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Previous work has found strong links between the choice of social mediaimages and users' emotions, demographics and personality traits. In this study,we examine which attributes of profile and posted images are associated withdepression and anxiety of Twitter users. We used a sample of 28,749 Facebookusers to build a language prediction model of survey-reported depression andanxiety, and validated it on Twitter on a sample of 887 users who had takenanxiety and depression surveys. We then applied it to a different set of 4,132Twitter users to impute language-based depression and anxiety labels, andextracted interpretable features of posted and profile pictures to uncover theassociations with users' depression and anxiety, controlling for demographics.For depression, we find that profile pictures suppress positive emotions ratherthan display more negative emotions, likely because of social mediaself-presentation biases. They also tend to show the single face of the user(rather than show her in groups of friends), marking increased focus on theself, emblematic for depression. Posted images are dominated by grayscale andlow aesthetic cohesion across a variety of image features. Profile images ofanxious users are similarly marked by grayscale and low aesthetic cohesion, butless so than those of depressed users. Finally, we show that image features canbe used to predict depression and anxiety, and that multitask learning thatincludes a joint modeling of demographics improves prediction performance.Overall, we find that the image attributes that mark depression and anxietyoffer a rich lens into these conditions largely congruent with thepsychological literature, and that images on Twitter allow inferences about themental health status of users.

What Twitter Profile and Posted Images Reveal About Depression and Anxiety