Abstract
In this paper, we introduce an interesting but challenging problem: how to infer social media personality from portrait. To address this problem, we jointly consider social media content and behavior information. Specifically, first, we represent social media personality as a reflection in accordance with user behaviors in social media. Second, by means of clustering, people are divided into eight groups and labeled with different personality types. Upon regression analysis, discriminative visual attributes for personality classification are determined. Third, low-level features of selected visual attributes are trained to predict personality from given portrait. To evaluate the proposed method, we collect images of people from the internet and the behaviors of these people from their micro-blog. Comprehensive experiments demonstrate that the proposed method can achieve significant performance gain over the existing method.
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Acknowledgements
This work is supported by the National Nature Science Foundation of China (No. 61402428, No. 61202208); the Self Innovation and Achievements Transformation of Shandong Province (No. 2014CGZH0708).
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Nie, J. et al. (2016). Social Media Profiler: Inferring Your Social Media Personality from Visual Attributes in Portrait. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_63
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DOI: https://doi.org/10.1007/978-3-319-48896-7_63
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