Mapping the global Twitter heartbeat: The geography of Twitter

By Kalev H. Leetaru, Shaowen Wang, Guofeng Cao, Anand Padmanabhan, Eric Shook. 2013

403 citations recorded by the source corpus.

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.

Subfields: Geography, Planning and Development, Transportation

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “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).”
Twitter as Data (2018, background): “Tweets with GPS coordinates, which represent 2-3% of all tweets (Leetaru et al., 2013), provide the most precision in estimating location.”
A Manifesto, in 140 Characters or Fewer: Social Media as a Tool of Rebel Diplomacy (2017, neutral): “20 Leetaru et al. 2013.”
Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning (2017, background): “The GTSM data provide an unprecedented coverage of today’s major cities [22] and serve as a detailed proxy for understanding human activities [17, 5, 27, 18, 8, 26].”
Assessing the utility of social media as a data source for flood risk management using a real‐time modelling framework (2017, background): “Comparison of locations geocoded from the text within Tweets against the actual location of the user from geotags suggests even when Tweets are geotagged, this data can rarely be considered reliable for inferring flooded locations (Leetaru et al., 2013).”
Geo-located Twitter as proxy for global mobility patterns (2013, background): “Leetaru et al. (2013) attempted to describe the geography of Twitter based on a one-month sample of global geolocated tweets, while Mocanu et al. (2013) described the global distribution of the different languages used while tweeting.”
Social Media data: Challenges, opportunities and limitations in urban studies (2019, methodology): “…of the most commonly cited limitations associated with the use of LBSNs refer to the lack of consistency in the provision of an acceptable amount of valid geocoded data for each sample (Boyd & Crawford, 2012; Cerrone, 2015; Leetaru, Wang, Cao, Padmanabhan, & Shook, 2013; Sloan & Quan-Haase, 2017).”
State of the Geotags: Motivations and Recent Changes (2017, background): “One study estimated 600,000 geotagged posts per day from a 10% sample of Twitter (Leetaru et al. 2013).”
Mapping the Data Shadows of Hurricane Sandy: Uncovering the Sociospatial Dimensions of ‘Big Data’ (2014, background): “For example, the physicist Geoffrey West has supposedly ‘solved the city’ using mathematical approaches, without having ever read any work in urban studies (Lehrer, 2010), while others have used similar databases of Twitter activity to revive the ‘death of distance’ thesis (Leetaru et al., 2013).”
Protest Activity Detection and Perceived Violence Estimation from Social Media Images (2017, neutral): “Twitter returns all tweets with GPS coordinates up to 1% of the total volume of tweets at a given time; since approximately 2% of tweets contain GPS coordinates [5, 25], we estimate we have collected half of all tweets with GPS coordinates (approximately 6.4 billion).”
Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks (2016, result): “Similar results have also been reported in Leetaru et al. [2013]. By excluding the unknown languages (i.”
Collective aspects of privacy in the Twitter social network (2018, background): “The top panel of Figure 1 shows the locations of users in the dataset, illustrating that users come from a wide variety of countries but are generally located in countries where Twitter adoption is high [27, 28].”
Population Bias in Geotagged Tweets (2015, background): “• discourse (Leetaru et al. 2013), • information diffusion and flows (Kamath et al.”