CoChat
Sign Up
Cited by 148

Text-Based Twitter User Geolocation Prediction

Han, B., Cook, P., Baldwin, T.·2014·Journal of Artificial Intelligence Research
DOI 10.1613/jair.4200
Open this paper in CoChat — free account

Abstract

Geographical location is vital to geospatial applications like local search and event detection. In this paper, we investigate and improve on the task of text-based geolocation prediction of Twitter users. Previous studies on this topic have typically assumed that geographical references (e.g., gazetteer terms, dialectal words) in a text are indicative of its author’s location. However, these references are often buried in informal, ungrammatical, and multilingual data, and are therefore non-trivial to identify and exploit. We present an integrated geolocation prediction framework and investigate what factors impact on prediction accuracy. First, we evaluate a range of feature selection methods to obtain “location indicative words”. We then evaluate the impact of non-geotagged tweets, language, and user-declared metadata on geolocation prediction. In addition, we evaluate the impact of temporal variance on model generalisation, and discuss how users differ in terms of their geolocatability. We achieve state-of-the-art results for the text-based Twitter user geolocation task, and also provide the most extensive exploration of the task to date. Our findings provide valuable insights into the design of robust, practical text-based geolocation prediction systems.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1613/jair.4200

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In open scenarios, alternative API endpoints, random 1% streaming sample, or geolocation filters can be used to collect broader posts.

    “A solution for this is to use alternative API endpoints to collect posts through a less restrictive stream of data, such as Twitter's streaming API sampling a random 1% of the whole, or a filter of posts by geolocation, where available, to collect posts coming from a country or region (Han et al. 2014).”

    From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Han et al. 2014

    Section 3.4 (Data; page unavailable) says the authors collected a global dataset through Twitter’s public Streaming API, supporting that part of the citation. It does not say the stream was a random 1% sample, describe alternative API endpoints, or recommend API geolocation filters for collecting posts from a country or region; its geographic filtering describes preprocessing collected data. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 69

