Red Is Open-Minded, Blue Is Conscientious: Predicting User Traits From I nstagram Image Data (2020)

Abstract: Various studies have addressed the connection between a user’s traits and their social media content. This paper explores the relationship between gender, age and Big Five personality traits of 179 university students from Germany and their Instagram images. With regards to both image features and image content, significant differences between genders as well as preferences related to age and personality traits emerged. Gender, age and personality traits are predicted using machine learning classification and regression methods. This work is the first of its kind to focus on data from European Instagram users, as well as to predict age from Instagram image features and content on a fine-grained level.

## introduction the rise of machine learning methods has opened up novel possibilities to extract patterns from social media data that allow researchers to identify and predict user traits based on data features. this work presents an approach towards finding connections between a user's traits and their social media image data as well as predicting a user's traits from their data. focusing on instagram data of german university students, we analyze the connection of image features and image content with a user's gender, age and personality traits. while some studies with similar approaches will be used for comparison, some novel insights regarding the connection of image data and user traits as well as their prediction will be presented. ## related work a meta-analysis published by @xcite gives an overview of 28 studies exploring the connection between digital footprints and users' big five personality traits. the authors conclude that personality traits can be inferred with high accuracy from information shared by users on social media platforms. however, only four out of 28 studies included take image content into account and only one of the studies focuses on instagram. ferwerda et al. provided deeper insights into the area by exploring the correlation between big five personality traits and image features @xcite as well as image content @xcite and their suitability for predicting a user's personality @xcite . @xcite the connection between instagram image color characteristics @xcite as well as image features, image content and emotional expressions in images @xcite with user traits, such as personality or gender. @xcite aimed at predicting a user's gender from posting behavior and image content on pinterest and identified a number of image content categories suitable for classifying a user's gender. @xcite explored correlations between an instagram user's image content with their gender and age group, and successfully predicted these user traits from image content. however, only two large age groups were distinguished and age group as well as gender were inferred from a user's profile information and could, therefore, not be verified. @xcite successfully predicted a user's age group from user activities on instagram. like @xcite , the researchers predicted age only on a coarse-grained level and inferred age information from a user's instagram account. at this point there is, to our knowledge, no study predicting an instagram user's age on a fine-grained level. this requires collecting a user's age directly through a survey in order to obtain the exact age in years. moreover, all studies mentioned above were conducted using data from the us, south korea or randomly selected locations. hitherto there is, to our knowledge, no study specifically focusing on data from european instagram users. ## method participants were students of technische hochschule nuremberg, germany taking an online survey. the user traits assessed included a participant's age, gender and big five personality traits openness (o), conscientiousness (c), extraversion (e), agreeableness (a) and neuroticism (n). personality traits were assessed using the german version of the big five inventory 2 (bfi-2) @xcite . the data sample consisted of 16,458 images and survey data of 182 participants (100 female, 79 male, 3 non-binary (excluded)) between the ages of 18 and 36 years with a mean age of 23 years (@xmath0.29). instagram-scraper for python (rarcega, 2017) was used to download all image content from the participants' instagram profiles. saturation-related, hue-related and value-related features were extracted from every image, and average values for each user were calculated. following the approach in @xcite and @xcite , the pleasure-arousal-dominance model of @xcite ) was adopted. the author found specific combinations of brightness and saturation to represent different emotional states, which are calculated as follows: @xmath1.69 brightness + .22 saturation, @xmath2.31 brightness + .60 saturation, @xmath3.76 brightness + .32 saturation. image features used for analysis are displayed in ## discussion in regards to the user trait gender, results align well with @xcite and @xcite . women posting more warm-toned, pleasurable images and displaying a preference for fashion and beauty-related topics as well as social scenes as opposed to men whose images exude more dominance and who tend to display a preference for architecture, sports and outdoor scenes are also in line with common expectations and, therefore, emphasize the validity of results. in regards to age, the preference for social scenes in younger users could be rooted in their stronger desire for social validation and feedback @xcite in order to build social identity and develop relations @xcite . in regards to personality traits, only some findings of @xcite and @xcite could be reproduced while in some cases findings even contradict each other (e.g. a preference for red in conscientious users @xcite vs. blue in conscientious users (this study as well as @xcite , @xcite )) even though a very similar approach was adopted. reasons for the discrepancies might include the composition of the user sample in regards to location or socio-economic status as well as current posting and editing trends on social media. however, more research in the area based on larger and more diverse data samples is required to obtain a clearer picture. preferences that emerged in this work as well as other studies include a preference for art-related topics vs. social scenes in open-minded users @xcite ) @xcite as well as a preference for warmer colors and social scenes in extraverts and cooler colors in introverts @xcite . these findings are in line with attributes ascribed to these personality traits @xcite . the trait agreeableness generally shows a statistical correlation with a user's gender @xcite , with women also scoring significantly higher in our sample. in the light of this connection, both the aversion against sports-related images found in this study as well as the preference for fashion found by @xcite are in line with expectations. the same holds true for the trait neuroticism, explaining the preference for beauty products and aversion against weapons in images in highly neurotic users. regarding the prediction of gender, this study outperformed @xcite by a large margin. while @xcite did achieve better results, it has to be noted that their study predicted gender as inferred from image content, not a user's verified gender. regarding the prediction of personality traits, the prediction in this study outperformed @xcite in regards to all five personality traits. while @xcite achieved better results predicting conscientiousness, extraversion and neuroticism, this study achieved the same or better results for openness and agreeableness. ## strengths and limitations while other researchers (e.g. @xcite ) inferred a user's gender and age group from their profile information, we tested our results against survey data. our approach holds two main benefits. firstly, in order to make age groups clearly distinguishable, @xcite only included teenagers (@xmath4. this approach forced the researchers to exclude all users estimated to be in their twenties, which represents a very large group among instagram users in general as well as in the data sample for our study. secondly, it is crucial to mention that predicting a user's age from their instagram profile might not be reliable. errors can easily be introduced into the ground truth by users not disclosing their real age, by lack of precision in face recognition tools as well as image editing and the common use of filters. moreover, in our study we are looking at a user's age on a fine-grained level. this way we are accounting for the fact that particularly among teenagers and young adults, an age difference of a few years can have a large impact on preferences, habits and aversions which cannot be captured as precisely when working with age groups. another strength of this study is that it is, to our knowledge, the first study of its kind to focus on data from european users, opening up more possibilities for comparison of results across cultures and geographic locations. limitations include the limited age range of participants as well as the data sample mostly consisting of university students. future studies should also focus on other age groups and socio-economic groups as well as collect sufficient data from participants of non-binary gender identity. another limitation is the assignment of each image content label to only one category to avoid statistic interaction between categories. while this makes sense from a statistical point of view, many labels could be assigned to more than one category, which might yield more accurate results in regards to the occurrence of a specific category. ## outlook possible areas of application could be the non-invasive acquisition of personality data for statistics or research when conducting a survey is not feasible as well as the generation of targeted content in areas such as advertising or usability. however, ethical concerns will have to be addressed when inferring user traits from image data, particularly in areas such as health or employment. the question whether users can consent to the extraction of information from their publicly visible image data with machine learning methods largely incomprehensible to humans will have to be discussed. while some overlap between results of studies in the area is emerging, more research on larger and more diverse data samples is still required to obtain robust results on the connection between user traits and social media image data. based on the results of this work and related studies, image features and content categories that hold the most and least predictive value in regards to user traits should be identified and used to improved the proposed approach. as data for this study was collected shortly before the onset of the covid-19 pandemic, future research should explore its impact on how user traits manifest in social media content.