OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Uncovering social spammers: social honeypots + machine learning
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
| Hi-index | 0.00 |
In the last decade, the Internet has seen the rise of social networking as the number one online activity worldwide. To estimate the geographical location of users of social networks at a particular moment, we propose an approach to geo-tag Twitter users based only on the content of their posts. These data can later be used for local sentiment analysis, emergency detection, finding a missing person, and other novel location-based purposes. Our approach carries out a semantic analysis of tweet content to infer where in the globe a particular user is located at a given time. Based on our experimental results, conducted through Amazon Mechanical Turk, the proposed framework was evaluated by 93 evaluators who assessed 654 twitter user profiles and 2,165 tweets from 17 countries. Our system inferred some geographical information for 81% of evaluated profiles. Results show 79% accuracy in identifying the user's country and 66% accuracy in identifying the user's current location. This high accuracy shows that our proposed method is feasible and effective.