Next Article in Journal
An Analysis of Existing Production Frameworks for Statistical and Geographic Information: Synergies, Gaps and Integration
Next Article in Special Issue
Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics
Previous Article in Journal
A GloVe-Based POI Type Embedding Model for Extracting and Identifying Urban Functional Regions
Previous Article in Special Issue
Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data
Article

Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?

1
School of Information Science, University of South Carolina, Columbia, SC 29208, USA
2
Computer Science and Engineering Department, University of South Carolina, Columbia, SC 29208, USA
3
Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Ourania Kounadi, Bernd Resch and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(6), 373; https://doi.org/10.3390/ijgi10060373
Received: 8 March 2021 / Revised: 21 May 2021 / Accepted: 28 May 2021 / Published: 2 June 2021
(This article belongs to the Special Issue Applications and Implications in Geosocial Media Monitoring)
Twitter’s APIs are now the main data source for social media researchers. A large number of studies have utilized Twitter data for diverse research interests. Twitter users can share their precise real-time location, and Twitter APIs can provide this information as longitude and latitude. These geotagged Twitter data can help to study human activities and movements for different applications. Compared to the mostly small-scale data samples in different domains, such as social science, collecting geotagged data offers large samples. There is a fundamental question whether geotagged users can represent non-geotagged users. While some studies have investigated the question from different perspectives, they did not investigate profile information and the contents of tweets of geotagged and non-geotagged users. This empirical study addresses this limitation by applying text mining, statistical analysis, and machine learning techniques on Twitter data comprising more than 88,000 users and over 170 million tweets. Our findings show that there is a significant difference (p-value < 0.001) between geotagged and non-geotagged users based on 73% of the features obtained from the users’ profiles and tweets. The features can also help to distinguish between geotagged and non-geotagged users with around 80% accuracy. This research illustrates that geotagged users do not represent the Twitter population. View Full-Text
Keywords: social media; Twitter; geotagging; text analysis; topic modeling; linguistic analysis; big data analytics social media; Twitter; geotagging; text analysis; topic modeling; linguistic analysis; big data analytics
Show Figures

Figure 1

MDPI and ACS Style

Karami, A.; Kadari, R.R.; Panati, L.; Nooli, S.P.; Bheemreddy, H.; Bozorgi, P. Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population? ISPRS Int. J. Geo-Inf. 2021, 10, 373. https://doi.org/10.3390/ijgi10060373

AMA Style

Karami A, Kadari RR, Panati L, Nooli SP, Bheemreddy H, Bozorgi P. Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population? ISPRS International Journal of Geo-Information. 2021; 10(6):373. https://doi.org/10.3390/ijgi10060373

Chicago/Turabian Style

Karami, Amir, Rachana R. Kadari, Lekha Panati, Siva P. Nooli, Harshini Bheemreddy, and Parisa Bozorgi. 2021. "Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?" ISPRS International Journal of Geo-Information 10, no. 6: 373. https://doi.org/10.3390/ijgi10060373

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop