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

The city as a construction site — a visual record of a multisensory experience

In this article, I consider the reception of images that are present in a city space. I focus on the juxtaposition of computer‑generated images covering fences surrounding construction sites and the real spaces which they screen from view. I postulate that a visual experience is dependent on input from the other human senses. While looking at objects, we are not only standing in front of them but are being influenced by them. Seeing does not leave a physical trace on the object; instead the interference is more subtle — it influences the way in which we perceive space. Following in the footsteps of Sarah Pink, Michael Taussig and William J. T. Mitchell, I show that seeing (to paraphrase the title of an article by the last of the above mentioned scholars) is a cultural practice. The last part of the article presents a visual essay as a method that can contribute to cultural urban studies. I give as an example of such a method a photo‑essay about chosen construction sites in Poznań, which I photographed between December 2014 and June 2015

Scatteract: Automated extraction of data from scatter plots

Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89% of the plots in our test set.Comment: Submitted to ECML PKDD 2017 proceedings, 16 page

Playing with Identity. Authors, Narrators, Avatars, and Players in The Stanley Parable and The Beginner’s Guide

This article offers a comparative analysis of Davey Wreden’s The Stanley Parable (Wreden 2011 / Galactic Cafe 2013) and The Beginner’s Guide (Everything Unlimited Ltd. 2015) in order to explore the interrelation of authors, narrators, avatars, and players as four salient functions in the play with identity that videogames afford. Building on theories of collective and collaborative authorship, of narratives and narrators across media, and of the avatar-player relationship, the article reconstructs the similarities and differences between the way in which The Stanley Parable and The Beginner’s Guide position their players in relation to the two games’ avatars, narrators, and (main) author, while also underscoring how both The Stanley Parable and The Beginner’s Guide use metareferential strategies to undermine any overly rigid conceptualization of these functions and their interrelation