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 about a vast spectrum of topics. To sense the whys of certain social user's demands and cultural-driven interests, however, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited since this process has been typically been text-based. Following this trend on visual-based social analysis, we present a novel methodology based on Deep Learning to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So the key contribution here is to explore whether OCEAN personality trait modeling can be addressed based on images, here called ics , appearing with certain tags with psychological insights. We found that there is a correlation between those posted images and their accompanying texts, which can be successfully modeled using deep neural networks for personality estimation. The experimental results are consistent with previous cyber-psychology results based on texts or images. In addition, classification results on some traits show that some patterns emerge in the set of images corresponding to a specific text, in essence to those representing an abstract concept. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts.
Guntuku @cite_14 improved the low level features used in previous work by changing the usual Features-to-Personality (F2P) approach to a two step approach: Features-to-Answers (F2A) + Answers-to-Personality (A2P). Instead of building a model that directly maps features extracted from an image to a personality, with this approach the features are first mapped to the answers of the questionnaire BFI-10 for personality assessment @cite_12 . Then, the answers are mapped to a personality. Besides this two-step approach, they also add new semantic features to extract from the images, like , and .
@cite_14 @cite_12
9991798 2169863498
With the increasing proliferation of data production technologies (like cameras) and consumption avenues (like social media) multimedia has become an interaction channel among users today. Images and videos are being used by the users to convey innate preferences and tastes. This has led to the possibility of using multimedia as a source for user-modeling, thereby contributing to the field of personalization, recommender systems, content generation systems and so on. This work investigates approaches for modeling personality traits (based on the Five Factor Modeling approach) of users based on a collection of images they tag as ‘favorite’ on Flickr. It presents several insights for improving the personality estimation performance by proposing better features and modeling approaches. The efficacy of the improved personality modeling approach is demonstrated by its use in an image recommendation system with promising results. Abstract To provide a measure of the Big Five for contexts in which participant time is severely limited, we abbreviated the Big Five Inventory (BFI-44) to a 10-item version, the BFI-10. To permit its use in cross-cultural research, the BFI-10 was developed simultaneously in several samples in both English and German. Results focus on the psychometric characteristics of the 2-item scales on the BFI-10, including their part-whole correlations with the BFI-44 scales, retest reliability, structural validity, convergent validity with the NEO-PI-R and its facets, and external validity using peer ratings. Overall, results indicate that the BFI-10 scales retain significant levels of reliability and validity. Thus, reducing the items of the BFI-44 to less than a fourth yielded effect sizes that were lower than those for the full BFI-44 but still sufficient for research settings with truly limited time constraints.