Abstract
Our posts on social media are a way of expressing ourselves and our inclinations. In this research, we focused on Instagram (a social media platform for sharing images) to study the relationship between the personality of users and the content of photos they post. We also predicted their personalities based on the relationship between them and their images. This study is the first work that focuses on the Moroccan population. We collected a database of 316 Instagram Moroccan users, larger than the other databases used in previous works. We asked the users to fill out a NEO PI form to extract a Big Five that we chose to express their personality. And then we downloaded the images of users and extracted from them three categories of features; visual features (9 colors and lighting), image content (objects, animals, faces, people, light sources, dark, buildings), and emotional features (anger, disgust, fear, happy, neutral, sad, surprise). A root mean square error (RMSE) was used to indicate prediction accuracy, which relates to the [1,5] score scale. We succeeded in outperforming the best results obtained in the previous works, especially in predicting extraversion and neuroticism traits. We got an RMSE value of 0.83 versus 0.90 for extraversion and 0.87 versus 0.89 for neuroticism.
Supported by LIPIM laboratory.
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El Bahy, S., Aboutabit, N., Hafidi, I. (2023). Analyzing Instagram Images to Predict Personality Traits. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_31
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