research-article

A content-driven framework for geolocating microblog users

Authors:

James Caverlee,

Kyumin LeeAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 4, Issue 1

Article No.: 2, Pages 1 - 27

https://doi.org/10.1145/2414425.2414427

Published: 01 February 2013 Publication History

Abstract

Highly dynamic real-time microblog systems have already published petabytes of real-time human sensor data in the form of status updates. However, the lack of user adoption of geo-based features per user or per post signals that the promise of microblog services as location-based sensing systems may have only limited reach and impact. Thus, in this article, we propose and evaluate a probabilistic framework for estimating a microblog user's location based purely on the content of the user's posts. Our framework can overcome the sparsity of geo-enabled features in these services and bring augmented scope and breadth to emerging location-based personalized information services. Three of the key features of the proposed approach are: (i) its reliance purely on publicly available content; (ii) a classification component for automatically identifying words in posts with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. On average we find that the location estimates converge quickly, placing 51% of users within 100 miles of their actual location.

References

[1]

Amitay, E., Har'El, N., Sivan, R., and 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.

Digital Library

[2]

Atkinson, K. 2007. Kevin's word list. http://wordlist.sourceforge.net

[3]

Backstrom, L., Kleinberg, J., Kumar, R., and Novak, J. 2008. Spatial variation in search engine queries. In Proceedings of the 17th International Conference on World Wide Web.

Digital Library

[4]

Backstrom, L., Sun, E., and Marlow, C. 2010. Find me if you can: Improving geographical prediction with social and spatial proximity. In Proceedings of the 19th International Conference on World Wide Web.

Digital Library

[5]

Beresford, A. R. and Stajano, F. 2003. Location privacy in pervasive computing. IEEE Pervas. Comput. 2, 1, 46--55.

Digital Library

[6]

Cheema, A. 2010. Twitter hits 20 billion tweets: Giga tweet. http://gopak.co.cc/social-media/twitter-socialmedia/twitter-hits-20-billion-tweets-giga-tweet/

[7]

Cheng, Z., Caverlee, J., and Lee, K. 2010. You are where you tweet: A content-based approach to geolocating twitter users. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management.

Digital Library

[8]

Crandall, D. J., Backstrom, L., Huttenlocher, D., and Kleinberg, J. 2009. Mapping the world's photos. In Proceedings of the 18th International Conference on World Wide Web.

Digital Library

[9]

Cranshaw, J., Toch, E., Hong, J., Kittur, A., and Sadeh, N. 2010. Bridging the gap between physical location and online social networks. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing.

Digital Library

[10]

Fink, C., Piatko, C., Mayfield, J., Finin, T., and Martineau, J. 2009. Geolocating blogs from their textual content. In Proceedings of the AAAI Spring Symposia on Social Semantic Web: Where Web 2.0 Meets Web 3.0.

[11]

Freni, D., Vicente, C. R., Mascetti, S., Bettini, C., and Jensen, C. S. 2010. Preserving location and absence privacy in geo-social networks. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management.

Digital Library

[12]

Heatherly, R., Kantarcioglu, M., and Thuraisingham, B. 2009. Social network classification incorporating link type values. In Proceedings of the IEEE International Conference on Intelligence and Security Informatics.

Digital Library

[13]

Huberman, B. A., Romero, D. M., and Wu, F. 2008. Social networks that matter: Twitter under the microscope. Social Science Research Network Working Paper Series.

[14]

Hurst, M., Siegler, M., and Glance, N. 2007. On estimating the geographic distribution of social media. In Proceedings of the International Conference on Weblogs and Social Media.

[15]

Java, A., Song, X., Finin, T., and Tseng, B. 2007. Why we twitter: Understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD Workshop on Web Mining and Social Network Analysis (WebKDD/SNA-KDD '07).

Digital Library

[16]

Johnson, S. 2009. How twitter will change the way we live. Time 6/5/09.

[17]

Kalnis, P., Ghinita, G., Mouratidis, K., and Papadias, D. 2007. Preventing location-based identity inference in anonymous spatial queries. IEEE Trans. Knowl. Data Engin. 19, 12, 1719--1733.

Digital Library

[18]

Lee, K., Caverlee, J., and Webb, S. 2010. Uncovering social spammers: Social honeypots + machine learning. In Proceedings of the 33rd ACM SIGIR International Conference on Research and Development in Information Retrieval.

Digital Library

[19]

Lin, J. and Halavais, A. 2004. Mapping the blogosphere in america. In Proceedings of the Workshop on the Weblogging Ecosystem at the 13th International World Wide Web Conference.

[20]

Lindamood, J., Heatherly, R., Kantarcioglu, M., and Thuraisingham, B. 2009. Inferring private information using social network data. In Proceedings of the 18th International Conference on World Wide Web.

Digital Library

[21]

McGee, J., Caverlee, J., and Cheng, Z. 2011. A geographic study of tie strength in social media. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management.

Digital Library

[22]

Miller, C. C. 2010. Sports fans break records on twitter. Blogs of New York Times.

[23]

Patrick, K. and Kevin, B. 2009. The local business owner's guide to twitter. http://domusconsultinggroup.com/wp-content/uploads/2009/06/090624-twitter-ebook.pdf

[24]

Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. 1986. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press.

Digital Library

[25]

Sakaki, T., Okazaki, M., and Matsuo, Y. 2010. Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the International Conference on World Wide Web.

Digital Library

[26]

Serdyukov, P., Murdock, V., and van Zwol, R. 2009. Placing flickr photos on a map. In Proceedings of the 32nd International and Development in Information Retrieval.