  1. Ahmed, A., Hong, L., & Smola, A. J. (2013). Hierarchical geographical modeling of user locations from social media posts. In Proceedings of the 22nd international conference on World Wide Web, WWW '13, pp. 25-36, Rio de Janeiro, Brazil.
  2. Amitay, E., Har'El, N., Sivan, R., & Soffer, A. (2004). Web-a-where: geotagging web content. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2004), pp. 273-280, Sheffield, UK.
  3. Backstrom, L., Kleinberg, J., Kumar, R., & Novak, J. (2008). Spatial variation in search engine queries. In Proceeding of the 17th international conference on World Wide Web, WWW '08, pp. 357-366, Beijing, China.
  4. Backstrom, L., Sun, E., & Marlow, C. (2010). Find me if you can: improving geographical predic- tion with social and spatial proximity. In Proceedings of the 19th International Conference on World Wide Web, pp. 61-70, Raleigh, USA.
  5. Baldwin, T., Cook, P., Lui, M., MacKinlay, A., & Wang, L. (2013). How noisy social media text, how diffrnt social media sources?. In Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP 2013), pp. 356-364, Nagoya, Japan.
  6. Bennett, P. N., Radlinski, F., White, R. W., & Yilmaz, E. (2011). Inferring and using location metadata to personalize web search. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '11, pp. 135-144, Beijing, China.
  7. Bentley, J. L. (1975). Multidimensional binary search trees used for associative searching. Commu- nication of the ACM, 18(9), 509-517.
  8. Bergsma, S., Dredze, M., Van Durme, B., Wilson, T., & Yarowsky, D. (2013). Broadly improving user classification via communication-based name and location clustering on Twitter. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2013), pp. 1010- 1019, Atlanta, USA.
  9. Bilhaut, F., Charnois, T., Enjalbert, P., & Mathet, Y. (2003). Geographic reference analysis for geo- graphic document querying. In Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references -Volume 1, pp. 55-62, Edmonton, Canada.
  10. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
  11. Buyukokkten, O., Cho, J., Garcia-Molina, H., Gravano, L., & Shivakumar, N. (1999). Exploiting geographical location information of web pages. In ACM SIGMOD Workshop on The Web and Databases (WebDB'99), pp. 91-96, Philadelphia, USA.
  12. Carletta, J. (1996). Assessing agreement on classification tasks: the kappa statistic. Computational Linguistics, 22(2), 249-254.
  13. Chandra, S., Khan, L., & Muhaya, F. (2011). Estimating Twitter user location using social interactions-a content based approach. In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom), pp. 838-843, Boston, USA.
  14. Chang, H.-w., Lee, D., M., E., & Lee, J. (2012). @Phillies tweeting from Philly? predicting Twitter user locations with spatial word usage. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 111-118, Istanbul, Turkey.
  15. Cheng, Z., Caverlee, J., & Lee, K. (2010). You are where you tweet: a content-based approach to geo-locating twitter users. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 759-768, Toronto, Canada.
  16. Cho, E., Myers, S. A., & Leskovec, J. (2011). Friendship and mobility: user movement in location- based social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082-1090, San Diego, USA.
  17. Crandall, D. J., Backstrom, L., Huttenlocher, D., & Kleinberg, J. (2009). Mapping the world's photos. In Proceedings of the 18th international conference on World wide web, WWW '09, pp. 761-770, Madrid, Spain.
  18. Dalvi, N., Kumar, R., & Pang, B. (2012). Object matching in tweets with spatial models. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM 2012), pp. 43-52, Seattle, USA.
  19. Ding, J., Gravano, L., & Shivakumar, N. (2000). Computing geographical scopes of web resources. In Proceedings of the 26th International Conference on Very Large Data Bases, VLDB '00, pp. 545-556, Cairo, Egypt.
  20. Dunning, T. (1993). Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1), 61-74.
  21. Eisenstein, J., O'Connor, B., Smith, N. A., & Xing, E. P. (2010). A latent variable model for geographic lexical variation. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP 2010), pp. 1277-1287, Cambridge, USA.
  22. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., & Lin, C.-J. (2008). LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9, 1871-1874.
  23. Gelernter, J., & Mushegian, N. (2011). Geo-parsing messages from microtext. Transactions in GIS, 15(6), 753-773.
  24. Giacinto, G., & Roli, F. (2001). Design of effective neural network ensembles for image classifica- tion purposes. Image and Vision Computing, 19(9-10), 699-707.
