Home is where your friends are: Utilizing the social graph to locate twitter users in a city

Abstract

Micro-blogging services such as Twitter have gained enormous popularity over the last few years leading to massive volumes of user generated content. A portion of this content is shared via geoaware mobile devices, such as smartphones. Pieces of information shared on such a device can be tagged with the user's location, conditional on the user's settings. These geostamps enable a number of mainstream applications, such as emergency response, disease tracking, news reporting, and advertising. Unfortunately, informative geostamps are typically sparse, since content is often shared via devices that do not support geo-tagging, such as desktop or laptop computers. In addition, even if a mobile device is used, a flawed geo-location service can lead to missing geostamps, or geostamps that are too general to be informative. In this work, we address this sparsity issue via a new approach that identifies users attached to a given location of interest, such as a city. We then focus on retrieving specific tweets at a finer granularity within the given location, such as specific blocks within a city. Our approach leverages the correlation between strong connectivity in the social graph and proximity in the real world, while utilizing both textual tweet content and Twitter's underlying social graph. Previous relevant work assumes that all required Twitter data is available without access restrictions. This is an unrealistic assumption, since Twitter limits the number of data requests per user and charges a subscription fee for unrestricted access. Therefore, in order to increase the number of practitioners and applications that can benefit from our work, we optimize our method to work with the minimum amount of queries to the Twitter API. Finally, our experiments demonstrate the efficacy of our work via both a quantitative and qualitative evaluation.

Enhanced geocoding precision for location inference of tweet text using spaCy, Nominatim and Google Maps. A comparative analysis of the influence of data selection

“…Their highest F-measure reported from their study was 37.71%. Again using social graphs, [26], analysed user home locations and managed to infer about 80% users within the home city of Dublin. Recently, [11], used the time of opening of a twitter account together with the tweet language to infer the users home location.…”

A Google Trends spatial clustering approach for a worldwide Twitter user geolocation

“…More importantly, social media typically does not disclose IP data, thus two main alternative approaches have been proposed: Friendship Networks (FN) or WD analysis. The FD approach uses a social network analysis, in which the user location is assumed to be close to the locations of her/his friends (Kotzias et al, 2016). This work is focused on WD geolocation, which involves a pure analysis of user texts.…”

Restricted Spatiality and the Inflation of Digital Space, an Urban Perspective

“…When public life is to be suspended until the end of quarantine, virtual representations become central in urban practice. A quarantine pushes against the strength of local proximity as a critical force in urban life (Iranmanesh & Atun, 2018;Kotzias et al, 2015;Tobler, 1970), forcing interactions to become more virtual. Consequently, the affordance that the urban space provides for interaction shifts toward digital space.…”