As governments and healthcare systems grow increasingly concerned with the current obesity 'epidemic', sociological interest in the condition has also increased. Despite the emergence of work discussing obesity as a social phenomenon, the sociological dimensions of medical weight-loss treatments for obesity remain underexplored. This paper reports on a conversation analytic (CA) study and describes how moral issues surrounding weight and patienthood become visible when doctors and patients discuss obesity. Consultations in two UK National Health Service clinics were video-recorded and analysed to identify recurring patterns of interaction. This paper describes how patients answer opening questions: questions which begin the consultation, enabling patients to report their medical status. Analysis reveals that when producing their answers, patients typically imply either 'success' or 'lack of success' in their weight-loss progress. Whilst doing so, they construct their personal agency in different ways, crediting themselves for implied successes and resisting responsibility for lack of success. Through interaction the doctor and patient collaboratively construct obesity as a moral issue. The moral obligations invoked share similarities with certain perceived normative dynamics surrounding obesity and the responsibilities of patienthood. These findings have relevance to healthcare practice and add to sociological understanding of the modern obesity 'crisis'.

Background Clinical guidelines exhort clinicians to encourage patients to improve their health behaviours. However, most offer little support on how to have these conversations in practice. Clinicians fear that health behaviour change talk will create interactional difficulties and discomfort for both clinician and patient. This review aims to identify how healthcare professionals can best communicate with patients about health behaviour change (HBC). Methods We included studies which used conversation analysis or discourse analysis to study recorded interactions between healthcare professionals and patients. We followed an aggregative thematic synthesis approach. This involved line-by-line coding of the results and discussion sections of included studies, and the inductive development and hierarchical grouping of descriptive themes. Top-level themes were organised to reflect their conversational positioning. Results Of the 17,562 studies identified through systematic searching, ten papers were included. Analysis resulted in 10 top-level descriptive themes grouped into three domains: initiating; carrying out; and closing health behaviour change talk. Of three methods of initiation, two facilitated further discussion, and one was associated with outright resistance. Of two methods of conducting behaviour change talk, one was associated with only minimal patient responses. One way of closing was identified, and patients did not seem to respond to this positively. Results demonstrated a series of specific conversational practices which clinicians use when talking about HBC, and how patients respond to these. Our results largely complemented clinical guidelines, providing further detail on how they can best be delivered in practice. However, one recommended practice - linking a patient’s health concerns and their health behaviours - was shown to receive variable responses and to often generate resistance displays. Conclusions Health behaviour change talk is smoothly initiated, conducted, and terminated by clinicians and this rarely causes interactional difficulty. However, initiating conversations by linking a person’s current health concern with their health behaviour can lead to resistance to advice, while other strategies such as capitalising on patient initiated discussions, or collaborating through question-answer sequences, may be well received. Electronic supplementary material The online version of this article (10.1186/s12875-019-0992-x) contains supplementary material, which is available to authorized users.

Empirical research involving the analysis of Internet-based data raises a number of ethical challenges. One instance of this is the analysis of Twitter data, in particular when specific tweets are reproduced for the purposes of dissemination. Although Twitter is an open platform it is possible to question whether this provides a sufficient ethical justification to collect, analyse and reproduce tweets for the purposes of research or whether it is necessary to also undertake specific informed consent procedures. This paper reports on an ethics consultation that formed part of a wider research study and that aimed to identify best practice procedures for the publication of Twitter data in research findings. We focus largely on the UK context and draw on the outcomes of the consultation to highlight the range and depth of ethical issues that arise in this area. We can see Twitter as a case study for a wide number of data sources used in Web Science. This is a highly complex landscape in which questions crystallise around fundamental principles such as informed consent, anonymisation and the minimisation of harm. Furthermore, tensions exist between commercial, regulatory and academic practices, and there are also circumstances in which good ethical practice might compromise academic integrity. There is an absence of consensus in Web science and related fields over how to resolve these issues and we argue that constructive debate is necessary in order to take a proactive approach towards good practice.

The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

The increasing popularity of social media platforms creates new digital social networks in which individuals can interact and share information, news, and opinion with unprecedented speed and ease. Consequently, the use of such technologies appears to have the capacity to transform current social configurations and relations-not least within the public and civic spheres. In this article, we develop the notion of transformation in relation to the particular affordances and characteristics of micro-interaction within social media environments. Social media are emerging as a new research topic across fields, including social science, web science, computer science, and psychology. Within the social sciences, much emphasis has been placed on conceptualizing social media's role in modern society (

During the course of this article, we explore ethnomethodological principles in relation to approaching social media as data. More specifically, we consider the extent to which the work of Harvey Sacks and his rich intellectual legacy might inform this nascent field of empirical inquiry. This exploration is realised in the context of interdisciplinary research at the interface of social and computational science. Drawing from an extensive range of empirical projects into social media we reflect on the efficacy and limitations of these principles (Sacks, 1992) for understanding social media interaction as open data and practical action in the digital age.

Turning principles into practice is one of the most pressing challenges of artificial intelligence (AI) governance. In this article, we reflect on a novel governance initiative by one of the world's largest AI conferences. In 2020, the Conference on Neural Information Processing Systems (NeurIPS) introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research. Drawing insights from similar governance initiatives, including institutional review boards (IRBs) and impact requirements for funding applications, we investigate the risks, challenges and potential benefits of such an initiative. Among the challenges, we list a lack of recognised best practice and procedural transparency, researcher opportunity costs, institutional and social pressures, cognitive biases, and the inherently difficult nature of the task. The potential benefits, on the other hand, include improved anticipation and identification of impacts, better communication with policy and governance experts, and a general strengthening of the norms around responsible research. To maximise the chance of success, we recommend measures to increase transparency, improve guidance, create incentives to engage earnestly with the process, and facilitate public deliberation on the requirement's merits and future. Perhaps the most important contribution from this analysis are the insights we can gain regarding effective community-based governance and the role and responsibility of the AI research community more broadly.

The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environments and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs' behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have 'seen', done, and might do in environments in which they operate.In this paper, we provide a comprehensive survey of the existing body of work around explainable autonomous driving. First, we open with a motivation for explanations by highlighting and emphasising the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their explanation requirements for AV. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, control, and system management). Finally, we identify pertinent challenges and provide recommendations, such as a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explainability in AVs.