nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Cross-Cultural Image-Based Author Profiling in Twitter

verfasst von : Ivan Feliciano-Avelino, Miguel Á. Álvarez-Carmona, Hugo Jair Escalante, Manuel Montes-y-Gómez, Luis Villaseñor-Pineda

Erschienen in: Advances in Soft Computing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Recent works have shown that it is possible to use information extracted from images to address the task of automatic gender identification. These proposals have validated their solutions using monolingual datasets, i.e., collections where images are shared by users having the same mother tongue. This paper aims to test the usefulness of images collected from users who do not share the same language. In principle, these users present cultural differences, which may be reflected in the images they share. However, a cross-cultural image-based approach would be very useful for languages where data is not available or scarce. The experiments presented demonstrate that characteristics obtained from the images, regardless of the users’ mother tongue, can be used for gender prediction. They mainly confirm the usefulness of a cross-cultural image-based approach, showing that culturally different individuals with equivalent profiles traits tend to share similar images.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Vorheriges Kapitel Solving Dynamic Combinatorial Optimization Problems Using a Probabilistic Distribution as Self-adaptive Mechanism in a Genetic Algorithm

Nächstes Kapitel Application of Fuzzy Logic in the Edge Detection of Real Pieces in Controlled Scenarios

A different scheme was also tested: each image was treated separately (without computing the average of the representations of the images), so that in order to infer the authors’ profile, a vote was made, according to the genre predicted by the classifier for each single image. However, this approach did not show good results, probably due to the small number of images per author.

Aragón, M.E., López-Monroy, A.P.: A straightforward multimodal approach for author profiling. http://ceur-ws.org/Vol-2125/paper_96

Cheng, N., Chandramouli, R., Subbalakshmi, K.: Author gender identification from text. Digit. Investig. 1, 78–88 (2011). https://doi.org/10.1016/j.diin.2011.04.002CrossRef

Ciot, M., Sonderegger, M., Ruths, D.: Gender inference of Twitter users in non-English contexts. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1136–1145 (2013)

Fatima, M., Hasan, K., Anwar, S., Nawab, R.M.A.: Multilingual author profiling on Facebook. Inf. Process. Manag. 4, 886–904 (2017). https://doi.org/10.1016/j.ipm.2017.03.005. http://www.sciencedirect.com/science/article/pii/S0306457316302424 CrossRef

Gómez-Adorno, H., Markov, I., Sidorov, G., Posadas-Durán, J.P., Sanchez-Perez, M.A., Chanona-Hernandez, L.: Improving feature representation based on a neural network for author profiling in social media texts. Comput. Intell. Neurosci. 2 (2016)

Merler, M., Cao, L., Smith, J.R.: You are what you tweet... pic! Gender prediction based on semantic analysis of social media images. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2015)

Miller, Z., Dickinson, B., Hu, W.: Gender prediction on twitter using stream algorithms with n-gram character features. Int. J. Intell. Sci. 04, 143–148 (2012). https://doi.org/10.4236/ijis.2012.224019CrossRef

Rangel, F., Rosso, P.: Use of language and author profiling: identification of gender and age. In: Natural Language Processing and Cognitive Science (2013)

Rangel, F., Rosso, P., Montes-y Gómez, M., Potthast, M., Stein, B.: Overview of the 6th author profiling task at pan 2018: multimodal gender identification in Twitter. http://ceur-ws.org/Vol-2125/

10.

Rangel, F., Rosso, P., Potthast, M., Stein, B.: Overview of the 5th author profiling task at pan 2017: gender and language variety identification in Twitter. In: Working Notes Papers of the CLEF (2017)

11.

Shigenaka, R., Tsuboshita, Y., Kato, N.: Content-aware multi-task neural networks for user gender inference based on social media images. In: 2016 IEEE International Symposium on Multimedia (ISM), pp. 169–172, December 2016. https://doi.org/10.1109/ISM.2016.0040

12.

Sierra, S., Gonzáles, F.: Combining textual and visual representations for multimodal author profiling. In: CLEF 2018 Working Notes (2018). http://ceur-ws.org/Vol-2125/paper_219.pdf

13.

Takahashi, T., Tahara, T., Nagatan, K., Miura, Y., Taniguchi, T., Ohkuma, T.: Text and image synergy with feature cross technique for gender identification. In: CLEF 2018 Working Notes (2018). http://ceur-ws.org/Vol-2125/paper_83.pdf

14.

Vijayaraghavan, P., Vosoughi, S., Roy, D.: Twitter demographic classification using deep multi-modal multi-task learning. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 478–483. Association for Computational Linguistics, Vancouver, Canada, July 2017. https://doi.org/10.18653/v1/P17-2076. https://www.aclweb.org/anthology/P17-2076

15.

Ma, X., Tsuboshita, Y., Kato, N.: Gender estimation for SNS user profiling using automatic image annotation. In: 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–6, July 2014. https://doi.org/10.1109/ICMEW.2014.6890569

16.

You, Q., Bhatia, S., Luo, J.: A picture tells a thousand words-about you! user interest profiling from user generated visual content. Sig. Process. 124, 45–53 (2016)CrossRef

17.

Zisserman, A., Simonyan, K.: Very deep convolutional networks for large-scale image recognition, April 2015. https://arxiv.org/abs/1409.1556

Titel: Cross-Cultural Image-Based Author Profiling in Twitter
verfasst von: Ivan Feliciano-Avelino
Miguel Á. Álvarez-Carmona
Hugo Jair Escalante
Manuel Montes-y-Gómez
Luis Villaseñor-Pineda
Verlag: Springer International Publishing
Buch: Advances in Soft Computing
Print ISBN: 978-3-030-33748-3

Electronic ISBN: 978-3-030-33749-0

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-33749-0_28

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"