Perceptions of the Agency and Responsibility of the NHS COVID-19 App on Twitter: Critical Discourse Analysis.

BACKGROUND: Since September 2020, the National Health Service (NHS) COVID-19 contact-tracing app has been used to mitigate the spread of COVID-19 in the United Kingdom. Since its launch, this app has been a part of the discussion regarding the perceived social agency of decision-making algorithms. On the social media website Twitter, a plethora of views about the app have been found but only analyzed for sentiment and topic trajectories thus far, leaving the perceived social agency of the app underexplored. OBJECTIVE: We aimed to examine the discussion of social agency in social media public discourse regarding algorithm-operated decisions, particularly when the artificial intelligence agency responsible for specific information systems is not openly disclosed in an example such as the COVID-19 contact-tracing app. To do this, we analyzed the presentation of the NHS COVID-19 App on Twitter, focusing on the portrayal of social agency and the impact of its deployment on society. We also aimed to discover what the presentation of social agents communicates about the perceived responsibility of the app. METHODS: Using corpus linguistics and critical discourse analysis, underpinned by social actor representation, we used the link between grammatical and social agency and analyzed a corpus of 118,316 tweets from September 2020 to July 2021 to see whether the app was portrayed as a social actor. RESULTS: We found that active presentations of the app-seen mainly through personalization and agency metaphor-dominated the discourse. The app was presented as a social actor in 96% of the cases considered and grew in proportion to passive presentations over time. These active presentations showed the app to be a social actor in 5 main ways: informing, instructing, providing permission, disrupting, and functioning. We found a small number of occasions on which the app was presented passively through backgrounding and exclusion. CONCLUSIONS: Twitter users presented the NHS COVID-19 App as an active social actor with a clear sense of social agency. The study also revealed that Twitter users perceived the app as responsible for their welfare, particularly when it provided instructions or permission, and this perception remained consistent throughout the discourse, particularly during significant events. Overall, this study contributes to understanding how social agency is discussed in social media discourse related to algorithmic-operated decisions This research offers valuable insights into public perceptions of decision-making digital contact-tracing health care technologies and their perceptions on the web, which, even in a postpandemic world, may shed light on how the public might respond to forthcoming interventions.

Understanding Trust and Changes in Use After a Year With the NHS COVID-19 Contact Tracing App in the United Kingdom: Longitudinal Mixed Methods Study.

BACKGROUND: Digital contact tracing (DCT) apps have been implemented as a response to the COVID-19 pandemic. Research has focused on understanding acceptance and adoption of these apps, but more work is needed to understand the factors that may contribute to their sustained use. This is key to public health because DCT apps require a high uptake rate to decrease the transmission of the virus within the general population. OBJECTIVE: This study aimed to understand changes in the use of the National Health Service Test & Trace (T&T) COVID-19 DCT app and explore how public trust in the app evolved over a 1-year period. METHODS: We conducted a longitudinal mixed methods study consisting of a digital survey in December 2020 followed by another digital survey and interview in November 2021, in which responses from 9 participants were explored in detail. Thematic analysis was used to analyze the interview transcripts. This paper focuses on the thematic analysis to unpack the reasoning behind participants' answers. RESULTS: In this paper, 5 themes generated through thematic analysis are discussed: flaws in the T&T app, usefulness and functionality affecting trust in the app, low trust in the UK government, varying degrees of trust in other stakeholders, and public consciousness and compliance dropping over time. Mistrust evolved from participants experiencing sociotechnical flaws in the app and led to concerns about the app's usefulness. Similarly, mistrust in the government was linked to perceived poor pandemic handling and the creation and procurement of the app. However, more variability in trust in other stakeholders was highlighted depending on perceived competence and intentions. For example, Big Tech companies (ie, Apple and Google), large hospitality venues, and private contractors were seen as more capable, but participants mistrust their intentions, and small hospitality venues, local councils, and the National Health Service (ie, public health system) were seen as well-intentioned but there is mistrust in their ability to handle pandemic matters. Participants reported complying, or not, with T&T and pandemic guidance to different degrees but, overall, observed a drop in compliance over time. CONCLUSIONS: These findings contribute to the wider implications of changes in DCT app use over time for public health. Findings suggest that trust in the wider T&T app ecosystem could be linked to changes in the use of the app; however, further empirical and theoretical work needs to be done to generalize the results because of the small, homogeneous sample. Initial novelty effects occurred with the app, which lessened over time as public concern and media representation of the pandemic decreased and normalization occurred. Trust in the sociotechnical capabilities of the app, stakeholders involved, and salience maintenance of the T&T app in conjunction with other measures are needed for sustained use.

