This chapter takes as its focal point a press photograph of the arrest of DeRay McKesson, a prominent black figure associated with the Black Lives Matter (BLM) movement in the United States. In the photograph, McKesson is shown wearing a T-shirt, produced by the social media company Twitter, that bears the hashtag #StayWoke. This photographic image is examined by deploying an ‘anatomy of an image’ approach, defined by two qualitative modes of analysis. First, looking at the use of the photograph in the mainstream online press as well as selectively on Twitter; second, by treating the image and, in particular, the T-shirt McKesson wears as a starting point for a discussion of relationships between BLM, McKesson, and Twitter.

This article presents Epistemic Spatialization as a new framework for investigating the interconnected patterns of biases when identifying objects with convolutional neural networks (convnets). It draws upon Foucault's notion of spatialized knowledge to guide its method of enquiry. We argue that decisions involved in the creation of algorithms, alongside the labeling, ordering, presentation, and commercial prioritization of objects, together create a distorted "nomination of the visible": they harden the visibility of some objects, make other objects excessively visible, and consign yet others to permanent or haphazard invisibility. Our approach differs from those who focus on high-stakes misidentifications, such as errors tied to structural racism. Examining the far more dominant series of low-stakes mistakes shows the scope of errors, destabilizing the goal of image content identification with considerable societal impact. We explore these issues by closely examining the demonstration video of a popular convnet. This examination reveals an interlocking series of biases undermining the content identification process. The picture we paint is crucial for a better understanding of the errors that result as these convnets become further embedded in everyday products. The framework is valuable for critical work on computer vision, AI studies, and large-scale visual analysis.

Purpose Despite the on going shift from text-based to image-based communication in the social web, supported by the affordances of smartphones, little is known about the new image sharing practices. Both gender and platform type seem likely to be important, but it is unclear how. The paper aims to discuss these issues. Design/methodology/approach This paper surveys an age-balanced sample of UK Facebook, Twitter, Instagram, Snapchat and WhatsApp image sharers with a range of exploratory questions about platform use, privacy, interactions, technology use and profile pictures. Findings Females shared photos more often overall and shared images more frequently on Snapchat, but males shared more images on Twitter, particularly for hobbies. Females also tended to have more privacy-related concerns but were more willing, in principle, to share pictures of their children. Females also interacted more through others’ images by liking and commenting on them. Both genders used supporting apps but in different ways: females applied filters and posted to albums whereas males retouched photos and used photo organising apps. Finally, males were more likely to be alone in their profile pictures. Practical implications Those designing visual social web communication strategies to reach out to users should consider the different ways in which platforms are used by males and females to optimise their message for their target audience. Social implications There are clear gender and platform differences in visual communication strategies. Overall, males may tend to have more informational and females more relationship-based, skills or needs. Originality/value This is the first detailed survey of electronic image sharing practices and the first to systematically compare the current generation of platforms.

Twitter is used by a substantial minority of the populations of many countries to share short messages, sometimes including images. Nevertheless, despite some research into specific images, such as selfies, and a few news stories about specific tweeted photographs, little is known about the types of images that are routinely shared. In response, this article reports a content analysis of random samples of 800 images tweeted from the UK or USA during a week at the end of 2014. Although most images were photographs, a substantial minority were hybrid or layered image forms: phone screenshots, collages, captioned pictures, and pictures of text messages. About half were primarily of one or more people, including 10% that were selfies, but a wide variety of other things were also pictured. Some of the images were for advertising or to share a joke but in most cases the purpose of the tweet seemed to be to share the minutiae of daily lives, performing the function of chat or gossip, sometimes in innovative ways.

Twitter is used by a substantial minority of the populations of many countries to share short messages, sometimes including images. Nevertheless, despite some research into specific images, such as selfies, and a few news stories about specific tweeted photographs, little is known about the types of images that are routinely shared. In response, this article reports a content analysis of random samples of 800 images tweeted from the UK or USA during a week at the end of 2014. Although most images were photographs, a substantial minority were hybrid or layered image forms: phone screenshots, collages, captioned pictures, and pictures of text messages. About half were primarily of one or more people, including 10% that were selfies, but a wide variety of other things were also pictured. Some of the images were for advertising or to share a joke but in most cases the purpose of the tweet seemed to be to share the minutiae of daily lives, performing the function of chat or gossip, sometimes in innovative ways.

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This paper looks at how data is ‘made’, by whom and how. Rather than assuming data already exists ‘out there’, waiting to simply be recovered and turned into findings, the paper examines how data is co–produced through dynamic research intersections. A particular focus is the intersections between the application programming interface (API), the researcher collecting the data as well as the tools used to process it. In light of this, this paper offers three new ways to define and think about Big Data and proposes a series of practical suggestions for making data.

For social scientists, the widespread adoption of social media presents both an opportunity and a challenge. Data that can shed light on people's habits, opinions and behaviour is available now on a scale never seen before, but this also means that it is impossible to analyse using conventional methodologies and tools. This article represents an experiment in applying a computationally assisted methodology to the analysis of a large corpus of tweets sent during the August 2011 riots in England.

This study focuses on journalists Paul Lewis (The Guardian) and Ravi Somaiya (The New York Times), the most frequently mentioned national and international journalists on Twitter during the 2011 UK summer riots. Both actively tweeted throughout the four-day riot period and this article highlights how they used Twitter as a reporting tool. It discusses a series of Twitter conventions in detail, including the use of links, the taking and sharing of images, the sharing of mainstream media content and the use of hashtags. The article offers an in-depth overview of methods for studying Twitter, reflecting critically on commonly used data collection strategies, offering possible alternatives as well as highlighting the possibilities for combining different methodological approaches. Finally, the article makes a series of suggestions for further research into the use of Twitter by professional journalists.

This study focuses on journalists Paul Lewis (The Guardian) and Ravi Somaiya (The New York Times), the most frequently mentioned national and international journalists on Twitter during the 2011 UK summer riots. Both actively tweeted throughout the four-day riot period and this article highlights how they used Twitter as a reporting tool. It discusses a series of Twitter conventions in detail, including the use of links, the taking and sharing of images, the sharing of mainstream media content and the use of hashtags. The article offers an in-depth overview of methods for studying Twitter, reflecting critically on commonly used data collection strategies, offering possible alternatives as well as highlighting the possibilities for combining different methodological approaches. Finally, the article makes a series of suggestions for further research into the use of Twitter by professional journalists.

YouTube is one of the world's most popular websites and hosts numerous amateur and professional videos. Comments on these videos might be researched to give insights into audience reactions to important issues or particular videos. Yet, little is known about YouTube discussions in general: how frequent they are, who typically participates, and the role of sentiment. This article fills this gap through an analysis of large samples of text comments on YouTube videos. The results identify patterns and give some benchmarks against which future YouTube research into individual videos can be compared. For instance, the typical YouTube comment was mildly positive, was posted by a 29-year-old male, and contained 58 characters. About 23% of comments in the complete comment sets were replies to previous comments. There was no typical density of discussion on YouTube videos in the sense of the proportion of replies to other comments: videos with both few and many replies were common. The YouTube audience engaged with each other disproportionately when making negative comments, however; positive comments elicited few replies. The biggest trigger of discussion seemed to be religion, whereas the videos attracting the least discussion were predominantly from the Music, Comedy, and How to & Style categories. This suggests different audience uses for YouTube, from passive entertainment to active debating.

This paper was published as European Journal of Communication, 2011, 26 (2), pp. 172-176. It is available from http://ejc.sagepub.com/content/26/2.toc. DOI: 10.1177/02673231110260020504