# Estimating Twitter User Location Using Social Interactions A Content Based Approach

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6 when establishing the spatial distribution of all terms during the estimation of the probability distribution. The results given above are with the restriction that the accuracy was calculated for the top location (K=1) that was predicted by the estimator. By relaxing such a restriction, we can have a better understanding of the spatial distribution of the terms when estimating the user location with (RBPDM) rather than with (PDM), because we account for the information provided by the underlying social structure of interaction between users. The variation in estimator accuracy, with respect to the change in error distance, for the top K estimated cities, can be seen in Figure 4 (with K=2 and K=5), i.e., the actual location of a user appears in the top K cities of the estimated city list. It can be seen that the term distribution estimators using our method (RBPDM) performed significantly better than those using the PDM method, while considering the top two cities and the top five cities in the estimated list (Figure 4). For instance, in Figure 4, using RBPDM, the accuracy obtained by the estimator with K=2 was % with the error distance of 100 miles, as compared to the corresponding accuracy of % for PDM. Similarly, with K=5, the accuracy for RBPDM was 58.88% with an error distance of 300 miles, and that of PDM had an accuracy of 48.58%. This indicates that with an increase in K, accuracy increases with both methods, but the prediction of the estimator using the RBPDM technique is consistently better than that of the estimator using the PDM technique. Accuracy (%) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Error Distance PDM (K=1) PDM (K=2) PDM (K=5) RBPDM (K=1) RBPDM (K=2) RBPDM (K=5) Figure 4. Accuracy vs. Error Distance Increase On a negative note, the amount of data used in this experiment, to estimate the probability of a user s geographic location, was significantly more limited than the amount used in [2]. VII. CONCLUSION The experiment performed in this paper provides good insight into the problem of estimating a user s geographic location information purely based on the content of the user s publicly available information, while making use of the characteristics of the Twitter communication model. We use the concept of user interactions on Twitter and exploit the relationship between of different tweet message types. From the experimental results, we conclude that associating the tweet content of a conversation, containing reply-tweets, with the initial tweet s user s location (to obtain a spatial distribution of terms), improves the accuracy of estimating a user location. Further, the quality of this work can be refined by considering a larger data set that takes into account the reply messages for a given tweet. We would also like to see further improvements by combining the information in the underlying social network with additional information, to obtain a more accurate prediction of user location in a social network environment. ACKNOWLEDGMENT We thank Satyen Abrol for his valuable suggestions and his help in conducting the experiment. REFERENCES [1] L. Backstrom, E. Sun, and C. Marlow. Find me if you can: improving geographical prediction with social and spatial proximity. In WWW, [2] Z. Cheng, J. Caverlee, and K. Lee. You Are Where You Tweet: A Content-Based Approach to Geo-locating Twitter Users. In Proceeding of the 19th ACM Conference on Information and Knowledge Management (CIKM), Toronto, Oct 2010 [3] E. Amitay, N. Har'El, R. Sivan, and A. Soffer. Web-a-where: geotagging web content. In SIGIR, [4] S. Lee, D. Won and D. McLeod. Tag-geotag correlation in social network. In SSM `08 Proceeding of the 2008 ACM workshop on Search in social media. [5] S. Abrol and L. Khan. TweetHood: Agglomorative Clustering on Fuzzy k-closest Friends with Variable depth for Location Mining. SocialCom/PASSAT 2010: [6] C. Fink, C. Piatko, J. Mayfield, T. Finin, and J. Martineau. Geolocating blogs from their textual content. In AAAI 2009 Spring Symposia on Social Semantic Web: Where Web 2.0 Meets Web 3.0, [7] L. Backstrom, J. Kleinberg, R. Kumar, and J. Novak. Spatial variation in search engine queries. In WWW, 2008 [8] G. Friedland and R. Sommer. Cybercasing the Joint: On the Privacy Implications of Geo-Tagging. Proceedings of the Fifth USENIX Workshop on Hot Topics in Security (HotSec 10), Washington, D.C. [9] R. Heatherly, M. Kantarcioglu, and B. Thuraisingham. Social network classification incorporating link type. In IEEE Intelligence and Security Informatics, [10] Z. Cheng, J. Caverlee, K. Lee and D. Sui. Exploring Millions of Footprints in Location Sharing Services. In Proceeding of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM), Barcelona, July [11] A. Java, X. Song, T. Finin, and B. Tseng. Why we Twitter: Understanding microblogging usage and communities. In Proc. Joint 9th WEBKDD and 1 st SNA-KDD Workshop 2007, [12] B. Huberman and D. R. F. Wu. Social networks that matter: Twitter under the microscope. First Monday, 14, [13] J. Eisenstein, N. Smith and E. Xing, A Latent Variable Model for Geographic Lexical Variation, EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. 843

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