A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 18th international conference on World wide web
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
The demographics of web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Image tagging and search: a gender oriented study
Proceedings of second ACM SIGMM workshop on Social media
Modeling locations with social media
Information Retrieval
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Geo-tagged content from social media platforms such as Flickr provide large amounts of data about any given location, which can be used to create models of the language used to describe locations. To date, models of location have ignored the differences between users. This paper focuses on one aspect of demographics, namely gender, and explores the relationship between gender and location in a large-scale corpus of geo-tagged Flickr images. We find that male users are much more likely to geo-tag their photos than female users, and that the geo-tagged photos of male users have wider geographic coverage than those of females. We create gender-based language models of location using the Flickr tags describing geo-tagged photos, and find that Flickr tags created by male users contain more geographic information than those created by female users, and that they can be located based on their tags far more accurately. Further, models created exclusively with data from male users are more accurate than those created from female users' data. Although our results suggest that there is some benefit from using gender-specific models, this benefit is quite minor, and is overwhelmed by the richer location information in the male data. The results also show that gender-based differences in location models are more important at the hyper-local level.