%0 Conference Paper %1 Hecht:2011:TJB:1978942.1978976 %A Hecht, Brent %A Hong, Lichan %A Suh, Bongwon %A Chi, Ed H. %B Proceedings of the SIGCHI Conference on Human Factors in Computing Systems %C New York, NY, USA %D 2011 %I ACM %K k3 location mapping twitter %P 237--246 %R 10.1145/1978942.1978976 %T Tweets from Justin Bieber's Heart: The Dynamics of the Location Field in User Profiles %U http://doi.acm.org/10.1145/1978942.1978976 %X Little research exists on one of the most common, oldest, and most utilized forms of online social geographic information: the 'location' field found in most virtual community user profiles. We performed the first in-depth study of user behavior with regard to the location field in Twitter user profiles. We found that 34% of users did not provide real location information, frequently incorporating fake locations or sarcastic comments that can fool traditional geographic information tools. When users did input their location, they almost never specified it at a scale any more detailed than their city. In order to determine whether or not natural user behaviors have a real effect on the 'locatability' of users, we performed a simple machine learning experiment to determine whether we can identify a user's location by only looking at what that user tweets. We found that a user's country and state can in fact be determined easily with decent accuracy, indicating that users implicitly reveal location information, with or without realizing it. Implications for location-based services and privacy are discussed. %@ 978-1-4503-0228-9

@inproceedings{Hecht:2011:TJB:1978942.1978976, abstract = {Little research exists on one of the most common, oldest, and most utilized forms of online social geographic information: the 'location' field found in most virtual community user profiles. We performed the first in-depth study of user behavior with regard to the location field in Twitter user profiles. We found that 34% of users did not provide real location information, frequently incorporating fake locations or sarcastic comments that can fool traditional geographic information tools. When users did input their location, they almost never specified it at a scale any more detailed than their city. In order to determine whether or not natural user behaviors have a real effect on the 'locatability' of users, we performed a simple machine learning experiment to determine whether we can identify a user's location by only looking at what that user tweets. We found that a user's country and state can in fact be determined easily with decent accuracy, indicating that users implicitly reveal location information, with or without realizing it. Implications for location-based services and privacy are discussed.}, acmid = {1978976}, added-at = {2016-01-27T14:13:42.000+0100}, address = {New York, NY, USA}, author = {Hecht, Brent and Hong, Lichan and Suh, Bongwon and Chi, Ed H.}, biburl = {https://www.bibsonomy.org/bibtex/2186d97815388af45fd36600090965ee9/asmelash}, booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems}, description = {Tweets from Justin Bieber's heart}, doi = {10.1145/1978942.1978976}, interhash = {de11082ff229e11ddd9cca3facf7ca01}, intrahash = {186d97815388af45fd36600090965ee9}, isbn = {978-1-4503-0228-9}, keywords = {k3 location mapping twitter}, location = {Vancouver, BC, Canada}, numpages = {10}, pages = {237--246}, publisher = {ACM}, series = {CHI '11}, timestamp = {2016-01-27T14:13:42.000+0100}, title = {Tweets from Justin Bieber's Heart: The Dynamics of the Location Field in User Profiles}, url = {http://doi.acm.org/10.1145/1978942.1978976}, year = 2011 }