Abstract
Predicting the geolocation of social media users is one of the core tasks in many applications, such as rapid disaster response, targeted advertisement, and recommending local events. In this paper, we introduce a new approach for user geolocation that unifies users’ social relationships, textual content, and metadata. Our two key contributions are as follows: (1) We leverage semantic similarity between users’ posts to predict their geographic proximity. To achieve this, we train and utilize a powerful word embedding model over millions of tweets. (2) To deal with isolated users in the social graph, we utilize a stacking-based learning approach to predict users’ locations based on their tweets’ textual content and metadata. Evaluation on three standard Twitter benchmark datasets shows that our approach outperforms state-of-the-art user geolocation methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We consider uni-directional mentions, since bi-directional mentions are too rare to be useful in the datasets used in our experiments [30].
- 2.
- 3.
References
Ashktorab, Z., Brown, C., Nandi, M., Culotta, A.: Tweedr: mining twitter to inform disaster response. In: ISCRAM 2014 (2014)
Cha, M., Gwon, Y., Kung, H.T.: Twitter geolocation and regional classification via sparse coding. In: ICWSM 2015, pp. 582–585 (2015)
Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating twitter users. In: CIKM 2010, pp. 759–768. ACM (2010)
Compton, R., Jurgens, D., Allen, D.: Geotagging one hundred million twitter accounts with total variation minimization. In: BigData 2014, pp. 393–401. IEEE (2014)
Davis Jr., C.A., Pappa, G.L., Rocha de Oliveira, D.R., Arcanjo, F.L.: Inferring the location of twitter messages based on user relationships. Trans. GIS 15(6), 735–751 (2011)
Ebrahimi, M., ShafieiBavani, E., Wong, R., Chen, F.: Exploring celebrities on inferring user geolocation in twitter. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 395–406. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_31
Ebrahimi, M., ShafieiBavani, E., Wong, R., Chen, F.: Twitter user geolocation by filtering of highly mentioned users. JASIST (2018). https://doi.org/10.1002/asi.24011
Eisenstein, J., O’Connor, B., Smith, N.A., Xing, E.P.: A latent variable model for geographic lexical variation. In: EMNLP 2010, pp. 1277–1287. ACL (2010)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD 1996, vol. 96, pp. 226–231 (1996)
Han, B., Baldwin,T.: Lexical normalisation of short text messages: Makn sens a# twitter. In: ACL-HLT 2011, pp. 368–378. ACL (2011)
Han, B., Cook, P., Baldwin, T.: Geolocation prediction in social media data by finding location indicative words. In: COLING 2012, pp. 1045–1062 (2012)
Han, B., Cook, P., Baldwin, T.: Text-based twitter user geolocation prediction. Artif. Intell. Res. 49, 451–500 (2014)
Han, B., Hugo, A., Rahimi, A., Derczynski, L., Baldwin, T.: Twitter geolocation prediction shared task of the 2016 workshop on noisy user-generated text. In: WNUT 2016, pp. 213–217 (2016)
Hecht, B., Hong, L., Suh, B., Chi, E.H.: Tweets from Justin Bieber’s heart: the dynamics of the location field in user profiles. In: ACM SIGCHI 2011, pp. 237–246. ACM (2011)
Hong, L., Ahmed, A., Gurumurthy, S., Smola, A.J., Tsioutsiouliklis, K.: Discovering geographical topics in the twitter stream. In: WWW 2012, pp. 769–778. ACM (2012)
Hulden, M., Silfverberg, M., Francom, J.: Kernel density estimation for text-based geolocation. In: AAAI 2015, pp. 145–150 (2015)
Jayasinghe, G., Jin, B., Mchugh, J., Robinson, B., Wan, S.: Csiro data61 at the WNUT geo shared task. In: WNUT 2016, pp. 218–226 (2016)
Jurgens, D.: That’s what friends are for: inferring location in online social media platforms based on social relationships. In: ICWSM 2013, vol. 13, pp. 273–282 (2013)
Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: ICML 2015, pp. 957–966 (2015)
Li, R., Wang, S., Deng, H., Wang, R., Chang, K.C.-C.: Towards social user profiling: unified and discriminative influence model for inferring home locations. In: SIGKDD 2012, pp. 1023–1031. ACM (2012)
Lian, D., Ge, Y., Zhang, F., Yuan, N.J., Xie, X., Zhou, T., Rui, Y.: Content-aware collaborative filtering for location recommendation based on human mobility data. In: ICDM 2015, pp. 261–270. IEEE (2015)
Liu, J., Inkpen, D.: Estimating user location in social media with stacked denoising auto-encoders. In: NAACL-HLT 2015, pp. 201–210 (2015)
Mahmud, J., Nichols, J., Drews, C.: Where is this tweet from? inferring home locations of twitter users. In: ICWSM 2012, vol. 12, pp. 511–514 (2012)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS 2013, pp. 3111–3119 (2013)
Miura, Y., Taniguchi, M., Taniguchi, T., Ohkuma, T.: A simple scalable neural networks based model for geolocation prediction in twitter. In: WNUT 2016, pp. 235–239 (2016)
Qiang, J., Chen, P., Wang, T., Wu, X.: Topic modeling over short texts by incorporating word embeddings. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10235, pp. 363–374. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57529-2_29
Rahimi, A., Baldwin, T., Cohn, T.: Continuous representation of location for geolocation and lexical dialectology using mixture density networks. In: EMNLP 2017, pp. 167–176. ACL (2017)
Rahimi, A., Cohn, T., Baldwin, T.: Twitter user geolocation using a unified text and network prediction model. In: ACL-IJCNLP 2015, pp. 630–636. ACL (2015)
Rahimi, A., Cohn, T., Baldwin, T.: A neural model for user geolocation and lexical dialectology. In: ACL 2017 (2017)
Rahimi, A., Vu, D., Cohn, T., Baldwin, T.: Exploiting text and network context for geolocation of social media users. In: NAACL-HLT 2015, pp. 1362–1367. ACL (2015)
Roller, S., Speriosu, M., Rallapalli, S., Wing, B., Baldridge, J.: Supervised text-based geolocation using language models on an adaptive grid. In: EMNLP-CONLL 2012, pp. 1500–1510. ACL (2012)
Talukdar, P.P., Crammer, K.: New regularized algorithms for transductive learning. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5782, pp. 442–457. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04174-7_29
Wang, F., Lu, C.-T., Qu, Y., Yu, P.S.: Collective geographical embedding for geolocating social network users. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 599–611. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_47
Wing, B., Baldridge, J.: Hierarchical discriminative classification for text-based geolocation. In: EMNLP 2014, pp. 336–348. ACL (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Ebrahimi, M., ShafieiBavani, E., Wong, R., Chen, F. (2018). Leveraging Local Interactions for Geolocating Social Media Users. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_63
Download citation
DOI: https://doi.org/10.1007/978-3-319-93040-4_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93039-8
Online ISBN: 978-3-319-93040-4
eBook Packages: Computer ScienceComputer Science (R0)