Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Meta Paths and Meta Structures: Analysing Large Heterogeneous Information Networks Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Local-Global LDA Model for Discovering Geographical Topics from Social Media

Authors : Siwei Qiang, Yongkun Wang, Yaohui Jin

Published in: Web and Big Data

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Micro-blogging services can track users’ geo-locations when users check-in their places or use geo-tagging which implicitly reveals locations. This “geo tracking” can help to find topics triggered by certain events in certain regions. However, discovering such topics is very challenging because of the large amount of noisy messages (e.g. daily conversations). This paper proposes a method to model geographical topics, which can filter out irrelevant words by different weights in the local and global contexts. Our method is based on the Latent Dirichlet Allocation (LDA) model but each word is generated from either a local or a global topic distribution by its generation probabilities. We evaluated our model with data collected from Weibo, which is currently the most popular micro-blogging service for Chinese. The evaluation results demonstrate that our method outperforms other baseline methods in several metrics such as model perplexity, two kinds of entropies and KL-divergence of discovered topics.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter TrajSpark: A Scalable and Efficient In-Memory Management System for Big Trajectory Data
next chapter Team-Oriented Task Planning in Spatial Crowdsourcing
Literature
1.
Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR, pp. 50–57. ACM (1999)
2.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH
3.
Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. In: UbiComp, pp. 275–286 (2003)
4.
Mei, Q., Liu, C., Su, H., Zhai, C.: A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In: WWW, pp. 533–542. ACM (2006)
5.
Wang, C., Wang, J., Xie, X., Ma, W.Y.: Mining geographic knowledge using location aware topic model. In: GIR, pp. 65–70. ACM (2007)
6.
Backstrom, L., Kleinberg, J., Kumar, R., Novak, J.: Spatial variation in search engine queries. In: WWW, pp. 357–366. ACM (2008)
7.
Palma, A.T., Bogorny, V., Kuijpers, B., Alvares, L.O.: A clustering-based approach for discovering interesting places in trajectories. In: SAC (2008)
8.
Li, H., Li, Z., Lee, W.C., Lee, D.L.: A probabilistic topic-based ranking framework for location-sensitive domain information retrieval. In: SIGIR, pp. 331–338. ACM (2009)
9.
Sizov, S.: Geofolk: latent spatial semantics in web 2.0 social media. In: WSDM, pp. 281–290. ACM (2010)
10.
Mathioudakis, M., Koudas, N.: Identifying, attributing and describing spatial bursts. In: Proceedings of the VLDB Endowment, pp. 1091–1102. ACM (2010)
11.
Eisenstein, J., Connor, B.O., Smith, N.A., Xing, E.P.: A latent variable model for geographic lexical variation. In: EMNLP, pp. 1277–1287. ACM (2010)
12.
Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating twitter users. In: CIKM, pp. 759–768. ACM (2010)
13.
Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: SIGKDD, pp. 1099–1108. ACM (2010)
14.
Yin, Z., Cao, L., Han, J., Zhai, C., Huang, T.: Geographical topic discovery and comparison. In: WWW, pp. 247–256. ACM (2011)
15.
Ye, M., Yin, P., Lee, W.C., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: SIGIR, pp. 325–334. ACM (2011)
16.
Hong, L., Ahmed, A., Gurumurthy, S., Smola, A.J., Tsioutsiouliklis, K.: Discovering geographical topics in the Twitter stream. In: WWW, pp. 769–778. ACM (2012)
17.
Bauer, S., Noulas, A., Seaghdha, D.O., Clark, S., Mascolo, C.: Talking places: modelling and analyzing linguistic content in foursquare. In: SocialCom/PASSAT, pp. 348–357. IEEE (2012)
18.
Hu, B., Ester, M.: Spatial topic modeling in online social media for location recommendation. In: RecSys, pp. 25–32. ACM (2013)
19.
Hu, B., Jamali, M., Ester, M.: Spatio-temporal topic modeling in mobile social media for location recommendation. In: ICDM, pp. 1073–1078. ACM (2013)
20.
Ahmed, A., Hong, L., Smola, A.J.: Hierarchical geographical modeling of user locations from social media posts. In: WWW, pp. 25–36. ACM (2013)
21.
Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Who, where, when and what: discover spatio-temporal topics for twitter users. In: SIGKDD, pp. 605–613. ACM (2013)
22.
Kim, Y., Han, H., Yuan, C.: TOPTRAC: topical trajectory pattern mining. In: SIGKDD, pp. 587–596. ACM (2015)
23.
Liu, Y., Ester, M., Hu, B., Cheung, D.W.: Spatio-temporal topic models for check-in data. In: ICDM, pp. 889–894. IEEE (2015)
24.
Wu, F., Li, Z., Lee, W.C., Wang, H., Huang, Z.: Semantic annotaion of mobility data using social media. In: WWW, pp. 1253–1263. ACM (2015)
25.
26.
Metadata
Title
A Local-Global LDA Model for Discovering Geographical Topics from Social Media
Authors
Siwei Qiang
Yongkun Wang
Yaohui Jin
Copyright Year
2017
Book
Web and Big Data

Print ISBN: 978-3-319-63578-1

Electronic ISBN: 978-3-319-63579-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63579-8_3

Premium Partner

    Image Credits