Public Adoption of and Trust in the NHS COVID-19 Contact Tracing App in the United Kingdom: Quantitative Online Survey Study.

BACKGROUND: Digital contact tracing is employed to monitor and manage the spread of COVID-19. However, to be effective the system must be adopted by a substantial proportion of the population. Studies of mostly hypothetical contact tracing apps show generally high acceptance, but little is known about the drivers and barriers to adoption of deployed systems. OBJECTIVE: The aim of this study was to investigate adoption of and attitudes toward the NHS (National Health Service) COVID-19 smartphone app, the digital contact tracing solution in the United Kingdom. METHODS: An online survey based on the extended Technology Acceptance Model with the added factor of trust was carried out with a representative sample of the UK population. Statistical analysis showed adoption rates, attitudes toward and trust in the app, and compliance with self-isolation advice and highlighted differences for vulnerable populations (ie, older adults aged 65 years and over and members of Black, Asian, and minority ethnic [BAME] communities). RESULTS: A total of 1001 participants took part in the study. Around half of the participants who had heard of the NHS COVID-19 mobile phone app (490/963, 50.9%; 95% CI 47.8%-54.0%) had downloaded and kept the app, but more than one-third (345/963, 35.8%; 95% CI 32.8%-38.8%) either did not intend to download it or had deleted it. Significantly more BAME respondents than White respondents had deleted the app (16/115, 13.9%; 95% CI 11.8%-16.0%, vs 65/876, 7.4%; 95% CI 5.8%-9.0%), and significantly more older adults 65 years and over than those under 65 years did not intend to download it (44/127, 34.6%; 95% CI 31.7%-37.5%, vs 220/874, 25.2%; 95% CI 22.5%-27.9%). Broadly, one of the reasons for uptake was to help the NHS and other people, especially among older adults, although significantly fewer BAME participants agreed that they did so to help the NHS. Reported compliance with received notifications to self-isolate was high but was significantly lower than reported intended compliance without received notifications. Only one-fifth (136/699, 19.5%; 95% CI 17.0%-22.0%) of participants understood that the decision to send self-isolation notifications was automated by the app. There were a range of significantly more negative views among BAME participants, including lower trust in the NHS, while older adults were often significantly more positive. Respondents without the app reported significantly lower trust and more negative views toward the app and were less likely to report that they understood how the app works. CONCLUSIONS: While compliance on the part of the approximately 50% of participants who had the app was fairly high, there were issues surrounding trust and understanding that hindered adoption and, therefore, the effectiveness of digital contact tracing, particularly among BAME communities. This study highlights that more needs to be done to improve adoption among groups who are more vulnerable to the effects of the virus in order to enhance uptake and acceptance of contact tracing apps.

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods.

: Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.

"The algorithm will screw you": Blame, social actors and the 2020 A Level results algorithm on Twitter.

In August 2020, the UK government and regulation body Ofqual replaced school examinations with automatically computed A Level grades in England and Wales. This algorithm factored in school attainment in each subject over the previous three years. Government officials initially stated that the algorithm was used to combat grade inflation. After public outcry, teacher assessment grades used instead. Views concerning who was to blame for this scandal were expressed on the social media website Twitter. While previous work used NLP-based opinion mining computational linguistic tools to analyse this discourse, shortcomings included accuracy issues, difficulties in interpretation and limited conclusions on who authors blamed. Thus, we chose to complement this research by analysing 18,239 tweets relating to the A Level algorithm using Corpus Linguistics (CL) and Critical Discourse Analysis (CDA), underpinned by social actor representation. We examined how blame was attributed to different entities who were presented as social actors or having social agency. Through analysing transitivity in this discourse, we found the algorithm itself, the UK government and Ofqual were all implicated as potentially responsible as social actors through active agency, agency metaphor possession and instances of passive constructions. According to our results, students were found to have limited blame through the same analysis. We discuss how this builds upon existing research where the algorithm is implicated and how such a wide range of constructions obscure blame. Methodologically, we demonstrated that CL and CDA complement existing NLP-based computational linguistic tools in researching the 2020 A Level algorithm; however, there is further scope for how these approaches can be used in an iterative manner.

Critical reflections on three popular computational linguistic approaches to examine Twitter discourses.

