Inferring the Location of Twitter Messages Based on User Relationships

Abstract

User interaction in social networks, such as Twitter and Facebook, is increasingly becoming a source of useful information on daily events. The online monitoring of short messages posted in such networks often provides insight on the repercussions of events of several different natures, such as (in the recent past) the earthquake and tsunami in Japan, the royal wedding in Britain and the death of Osama bin Laden. Studying the origins and the propagation of messages regarding such topics helps social scientists in their quest for improving the current understanding of human relationships and interactions. However, the actual location associated to a tweet or to a Facebook message can be rather uncertain. Some tweets are posted with an automatically determined location (from an IP address), or with a user‐informed location, both in text form, usually the name of a city. We observe that most Twitter users opt not to publish their location, and many do so in a cryptic way, mentioning non‐existing places or providing less specific place names (such as “Brazil”). In this article, we focus on the problem of enriching the location of tweets using alternative data, particularly the social relationships between Twitter users. Our strategy involves recursively expanding the network of locatable users using following‐follower relationships. Verification is achieved using cross‐validation techniques, in which the location of a fraction of the users with known locations is used to determine the location of the others, thus allowing us to compare the actual location to the inferred one and verify the quality of the estimation. With an estimate of the precision of the method, it can then be applied to locationless tweets. Our intention is to infer the location of as many users as possible, in order to increase the number of tweets that can be used in spatial analyses of social phenomena. The article demonstrates the feasibility of our approach using a dataset comprising tweets that mention keywords related to dengue fever, increasing by 45% the number of locatable tweets.

Quantifying Interdependent Privacy Risks with Location Data

“…Recent revelations about NSA surveillance programs also show that this type of information is of great use for tracking and identifying individuals [30]. The dual problem, i.e., inferring location from social ties, has also been studied by the research community [31]- [33]. In [34], the authors exploit proximity information detected via Bluetooth, which is similar to co-location, to build an opportunistic ad-hoc localization algorithm by using intersection techniques similar to what we use in our attack.…”

Exploratory Spatio-Temporal Queries in Evolving Information

“…Geoparsing and toponym resolution has been addressed mainly using statistical techniques through trained models [23,21,30], also in combination with heuristics [20]. The social networks have been also used to infer the location of a user [8] or to disambiguate it [15] thanks to the additional contextual information. These researches focus mainly in the user home location rather than the locations mentioned in the messages, but recently it has been proved the usefulness of social networks also to overcome the problem of shortness and sparsity of tweet messages analyzing them with respect to other tasks.…”

Data Quality Challenges in Twitter Content Analysis for Informing Policy Making in Health Care

“…The first type looks into what can be inferred from the user's network of friends and the people that users most frequently or recently communicate with [17][18][19]. The other type looks into analyzing content or style of posts to infer user's profile data, such as age and gender, as it is done in [20], or by finding geographic references or regional language style to determine user's location [21,22].…”