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Mapping the global Twitter heartbeat: The geography of Twitter
Open accessAbstract
In just under seven years, Twitter has grown to count nearly 3% of the entire global population among its active users who have sent more than 170 billion 140-character messages. Today the service plays such a significant role in American culture that the Library of Congress has assembled a permanent archive of the site back to its first tweet, updated daily. With its open API, Twitter has become one of the most popular data sources for social research, yet the majority of the literature has focused on it as a text or network graph source, with only limited efforts to date focusing exclusively on the geography of Twitter, assessing the various sources of geographic information on the service and their accuracy. More than 3% of all tweets are found to have native location information available, while a naive geocoder based on a simple major cities gazetteer and relying on the user-provided Location and Profile fields is able to geolocate more than a third of all tweets with high accuracy when measured against the GPS-based baseline. Geographic proximity is found to play a minimal role both in who users communicate with and what they communicate about, providing evidence that social media is shifting the communicative landscape.
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Citations
Cited by
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
“Descriptive analyses are the basis of many studies, quantitatively depicting social data through numerical or graphical summaries of variables of interest, such as geographical distribution of messages ( Leetaru et al., 2013), or temporal associations among topics (Fourney et al., 2015).”
BackgroundGeo-located Twitter as proxy for global mobility patterns
Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014) · Cartography and Geographic Information Science · doi:10.1080/15230406.2014.890072 · Cited by 673
Spontaneous Collective Action: Peripheral Mobilization During the Arab Spring
STEINERT-THRELKELD, Z. C. (2017) · American Political Science Review · doi:10.1017/s0003055416000769 · Cited by 331
Social Media data: Challenges, opportunities and limitations in urban studies
Martí, P., Serrano-Estrada, L., & Nolasco-Cirugeda, A. (2019) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2018.11.001 · Cited by 325
Metadata
- Authors
- Leetaru, K., Wang, S., Cao, G., Padmanabhan, A., & Shook, E.
- Venue
- First Monday
- Publisher
- University of Illinois Libraries
- Published
- 2013-04-22
- ISSN
- 1396-0458 (ISSN-L)
- 1396-0466
- OpenAlex
- W2093588201
- Cited by
- 378
- References
- 0
- FWCI
- 106.23
- Type
- journal article
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.