Instagram is a popular social networking application, which allows photo-sharing and applying different photo filters to adjust the appearance of a picture. By applying these filters, users are able to create a style that they want to express to their audience. In this study we tried to infer personality traits from the way users manipulate the appearance of their pictures by applying filters to them. To investigate this relationship, we studied the relationship between picture features and personality traits. 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. Among 113 participants and 22,398 extracted Instagram pictures, we found distinct picture features (e.g., relevant to hue, brightness, saturation) that are related to personality traits. Our findings suggest a relationship between personality traits and these picture features. Based on our findings, we also show that personality traits can be accurately predicted. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.

Music streaming services increasingly incorporate additional music taxonomies (i.e., mood, activity, and genre) to provide users different ways to browse through music collections. However, these additional taxonomies can distract the user from reaching their music goal, and influence choice satisfaction. We conducted an online user study with an application called "Tune-A-Find," where we measured participants' music taxonomy choice (mood, activity, and genre). Among 297 participants, we found that the chosen taxonomy is related to personality traits. We found that openness to experience increased the choice for browsing music by mood, while conscientiousness increased the choice for browsing music by activity. In addition, those high in neuroticism were most likely to browse for music by activity or genre. Our findings can support music streaming services to further personalize user interfaces. By knowing the user's personality, the user interface can adapt to the user's preferred way of music browsing.

Instagram is a popular social networking application, which allows photo-sharing and applying different photo filters to adjust the appearance of a picture. By applying photo filters, users are able to create a style that they want to express to their audience. In this study we tried to infer personality traits from the way users take pictures and apply filters to them. To investigate this relationship, 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. Among 113 participants and 22,398 extracted Instagram pictures, we found distinct picture features (e.g., hue, brightness, saturation) that are related to personality traits. Our findings suggest a relationship between personality traits and the way users want to make their pictures look. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.

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.

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