Predicting Users' Personality from Instagram Pictures

Abstract

Instagram is a popular social networking application that allows users to express themselves through the uploaded content and the different filters they can apply. In this study we look at personality prediction from Instagram picture features. We explore two different features that can be extracted from pictures: 1) visual features (e.g., hue, valence, saturation), and 2) content features (i.e., the content of the pictures). To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire and grant us access to their Instagram account through the Instagram API. We gathered 54,962 pictures of 193 Instagram users. With our results we show that visual and content features can be used to predict personality from and perform in general equally well. Combining the two however does not result in an increased predictive power. Seemingly, they are not adding more value than they already consist of independently.

Exploring the Prediction of Personality Traits from Drug Consumption Profiles

“…Out of the three tested classifiers, we found that the RBF network performs best across the classifiers that were used. This seems to be in line with findings of prior work that tested multiple classifiers as well (e.g., [4,6]). The RBF network has shown to perform well on smaller datasets that are similar in size as used in the current work.…”

“…Although the overall performance between different datasets differ, similar trends can be observed on the predictability of each personality trait. The performance of the current dataset does not reach the levels of other datasets, but similar trends are observable Table 4: Comparison of personality prediction compared to prior work on Instagram [6], Facebook [4], and Twitter [17] that are using a similar performance measurement. Root-mean-square error (RMSE) is reported (r ∈ [1,5]) to indicate prediction performance of the personality traits: (O)penness to experience, (C)onscientiousness, (E)xtraversion, (A)greeableness, (N)euroticism.…”

“…Three different classifiers, chosen on the base of previous works on personality prediction, were used and compared against the baseline: M5'rules, random forest, and radial basis function (RBF) network (see Table 3 for the results). These classifiers are commonly used and have shown to perform well in personality prediction in previous works (e.g., [4,6,17]).…”

“…We first started to train our predictive model with the M5' rules. This is a classifier that has shown to be an effective classifier in previous works on personality prediction (e.g., [4,6,17]). The M5' rules outperform the baseline model in predicting all of the personality traits.…”

“…In order to support personality-based personalization, attempts have been made to implicitly acquire personality traits to circumvent the use of extensive and intruding questionnaires (e.g., [4,6]). For example, data that becomes available through the increased interconnectedness of services (e.g., single sign-on mechanisms with social networking services) has been exploited to predict personality traits from.…”