Incorporating users' personality traits has shown to be instrumental in many personalized retrieval and recommender systems. Analysis of users' digital traces has become an important resource for inferring personality traits. To date, the analysis of users' explicit and latent characteristics is typically restricted to a single social networking site (SNS). In this work, we propose a novel method that integrates text, image, and users' meta features from two different SNSs: Twitter and Instagram. Our preliminary results indicate that the joint analysis of users' simultaneous activities in two popular SNSs seems to lead to a consistent decrease of the prediction errors for each personality trait.
The above-mentioned efforts were made on a single social networking site, however, Skowron et al REF carried out PR experiments based on text, image, and users' meta data collected from two popular social networking sites, i.e., Twitter and Instagram, and reported that such joint analysis contributed to the decrease of the prediction error.
Fusing Social Media Cues: Personality Prediction from Twitter and Instagram
WWW
Computer Science