views or scenes with virtual, computer-generated objects that appear to visually coexist in the same space. Location-based social networks (LBSNs) are platforms for individuals to be connected through the interdependency derived from their physical locations and their location-tagged social media content. Current research and development in both areas focuses on integrating mobile-based AR and LBSNs. Several applications (e.g., Sekai Camera and Wallame) have been developed and commercialized successfully. However, little research has been done on the potential impacts and successful evaluation methods of AR-integrated LBSNs in the GIScience field. To close this gap, the article outlines the impacts and benefits of AR-integrated LBSNs and highlights the importance of LBSNs in GIScience research. Based on the status quo of AR-integrated LBSNs, this article discusses-from theoretical and application-oriented perspectives-how AR-integrated LBSNs could enrich the GIScience research agenda in three aspects: data conflation, platial GIS, and multimedia storytelling. The article concludes with guidelines on visualization, functionality, and ethics that aim to help users develop and evaluate AR-integrated LBSNs. | I NTR OD U CTI ONAugmented reality (AR) overlays real-world views or scenes with virtual, computer-generated objects that appear to visually coexist in the same space. It is well established in multiple domains and has achieved consumer market status, mainly due to the prevalence of smartphones equipped with high computational processors, high-resolution displays, and multiple sensors. As a result, AR is integrated into everyday applications, including games, marketing strategies, navigation aids, home design software, personal assistance and general education applications. However, one important aspect has not been discussed sufficiently: the potential application of AR in location-based social networks (LBSNs). LBSN discussions started early in 2010 when developers tried to better understand how to connect user locations with user social networks. LBSNs are tightly coupled with location information acquired from multiple sources,

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The Geography and Importance of Localness in Geotagged Social Media

Cited by 45 publications

References 40 publications

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

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

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

Enriching the GIScience research agenda: Fusing augmented reality and location‐based social networks

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