Abstract
How can we infer a tweet location? Are timestamps of tweets effective for the location inference? In this study, we propose a novel method for tweet location inference based on contents and timestamps of tweets. It is important to infer the locations of tweets for the services related to locations such as recommending restaurants, sending disaster-related information to users, and providing commercial messages to users. This study has two contributions: (1) we propose a novel method to infer tweet locations based on the contents and timestamps of tweets, and (2) we experimentally demonstrate the effectiveness of the proposed method using Twitter data. The experimental results suggest that the proposed method can infer tweet locations more precisely than a baseline that does not take the temporal association into account.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Tweets posted from Japan.
- 3.
- 4.
References
Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the CIKM 2010, pp. 759–768 (2010)
Chang, H.-W., Lee, D., Eltaher, M., Lee, J.: @Phillies tweeting from philly? predicting twitter user locations with spatial word usage. In: Proceedings of the ASONAM 2012, pp. 111–118 (2012)
Backstrom, L., Sun, E., Marlow, C.: Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the WWW 2010, pp. 61–70 (2010)
Kinsella, S., Murdock, V., O’Hare, N.: ‘I’m eating a sandwich in glasgow’: modeling locations with tweets. In: Proceedings of the SMUC 2011, pp. 61–68 (2011)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Proceeding of the NIPS 16, pp. 321–328 (2003)
Ikawa, Y., Enoki, M., Tatsubori, M.: Location inference using microblog messages. In: Proceedings of the WWW 2012, pp. 687–690 (2012)
Yamaguchi, Y., Amagasa, T., Kitagawa, H., Ikawa, Y.: Online user location inference exploiting spatiotemporal correlations in social streams. In: Proceedings of the CIKM 2014, pp. 1139–1148 (2014)
Eisenstein, J., O’Connor, B., Smith, N.A., Xing, E.P.: A latent variable model for geographic lexical variation. In: Proceedings of th EMNLP 2010, pp. 1277–1287 (2010)
Abrol, S., Khan, L.: Tweethood: agglomerative clustering on fuzzy k-closest friends with variable depth for location mining. In: Proceedings of the IEEE Second International Conference on Social Computing (SocialCom 2010), pp. 153–160 (2010)
Jurgens, D.: That’s what friends are for: inferring location in online social media platforms based on social relationships. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, pp. 273–282 (2013)
Acknowledgment
This research was partly supported by the program “Research and Development on Real World Big Data Integration and Analysis” of the Ministry of Education, Culture, Sports, Science and Technology, Japan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ueda, S., Yamaguchi, Y., Kitagawa, H., Amagasa, T. (2015). Tweet Location Inference Based on Contents and Temporal Association. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-26187-4_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26186-7
Online ISBN: 978-3-319-26187-4
eBook Packages: Computer ScienceComputer Science (R0)