  25. Gouws, S., Metzler, D., Cai, C., & Hovy, E. (2011). Contextual bearing on linguistic variation in social media. In Proceedings of the Workshop on Languages in Social Media, LSM '11, pp. 20-29, Portland, USA.
  26. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.
  27. Han, B., Cook, P., & Baldwin, T. (2012a). Automatically constructing a normalisation dictionary for microblogs. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning 2012 (EMNLP-CoNLL 2012), pp. 421-432, Jeju, Korea.
  28. Han, B., Cook, P., & Baldwin, T. (2012b). Geolocation prediction in social media data by finding location indicative words. In Proceedings of the 24th International Conference on Computa- tional Linguistics, pp. 1045-1062, Mumbai, India.
  29. Han, B., Cook, P., & Baldwin, T. (2013). A stacking-based approach to Twitter user geolocation prediction. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 7-12, Sofia, Bulgaria.
  30. Hauff, C., & Houben, G.-J. (2012). Geo-location estimation of Flickr images: social web based enrichment. In Proceedings of the 34th European Conference on Advances in Information Retrieval, pp. 85-96, Barcelona, Spain.
  31. Hecht, B., Hong, L., Suh, B., & Chi, E. H. (2011). Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 237-246, Vancouver, Canada.
  32. Hong, L., Ahmed, A., Gurumurthy, S., Smola, A. J., & Tsioutsiouliklis, K. (2012). Discovering geographical topics in the Twitter stream. In Proceedings of the 21st International Conference on World Wide Web (WWW 2012), pp. 769-778, Lyon, France.
  33. Hong, L., Convertino, G., & Chi, E. H. (2011). Language matters in Twitter: A large scale study. In Proceedings of the 5th International Conference on Weblogs and Social Media (ICWSM 2011), pp. 518-521, Barcelona, Spain.
  34. Jurgens, D. (2013). That's what friends are for: Inferring location in online social media platforms based on social relationships. In Proceedings of the 7th International Conference on Weblogs and Social Media (ICWSM 2013), pp. 273-282, Boston, USA.
  35. Kinsella, S., Murdock, V., & O'Hare, N. (2011). "I'm eating a sandwich in Glasgow": modeling locations with tweets. In Proceedings of the 3rd international workshop on Search and mining user-generated contents, pp. 61-68, Glasgow, UK.
  36. Laere, O. V., Quinn, J., Schockaert, S., & Dhoedt, B. (2014). Spatially-aware term selection for geotagging. IEEE Transactions on Knowledge and Data Engineering, 26(1), 221-234.
  37. Leidner, J. L., & Lieberman, M. D. (2011). Detecting geographical references in the form of place names and associated spatial natural language. SIGSPATIAL Special, 3(2), 5-11.
  38. Li, R., Wang, S., & Chang, K. C.-C. (2012). Multiple location profiling for users and relationships from social network and content. VLDB, 5(11), 1603-1614.
  39. Li, W., Serdyukov, P., de Vries, A. P., Eickhoff, C., & Larson, M. (2011). The where in the tweet. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM 2011), pp. 2473-2476, Glasgow, UK.
  40. Lieberman, M. D., & Lin, J. (2009). You are where you edit: Locating Wikipedia contributors through edit histories. In Proceedings of the 3rd International Conference on Weblogs and Social Media (ICWSM 2009), pp. 106-113, San Jose, USA.
  41. Lui, M., & Baldwin, T. (2012). langid.py: An off-the-shelf language identification tool. In Pro- ceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012) Demo Session, pp. 25-30, Jeju, Korea.
  42. Mahmud, J., Nichols, J., & Drews, C. (2012). Where is this tweet from? Inferring home locations of Twitter users. In Proceedings of the 6th International Conference on Weblogs and Social Media (ICWSM 2012), pp. 511-514, Dublin, Ireland.
  43. Mao, H., Shuai, X., & Kapadia, A. (2011). Loose tweets: an analysis of privacy leaks on Twitter. In Proceedings of the 10th Annual ACM Workshop on Privacy in the Electronic Society, pp. 1-12, Chicago, USA.
  44. Ng, A. Y., & Jordan, M. I. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. In Advances in Neural Information Processing Systems 14 (NIPS-02), pp. 841-848, Whistler, Canada.
  45. Nocedal, J. (1980). Updating quasi-Newton matrices with limited storage. Mathematics of Compu- tation, 35(151), 773-782.
  46. Núnez-Redó, M., Díaz, L., Gil, J., González, D., & Huerta, J. (2011). Discovery and integration of Web 2.0 content into geospatial information structures: a use case in wild fire monitoring. In Proceedings of the 6th International Conference on Availability, Reliability and Security, pp. 50-68, Vienna, Austria.
  47. O'Connor, B., Krieger, M., & Ahn, D. (2010). TweetMotif: Exploratory search and topic summa- rization for Twitter. In Proceedings of Fourth International AAAI Conference on Weblogs and Social Media, pp. 384-385, Washington, D.C., USA.
