How to conduct sociolinguistic research in online public video

There has been an increase in the sharing of video, and thus speech, in social media. Yet research has focused on written language. Considering our communications are continually becoming more computer-mediated, researching of the impact of such interaction contexts upon our speech is overdue. In this thesis I ask, “how can we conduct sociolinguistic research in online public video?”. Sociolinguistics is the study of the interplay between social factors and speech. Four key aspects that construct a sociolinguistic research method are identified - i) Formulating Research Questions, ii) Ethics, iii) Selecting Linguistic Variables, and iv) Statistical Analysis - and theorised in relation to online public video research. A case study is used as a vehicle through which the research practices of these four key aspects are explored. The case study asks, “Is speech influenced by written comments in online public video?”. YouTube is rationalised as an interaction context where explicit feedback is received via viewer comments, but who is commenting is ambiguous. Hence, the sociolinguistic theory under examination is Audience Design which assumes intraspeaker variation is an automatic response to one’s audience. It is hypothesised that a YouTuber will adjust their speech as they gain information about their audience via the comments. This thesis reports on the quantitative analysis of comments and the speech variable uptalk, as well as an online ethnography that motivates the quantitative analysis of a second speech variable, word-medial trochaic /t/. The relationship between the comments and speech appears to be dependent upon the YouTuber’s career stage and their engagement with the comments. The contributions of this thesis are illustrating the value of considering speech when researching social media, and defining resources to guide sociolinguistically-aligned research in online public video

RE-AIMing Predictive Text

Our increasing reliance on mobile applications means much of our communication is mediated with the support of predictive text systems. How do these systems impact interpersonal communication and broader society? In what ways are predictive text systems harmful, to whom, and why? In this paper, we focus on predictive text systems on mobile devices (Figure 1) and attempt to answer these questions. We introduce the concept of a ‘text entry intervention’ as a way to evaluate predictive text systems through an interventional lens, and consider the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) of predictive text systems. We finish with a discussion of opportunities for NLP

Could you define that in bot terms?:Requesting, creating and using bots on Reddit

Bots are estimated to account for well over half of all web traffic, yet they remain an understudied topic in HCI. In this paper we present the findings of an analysis of 2284 submissions across three discussion groups dedicated to the request, creation and discussion of bots on Reddit. We set out to examine the qualities and functionalities of bots and the practical and social challenges surrounding their creation and use. Our findings highlight the prevalence of misunderstandings around the capabilities of bots, misalignments in discourse between novices who request and more expert members who create them, and the prevalence of requests that are deemed to be inappropriate for the Reddit community. In discussing our findings, we suggest future directions for the design and development of tools that support more carefully guided and reflective approaches to bot development for novices, and tools to support exploring the consequences of contextually-inappropriate bot ideas

Supporting Self-Care of Adolescents with Nut Allergy Through Video and Mobile Educational Tools

Anaphylaxis is a life-threatening allergic reaction which is rapid in onset. Adolescents living with anaphylaxis risk often lack the knowledge and skills required to safely manage their condition or talk to friends about it. We designed an educational intervention comprising group discussion around videos of simulated anaphylaxis scenarios and a mobile application containing video-based branching anaphylaxis narratives. We trialed the intervention with 36 nut allergic adolescents. At 1-year follow-up participants had improved adrenaline auto-injector skills and carriage, disease- and age-specific Quality of Life and confidence in anaphylaxis management. At 3-year follow-up adrenaline carriage improved further and confidence remained higher. Participants expressed how the education session was a turning point in taking control of their allergy and how the app facilitated sharing about anaphylaxis with others. We contribute insights regarding design of mobile self-care and peer-support applications for health in adolescence, and discuss strengths and limitations of video-based mobile health interventions

“Could You Define That in Bot Terms?” : Requesting, Creating and Using Bots on Reddit

Bots are estimated to account for well over half of all web traffic, yet they remain an understudied topic in HCI. In this paper we present the findings of an analysis of 2284 submissions across three discussion groups dedicated to the request, creation and discussion of bots on Reddit. We set out to examine the qualities and functionalities of bots and the practical and social challenges surrounding their creation and use. Our findings highlight the prevalence of misunderstandings around the capabilities of bots, misalignments in discourse between novices who request and more expert members who create them, and the prevalence of requests that are deemed to be inappropriate for the Reddit community. In discussing our findings, we suggest future directions for the design and development of tools that support more carefully guided and reflective approaches to bot development for novices, and tools to support exploring the consequences of contextuallyinappropriate bot ideas

OMDDAC Snapshot Report 2: Tech-driven approaches to Public Health

This Snapshot Report incorporates OMDDAC’s findings from interviews with key stakeholders, together with published research, to capture the experiences and lessons learned throughout the pandemic in relation to technology-driven approaches to public health. This Report examines three case studies: digital proximity and exposure notification; risk scoring algorithms; and Covid-status certification

OMDDAC Snapshot Report 1: Data-driven Public Policy

‘Data-driven’ decision-making has been at the heart of the response to Covid-19 in the UK. Data-driven approaches include: sharing, linkage and analysis of different datasets from various sources; predictive modelling to anticipate and understand transmission and inform policy; and data-driven profiling to identify and support vulnerable individuals. This Snapshot Report incorporates OMDDAC’s findings from interviews with stakeholders, together with published research, to capture the lessons learned throughout the pandemic across these three case studies

OMDDAC Snapshot Report 3: Policing and Public Safety

Policing during a pandemic brings novel data-driven challenges. Solving them requires significant coordination and clear communication both within forces and across public sector agencies. This report presents three case studies demonstrating the range of opportunities and difficulties facing the police in this period: police access to NHS Test and Trace data; monitoring of crime and enforcement trends; and monitoring of police resourcing and wellbeing

Data-Driven Responses to COVID-19: Lessons Learned: OMDDAC Research Compendium

Funded by the Arts and Humanities Research Council under the UKRI COVID-19 Rapid Response call, the Observatory for Monitoring Data-Driven Approaches to COVID-19 (OMDDAC) is a collaboration between Northumbria University and the Royal United Services Institute (RUSI). This project has involved a multidisciplinary team of researchers (with expertise in the law on technology, data protection, and medicine as well as practical ethics, computer science, data science, applied statistics in health, technology and security studies and behavioural science) to investigate the legal, ethical, policy and operationalchallenges encountered in relation to key data-driven responses to the pandemic.The COVID-19 pandemic has accelerated the consideration of several priorities in the data and technology space, which are reflected in the UK Government’s present strategies. The National Data Strategy, in particular, pledges to take account of the lessons learned from the COVID-19 response and draw uponthe UK’s values of transparency, accountability and inclusion. Seeking to inform the lessons learned from the pandemic, the project used a mixed-methods research design that included case study analysis, interviews with key stakeholders (individuals with relevant expertise and/or experience in relation to the data-driven pandemic response), representative public surveys, and engagement with young people through a children’s rights charity. OMDDAC has published four snapshot reports focused on data-driven public policy, tech-driven approaches to public health, policing and public safety and key findings from the public perceptions survey. The emerging issues identified in those reports align closely with the four pillars of the National Data Strategy, which form the framework for this final project report:1. Data Foundations (data quality issues and infrastructure);2. Data Skills (data literacy of decision-makers);3. Data Availability (data sharing); and4. Responsibility (law, ethics, transparency, and public trust)