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Social media, as a major platform for communication and information exchange,is a rich repository of the opinions and sentiments of 2.3 billion users abouta vast spectrum of topics. To sense the whys of certain social user's demandsand cultural-driven interests, however, the knowledge embedded in the 1.8billion pictures which are uploaded daily in public profiles has just startedto be exploited since this process has been typically been text-based.Following this trend on visual-based social analysis, we present a novelmethodology based on Deep Learning to build a combined image-and-text basedpersonality trait model, trained with images posted together with words foundhighly correlated to specific personality traits. So the key contribution hereis to explore whether OCEAN personality trait modeling can be addressed basedon images, here called \emph{Mind{P}ics}, appearing with certain tags withpsychological insights. We found that there is a correlation between thoseposted images and their accompanying texts, which can be successfully modeledusing deep neural networks for personality estimation. The experimental resultsare consistent with previous cyber-psychology results based on texts or images.In addition, classification results on some traits show that some patternsemerge in the set of images corresponding to a specific text, in essence tothose representing an abstract concept. These results open new avenues ofresearch for further refining the proposed personality model under thesupervision of psychology experts.

Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks