What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-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 the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

Facebook Profiles and Usage as Indicators of Personality

The online social networking website, Facebook, has greatly changed the way the world communicates. Face-to-face interactions have been replaced by wall posts, status updates and friends liking posts or leaving comments. This study looks at how certain cues on Facebook profiles relate to personality traits, specifically, extraversion, conscientiousness and emotional stability. Three hypotheses focused on profile photos and how frequently the users change their photo. I predicted that 1) extraversion scores would be higher for participants who use a party scene as their profile photo, 2) conscientiousness scores would be lower for these same participants, and 3) the emotional stability scores would be negatively related to profile photo changing frequency. A total of 170 first year college students at Bryant University were surveyed about personality traits and Facebook usage. Out of this sample, 59 users provided access to their profiles and profile picture for data coding. The first hypothesis, that extraversion and party photos are positively related, was supported. The other two were not. However, additional analyses using the self-reported behaviors from the Facebook usage survey identified several other Facebook characteristics and behaviors that could be used as an indicator for each of the three personality traits studied

Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis

One of the main findings of cognitive sciences is that automatic processes of which we are unaware shape, to a significant extent, our perception of the environment. The phenomenon applies not only to the real world, but also to multimedia data we consume every day. Whenever we look at pictures, watch a video or listen to audio recordings, our conscious attention efforts focus on the observable content, but our cognition spontaneously perceives intentions, beliefs, values, attitudes and other constructs that, while being outside of our conscious awareness, still shape our reactions and behavior. So far, multimedia technologies have neglected such a phenomenon to a large extent. This paper argues that taking into account cognitive effects is possible and it can also improve multimedia approaches. As a supporting proof-of-concept, the paper shows not only that there are visual patterns correlated with the personality traits of 300 Flickr users to a statistically significant extent, but also that the personality traits (both self-assessed and attributed by others) of those users can be inferred from the images these latter post as "favourite"

The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

What your Facebook Profile Picture Reveals about your Personality

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism

Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media

A person's weight status can have profound implications on their life, ranging from mental health, to longevity, to financial income. At the societal level, "fat shaming" and other forms of "sizeism" are a growing concern, while increasing obesity rates are linked to ever raising healthcare costs. For these reasons, researchers from a variety of backgrounds are interested in studying obesity from all angles. To obtain data, traditionally, a person would have to accurately self-report their body-mass index (BMI) or would have to see a doctor to have it measured. In this paper, we show how computer vision can be used to infer a person's BMI from social media images. We hope that our tool, which we release, helps to advance the study of social aspects related to body weight.Comment: This is a preprint of a short paper accepted at ICWSM'17. Please cite that version instea

Group influence on blogs design behaviour

Issues of national culture influence on web design behaviour have been rampant and stimulating on static web pages across the globe. The emergence of a new breed of publication-type web page brought about by the advancement of web technology however, saw a different species of online communication groups. Bloggers as these groups are called; used blogs as their communication and publication tool to distinguish themselves from other websites and online social media users. Since bloggers are groups that are recognised and credited to cultivate their own culture, the idea that national culture has an influence on blogs design behaviour and preferences may have been weakened. Bloggers groups themselves would be the influential factor that determines design preferences of bloggers in a network of blogs. To address the issue, this paper has conducted an assessment on blogs from six countries using content analysis method, national culture traits and SIDE model to ascertain design features characteristics and behaviour. Results from both the global and local blogs in each country showed that blogs design preferences in one country differ between both the global and local bloggers. Furthermore, global bloggers design preferences in countries under observation are found to be similar to one another

Familiars: representing Facebook users’ social behaviour through a reflective playful experience

In this paper, we describe the design and development of a social game called Familiars. Inspired by the daemons in Pullman’s “Dark Material” trilogy, Familiars are animal companions that sit on your Facebook profile and change into different animal forms based on your social activity within the social network of Facebook. . Familiars takes advantage of the powerful capabilities of the developers platform of Facebook to build a multi-dimensional picture of a player’s state based on social activity, facial expression analysis on photographs and suggestions from friends. This rich information is then distilled and presented to the player in the form of animal that the familiar chooses to take. We show how the types of animals and personalities were associated in a cross-cultural user study, and present quantitative results from the social behaviours of the players within the game in addition to qualitative data gathered from questionnaire responses