Digital Library

[27]

Twitter. 2007. Twitter's open api. http://apiwiki.twitter.com

[28]

Uscensusbureau. 2002. Census 2000. u.s.gazetteer.http://www.census.gov/geo/www/gazetteer/places2k.html

[29]

Witten, I. H. and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan Kaufmann.

Digital Library

[30]

Yamamoto, Y. 2007. Twitter4j open-source library. http://yusuke.homeip.net/twitter4j/en/index.html.

[31]

Yardi, S. and Boyd, D. 2010. Tweeting from the town square: Measuring geographic local networks. In Proceedings of the International AAAI Conference on Weblogs and Social Media.

[32]

Yi, X., Raghavan, H., and Leggetter, C. 2009. Discovering users' specific geo intention in web search. In Proceedings of the 18th International Conference on World Wide Web.

Digital Library

[33]

Zheng, Y., Zhang, L., Ma, Z., and Ma, W. Y. 2011. Recommending friends and locations based on individual location history. http://research.microsoft.com/pubs/122435/recomfriend-zheng-published.pdf

[34]

Zong, W., Wu, D., Sun, A., Lim, E.-P., and Goh, D. H.-L. 2005. On assigning place names to geography related web pages. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries.

Digital Library

Cited By

Liu YSingh L(2024)Utilizing External Knowledge to Enhance Location Prediction for Twitter/X Users in Low Resource SettingsACM Transactions on Spatial Algorithms and Systems10.1145/367389910:3(1-25)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3673899
Wu HLi JLi SLi HMa JQiao Y(2023)Find You: Multi-View-Based Location Inference for Twitter UsersApplied Sciences10.3390/app13211184813:21(11848)Online publication date: 30-Oct-2023
https://doi.org/10.3390/app132111848
Tusher AAnjum AIslam MDas SRezowana Sammy SHossain G(2023)Automated Phrasal Verb and Key-Phrase Checking with LSTM-Based Attention Mechanism2023 3rd International Conference on Computing and Information Technology (ICCIT)10.1109/ICCIT58132.2023.10273937(285-290)Online publication date: 13-Sep-2023
https://doi.org/10.1109/ICCIT58132.2023.10273937
Show More Cited By

Index Terms

A content-driven framework for geolocating microblog users
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 4, Issue 1

Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context

January 2013

357 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2414425

Issue’s Table of Contents

Copyright © 2013 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 February 2013

Accepted: 01 July 2012

Revised: 01 June 2012

Received: 01 May 2011

Published in TIST Volume 4, Issue 1

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Cited By

Liu YSingh L(2024)Utilizing External Knowledge to Enhance Location Prediction for Twitter/X Users in Low Resource SettingsACM Transactions on Spatial Algorithms and Systems10.1145/367389910:3(1-25)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3673899
Wu HLi JLi SLi HMa JQiao Y(2023)Find You: Multi-View-Based Location Inference for Twitter UsersApplied Sciences10.3390/app13211184813:21(11848)Online publication date: 30-Oct-2023
https://doi.org/10.3390/app132111848
Tusher AAnjum AIslam MDas SRezowana Sammy SHossain G(2023)Automated Phrasal Verb and Key-Phrase Checking with LSTM-Based Attention Mechanism2023 3rd International Conference on Computing and Information Technology (ICCIT)10.1109/ICCIT58132.2023.10273937(285-290)Online publication date: 13-Sep-2023
https://doi.org/10.1109/ICCIT58132.2023.10273937
Binti Abdul Ghani NHamid SAhmad MSaadi YJhanjhi NA. Alzain MMasud M(2022)Tracking Dengue on Twitter Using Hybrid Filtration-Polarity and Apache FlumeComputer Systems Science and Engineering10.32604/csse.2022.01846740:3(913-926)Online publication date: 2022
https://doi.org/10.32604/csse.2022.018467
Liu YLuo XZhang MTao ZLiu F(2022)Who are thereJournal of Network and Computer Applications10.1016/j.jnca.2021.103302199:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.jnca.2021.103302
Mostafa AGad WAbdelkader TBadr N(2022)Pre-HLSA: Predicting home location for Twitter users based on sentimental analysisAin Shams Engineering Journal10.1016/j.asej.2021.05.01513:1(101501)Online publication date: Jan-2022
https://doi.org/10.1016/j.asej.2021.05.015
Schneider NSamet H(2021)Which portland is it?Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3486183.3491066(1-10)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3486183.3491066
Zhang XLin FDong DChen WLiu B(2021)Heterogeneous Graph Attention Network for User GeolocationPRICAI 2021: Trends in Artificial Intelligence10.1007/978-3-030-89363-7_33(433-447)Online publication date: 1-Nov-2021
https://doi.org/10.1007/978-3-030-89363-7_33
Mostafa AGad WAbdelkader TBadr N(2020)Predicting the Tweet Location Based on KNN-Sentimental Analysis2020 15th International Conference on Computer Engineering and Systems (ICCES)10.1109/ICCES51560.2020.9334566(1-6)Online publication date: 15-Dec-2020
https://doi.org/10.1109/ICCES51560.2020.9334566
Ozdikis ORamampiaro HNørvåg K(2019)Locality-adapted kernel densities of term co-occurrences for location prediction of tweetsInformation Processing & Management10.1016/j.ipm.2019.02.01356:4(1280-1299)Online publication date: Jul-2019
https://doi.org/10.1016/j.ipm.2019.02.013
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