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

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

Fashion Conversation Data on Instagram

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of {24,752} labeled images on fashion conversations, containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1

Asymmetry in Online Social Networks

Varying degrees of symmetry can exist in a social network's connections. Some early online social networks (OSNs) were predicated on symmetrical connections, such as Facebook 'friendships' where both actors in a 'friendship' have an equal and reciprocal connection. Newer platforms -- Twitter, Instagram, and Facebook's 'Pages' inclusive -- are counterexamples of this, where 'following' another actor (friend, celebrity, business) does not guarantee a reciprocal exchange from the other. This paper argues that the basic asymmetric connections in an OSN leads to emergent asymmetrical behaviour in the OSN's overall influence and connectivity, amongst others. This paper will then draw on empirical examples from popular sites (and prior network research) to illustrate how asymmetric connections can render individuals 'voiceless'. The crux of this paper is an argument from the existentialist viewpoint on how the above asymmetric network properties lead to Sartrean bad faith (Sartre, 1943). Instead of genuine interpersonal connection, one finds varying degrees of pressure to assume the Sartrean 'in-itself' (the en soi) mode-of-being, irregardless of the magnitude of 'followers' one has. Finally, this paper poses an open question: what other philosophical issues does this inherent asymmetry in modern social networking give rise to

Taste and the algorithm

Today, a consistent part of our everyday interaction with art and aesthetic artefacts occurs through digital media, and our preferences and choices are systematically tracked and analyzed by algorithms in ways that are far from transparent. Our consumption is constantly documented, and then, we are fed back through tailored information. We are therefore witnessing the emergence of a complex interrelation between our aesthetic choices, their digital elaboration, and also the production of content and the dynamics of creative processes. All are involved in a process of mutual influences, and are partially determined by the invisible guiding hand of algorithms. With regard to this topic, this paper will introduce some key issues concerning the role of algorithms in aesthetic domains, such as taste detection and formation, cultural consumption and production, and showing how aesthetics can contribute to the ongoing debate about the impact of today’s “algorithmic culture”

Capturing the Visitor Profile for a Personalized Mobile Museum Experience: an Indirect Approach

An increasing number of museums and cultural institutions around the world use personalized, mostly mobile, museum guides to enhance visitor experiences. However since a typical museum visit may last a few minutes and visitors might only visit once, the personalization processes need to be quick and efficient, ensuring the engagement of the visitor. In this paper we investigate the use of indirect profiling methods through a visitor quiz, in order to provide the visitor with specific museum content. Building on our experience of a first study aimed at the design, implementation and user testing of a short quiz version at the Acropolis Museum, a second parallel study was devised. This paper introduces this research, which collected and analyzed data from two environments: the Acropolis Museum and social media (i.e. Facebook). Key profiling issues are identified, results are presented, and guidelines towards a generalized approach for the profiling needs of cultural institutions are discussed

The Role of Country of Origin in Brand Following on Social Media Among U.S. Consumers

An understanding of how consumers interact with brands online is still in its infancy. This study will attempt to explain what motivates consumers to follow brands on social media, looking specifically at the role country and region of origin of products plays in explaining the relationship. Given the personal nature that attracts people to social media to build relationships, it is believed that the personal nature of brands originating from the social media users’ home country will heighten the likelihood that consumers track certain brands and may enhance the relationship that evolves between the brand and the consumer. A model is proposed to explain the relationship, with survey data from U.S. consumers used to begin to establish any links between product origins and brand tracking behavior through social media

Effect of Selfie, Social Network Sites Usa ge, Number of Photos Shared on Social Network Sites on Happiness among University Students: A Model Testing

This study aimed to explore the relationship between university students’ happiness levels and their daily number of selfies, daily duration of social networking sites (SNS) usage and daily number of photos shared on SNS. The study was carried out with 360 university students attending Muğla Sıtkı Koçman University in Turkey.  At the end of the statistical procedures, a model was achieved to include variables of happiness and daily number of selfies, daily duration of SNS usage and daily number of photos shared on social networks. It was understood from the model that daily number of selfies and daily duration of SNS usage predicted daily number of photos shared on social networks positively and significantly but did not predict happiness directly. In addition to this result, it is seen that daily number of selfies and daily duration of SNS usage has an indirect impact on happiness through daily number of photos shared on social networks. It can be said that the achieved Structural Equation Modelling (SEM) has good fit index values (X2=3.76, sd=2, X2/df=1.88, P=.15, RMSEA=0.050, NFI=0.97, CFI=0.99, GFI=0.99, AGFI=0.97, SRMR=0.032). Keywords: Social network sites, selfies, photo sharing, happiness, structural equation modellin