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.
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Notes
- 1.
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.
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Acknowledgement
This work was partially supported by CONACYT-Mexico under grants CB-2015-01-257383, FC-2016-2410. The first author thanks for scholarship CONACyT-Mexico 924024 and the second for scholarship CONACyT-Mexico 401887.
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Feliciano-Avelino, I., Álvarez-Carmona, M.Á., Escalante, H.J., Montes-y-Gómez, M., Villaseñor-Pineda, L. (2019). Cross-Cultural Image-Based Author Profiling in Twitter. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_28
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