  48. O'Hare, N., & Murdock, V. (2013). Modeling locations with social media. Information Retrieval, 16(1), 30-62.
  49. O'Sullivan, D., & Unwin, D. J. (2010). Point Pattern Analysis, pp. 121-155.
  50. Padmanabhan, V. N., & Subramanian, L. (2001). An investigation of geographic mapping tech- niques for internet hosts. In Proceedings of the 2001 Conference on Applications, Tech- nologies, Architectures, and Protocols for Computer Communications, SIGCOMM '01, pp. 173-185, San Diego, USA.
  51. Pontes, T., Vasconcelos, M., Almeida, J., Kumaraguru, P., & Almeida, V. (2012). We know where you live: Privacy characterization of Foursquare behavior. In 4th International Workshop on Location-Based Social Networks (LBSN 2012), Pittsburgh, USA.
  52. Priedhorsky, R., Culotta, A., & Valle, S. Y. D. (2014). Inferring the origin locations of tweets with quantitative confidence. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing, Baltimore, USA. To appear.
  53. Quercini, G., Samet, H., Sankaranarayanan, J., & Lieberman, M. D. (2010). Determining the spatial reader scopes of news sources using local lexicons. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '10, pp. 43- 52, San Jose, USA.
  54. Morgan Kaufmann, San Mateo, USA.
  55. Ritter, A., Clark, S., Mausam, & Etzioni, O. (2011). Named entity recognition in tweets: An ex- perimental study. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1524-1534, Edinburgh, UK.
  56. Roller, S., Speriosu, M., Rallapalli, S., Wing, B., & Baldridge, J. (2012). Supervised text-based geolocation using language models on an adaptive grid. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Nat- ural Language Learning, pp. 1500-1510, Jeju Island, Korea.
  57. Rout, D. P., Bontcheva, K., Preotiuc-Pietro, D., & Cohn, T. (2013). Where's @wally?: A classifica- tion approach to geolocating users based on their social ties. In Proceedings of the 24th ACM Conference on Hypertext and Social Media, pp. 11-20, Paris, France.
  58. Sadilek, A., Kautz, H., & Bigham, J. P. (2012). Finding your friends and following them to where you are. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 723-732, Seattle, USA.
  59. Schulz, A., Hadjakos, A., Paulheim, H., Nachtwey, J., & Mühlhäuser, M. (2013). A multi-indicator approach for geolocalization of tweets. In Proceedings of the 7th International Conference on Weblogs and Social Media (ICWSM 2013), pp. 573-582, Boston, USA.
  60. Serdyukov, P., Murdock, V., & van Zwol, R. (2009). Placing Flickr photos on a map. In Pro- ceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2009), pp. 484-491, Boston, USA.
  61. Vapnik, V. N. (1995). The nature of Statistical Learning Theory. Springer-Verlag, New York, USA.
  62. Vincenty, T. (1975). Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Survey Review, 22(176), 88-93.
  63. Wang, L., Wang, C., Xie, X., Forman, J., Lu, Y., Ma, W.-Y., & Li, Y. (2005). Detecting dominant locations from search queries. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2005), pp. 424- 431, Salvador, Brazil.
  64. Wing, B. P., & Baldridge, J. (2011). Simple supervised document geolocation with geodesic grids. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 955-964, Portland, USA.
  65. Yang, Y., & Pedersen, J. O. (1997). A comparative study on feature selection in text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, ICML '97, pp. 412-420, San Francisco, USA.
  66. Yi, X., Raghavan, H., & Leggetter, C. (2009). Discovering users' specific geo intention in web search. In Proceedings of the 18th International Conference on World Wide Web, WWW '09, pp. 481-490, Madrid, Spain.
  67. Yin, J., Lampert, A., Cameron, M., Robinson, B., & Power, R. (2012). Using social media to enhance emergency situation awareness. Intelligent Systems, 27(6), 52-59.
  68. Yin, Z., Cao, L., Han, J., Zhai, C., & Huang, T. (2011). Geographical topic discovery and compari- son. In Proceedings of the 20th International Conference on World Wide Web, pp. 247-256, Hyderabad, India.
  69. Zong, W., Wu, D., Sun, A., Lim, E.-P., & Goh, D. H.-L. (2005). On assigning place names to geography related web pages. In ACM/IEEE Joint Conference on Digital Libraries, pp. 354- 362, Denver, USA.

Cited by

Sorted by citations

Authors
Han, B., Cook, P., & Baldwin, T.
Venue
Journal of Artificial Intelligence Research
Publisher
AI Access Foundation
Published
2014-03-20
Volume
49
Pages
451-500
ISSN
1076-9757
DOI
10.1613/jair.4200
Cited by
148
References
1

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review