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:
Analysis of Link Prediction in Directed and Weighted Social Network Structure Read chapter Read first chapter

2018 | OriginalPaper | Chapter

StyloLIT: Stylometry and Location Indicative Terms Based Geographic Location Estimation Using Convolutional Neural Networks

Authors : K. Surendran, O. P. Harilal, P. Hrudya, Poornachandaran Prabaharan

Published in: Intelligent Systems Technologies and Applications

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Estimation of geographic location information of users from social media portals such as twitter plays a vital role in areas such as disaster management, marketing, cyber forensics etc. At the same time increasing data privacy concerns forced the social media sites to make the sharing of geographic location as the opt-in feature, also increasing user awareness about privacy prevents the users from disclosing their location details. However, most users leave footprints unknowingly that could be used to identify their approximate location information. Since it is observed that social media users from multiple locations possess diversity in their expression of language, we propose a two level approach involving stylometry and location indicative terms to address this problem. Experimental results shows that our approach outperforms the current state of the art in predicting the geographical location of twitter users purely based on their text content.

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 Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks
next chapter EMG Pattern Classification Using Neural Networks
Literature
1.
2.
3.
4.
5.
6.
7.
8.
Cheng, Z., Caverlee, J., Lee, K.: 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. ACM (2010)
9.
Chandra, S., Khan, L., Muhaya, F.B.: 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 Inernational Conference on Social Computing (SocialCom), pp. 838–843. IEEE (2011)
10.
Bo, H., Cook, P., Baldwin, T.: Geolocation prediction in social media data by finding location indicative words. In: Proceedings of COLING, pp. 1045–1062 (2012)
11.
Jurgens, D.: That’s what friends are for: inferring location in online social media platforms based on social relationships. ICWSM 13, 273–282 (2013)
12.
McGee, J., Caverlee, J., Cheng, Z.: Location prediction in social media based on tie strength. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 459–468. ACM (2013)
13.
Kong, L., Liu, Z., Huang, Y.: Spot: locating social media users based on social network context. Proc. VLDB Endow. 7(13), 1681–1684 (2014)CrossRef
14.
Compton, R., Jurgens, D., Allen, D.: Geotagging one hundred million twitter accounts with total variation minimization. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 393–401. IEEE (2014)
15.
Madden, M.: Privacy management on social media sites. In: Pew Internet Report, pp. 1–20 (2012)
16.
Molok, N.N.A., Ahmad, A., Chang, S.: Information leakage through online social networking: opening the doorway for advanced persistence threats. J. Aust. Inst. Prof. Intell. Off. 19(2), 38 (2011)
17.
18.
Grierson, G.A. (ed.): Linguistic Survey of India, vol. 4. Office of the Superintendent of Government Printing, Calcutta (1906)
19.
20.
Eisenstein, J., O’Connor, B., Smith, N.A., Xing, E.P.: A latent variable model for geographic lexical variation. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1277–1287. Association for Computational Linguistics (2010)
21.
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: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 237–246. ACM (2011)
22.
Rangel, F., Rosso, P., Koppel, M.M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN 2013. In: CLEF Conference on Multilingual and Multimodal Information Access Evaluation, pp. 352–365. CELCT (2013)
23.
24.
25.
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010)
26.
Anil, R., Manjusha, K., Kumar, S.S., Soman, K.P.: Convolutional neural networks for the recognition of Malayalam characters. In: Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, pp. 493–500. Springer (2015)
27.
Athira, S., Mohan, R., Poornachandran, P., Soman, K.P.: Automatic modulation classification using convolutional neural network. Int. J. Control Theory Appl. 9(16), 7733–7742 (2016)
28.
Miura, Y., Taniguchi, M., Taniguchi, T., Ohkuma, T.: A simple scalable neural networks based model for geolocation prediction in Twitter. WNUT 2016, 9026924, 235 (2016)
Metadata
Title
StyloLIT: Stylometry and Location Indicative Terms Based Geographic Location Estimation Using Convolutional Neural Networks
Authors
K. Surendran
O. P. Harilal
P. Hrudya
Poornachandaran Prabaharan
Copyright Year
2018
Book
Intelligent Systems Technologies and Applications

Print ISBN: 978-3-319-68384-3

Electronic ISBN: 978-3-319-68385-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-68385-0_19

Premium Partner

    Image Credits