Geography of Twitter networks

By Yuri Takhteyev, Anatoliy A. Gruzd, Barry Wellman. Soc. Networks, 2012

601 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/39f856d7cb079f2580e2889b1f6f1b5ef2bc1039

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Citations in the corpus

The Twitter of Babel: Mapping World Languages through Microblogging Platforms (2012, background): “…have been identiﬁed for the Top-10 more active countries [18], language-dependent diﬀerences have been pointed out in the user activity related to the posting and conversations patterns [19], and language has been shown to be a strong predictor for the formation of follower/followee relations [20].”
Where in the World Are You? Geolocation and Language Identification in Twitter* (2013, methodology): “The disagreement of several studies on the most used languages in Twitter (Honeycutt and Herring 2009; Semiocast 2010; Hong, Convertino, and Chi 2011; Takhteyev, Gruzd, and Wellman 2012) highlights the difficulty of language detection.”
You Tweet What You Eat: Studying Food Consumption Through Twitter (2014, background): “Friends on Twitter are also likely to live in close proximity [24] and hence are likely to share similar estimates of demographic variables, based on geographic census data.”
Hip and trendy: Characterizing emerging trends on Twitter (2011, background): “—@waxpancake on Twitter Temporal patterns in data on social awareness streams such as Twitter are becoming increasingly important to our society’s information and communication landscape (Yardi & boyd, 2010; Takhteyev et al., 2010).”
Mapping the Data Shadows of Hurricane Sandy: Uncovering the Sociospatial Dimensions of ‘Big Data’ (2014, background): “…the research practices of corporations, states, journalists and academics (Lazer et al., 2009; Leetaru, 2011; Issenberg, 2012; Lohr, 2012; see also Torrens, 2010 for a geographic perspective), driven by the idea that the data shadows of people, machines, commodities, and even nature, can reveal…”
Assessing the bias in samples of large online networks (2014, background): “…is on the properties of the underlying following-follower structure, measured as a global network (Kwak et al., 2010) or at the level of dyads (Takhteyev et al., 2012); in other cases, it is on the more direct 18 S. González-Bailón et al. / Social Networks 38 (2014) 16–27 c a 2 ( t a d t m c…”
Geo-located Twitter as proxy for global mobility patterns (2013, background): “But even in the world of this subjective “shrinkage” of distances, certain level of dependency is preserved, possibly because of social ties, which remain stronger on a local than global scale (Takhteyev, Gruzd, and Wellman 2012).”