Geolocation for Twitter: Timing Matters
By Mark Dredze, Miles Osborne, Prabhanjan Kambadur. 2016
60 citations recorded by the source corpus.
Automated geolocation of social media messages can benefit a variety of downstream applications. However, these geolocation systems are typically evaluated without attention to how changes in time impact geolocation. Since different people, in different locations write messages at different times, these factors can significantly vary the performance of a ge-olocation system over time. We demonstrate cyclical temporal effects on geolocation accuracy in Twitter, as well as rapid drops as test data moves beyond the time period of training data. We show that temporal drift can effectively be countered with even modest online model updates.
Subfields: Computer Vision and Pattern Recognition, Information Systems, Statistical and Nonlinear Physics
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Citations in the corpus
- Predicting Twitter User Demographics from Names Alone (2018, methodology): “…methods have been proposed for inferring demographics based on the user’s name and profile, such as for geolocation, gender, or social roles (Dredze et al., 2013; Osborne et al., 2014; Dredze et al., 2016; Knowles et al., 2016; Volkova et al., 2013; Burger et al., 2011; Beller et al., 2014).”
- How Twitter is Changing the Nature of Financial News Discovery (2016, methodology): “This decision can be aided by methods that contextualize tweets [35], such as displaying inferred location [16, 20, 47, 9, 7, 28, 39, 14, 13] or topic context [29, 33].”