Although computational linguistic methods-such as topic modelling, sentiment analysis and emotion detection-can provide social media researchers with insights into online public discourses, it is not inherent as to how these methods should be used, with a lack of transparent instructions on how to apply them in a critical way. There is a growing body of work focusing on the strengths and shortcomings of these methods. Through applying best practices for using these methods within the literature, we focus on setting expectations, presenting trajectories, examining with context and critically reflecting on the diachronic Twitter discourse of two case studies: the longitudinal discourse of the NHS Covid-19 digital contact-tracing app and the snapshot discourse of the Ofqual A Level grade calculation algorithm, both related to the UK. We identified difficulties in interpretation and potential application in all three of the approaches. Other shortcomings, such the detection of negation and sarcasm, were also found. We discuss the need for further transparency of these methods for diachronic social media researchers, including the potential for combining these approaches with qualitative ones-such as corpus linguistics and critical discourse analysis-in a more formal framework.

Revisiting the Digital Plumber: Modifying the Installation Process of an Established Commercial IoT Alarm System.

The 'digital plumber' is a conceptualisation in ubicomp research that describes the work of installing and maintaining IoT devices. But an important and often understated element of commercial IoT solutions is their long-term socio-technical infrastructural nature, and therefore long-term installation and maintenance needs. This adds complexity to both the practice of digital plumbing and to the work of design that supports it. In this paper we study a commercial company producing and installing IoT alarm systems. We examine video recordings that capture how a digital plumbing representative and software development team members make changes to both the installation process and supporting technology. Our data enables us to critically reflect on concepts of infrastructuring, and uncover the ways in which the team methodically foreground hidden elements of the infrastructure to address a point of failure experienced during field trials of a new version of their product. The contributions from this paper are twofold. Firstly, our findings build on previous examples of infrastructuring in practice by demonstrating the use of notions of elemental states to support design reasoning through the continual foregrounding and assessment of tensions identified as key factors at the point of failure. Secondly, we build on current notions of digital plumbing work. We argue that additional responsibilities of 'reporting failure' and 'facilitation of change' are part of the professional digital plumbing role and that commercial teams should support these additional responsibilities through collaborative troubleshooting and design sessions alongside solid communication channels with related stakeholders within the product team.

Working with roubles and failures in conversation between humans and robots: workshop report.

This paper summarizes the structure and findings from the first Workshop on Troubles and Failures in Conversations between Humans and Robots. The workshop was organized to bring together a small, interdisciplinary group of researchers working on miscommunication from two complementary perspectives. One group of technology-oriented researchers was made up of roboticists, Human-Robot Interaction (HRI) researchers and dialogue system experts. The second group involved experts from conversation analysis, cognitive science, and linguistics. Uniting both groups of researchers is the belief that communication failures between humans and machines need to be taken seriously and that a systematic analysis of such failures may open fruitful avenues in research beyond current practices to improve such systems, including both speech-centric and multimodal interfaces. This workshop represents a starting point for this endeavour. The aim of the workshop was threefold: Firstly, to establish an interdisciplinary network of researchers that share a common interest in investigating communicative failures with a particular view towards robotic speech interfaces; secondly, to gain a partial overview of the "failure landscape" as experienced by roboticists and HRI researchers; and thirdly, to determine the potential for creating a robotic benchmark scenario for testing future speech interfaces with respect to the identified failures. The present article summarizes both the "failure landscape" surveyed during the workshop as well as the outcomes of the attempt to define a benchmark scenario.

The relationship between trust and attitudes towards the COVID-19 digital contact-tracing app in the UK.

During the COVID-19 pandemic, digital contact-tracing has been employed in many countries to monitor and manage the spread of the disease. However, to be effective such a system must be adopted by a substantial proportion of the population; therefore, public trust plays a key role. This paper examines the NHS COVID-19 smartphone app, the digital contact-tracing solution in the UK. A series of interviews were carried out prior to the app's release (n = 12) and a large scale survey examining attitudes towards the app (n = 1,001) was carried out after release. Extending previous work reporting high level attitudes towards the app, this paper shows that prevailing negative attitudes prior to release persisted, and affected the subsequent use of the app. They also show significant relationships between trust, app features, and the wider social and societal context. There is lower trust amongst non-users of the app and trust correlates to many other aspects of the app, a lack of trust could hinder adoption and effectiveness of digital contact-tracing. The design of technology requiring wide uptake, e.g., for public health, should embed considerations of the complexities of trust and the context in which the technology will be used.

Data Work: How Energy Advisors and Clients Make IoT Data Accountable.

We present fieldwork findings from the deployment of an interactive sensing system that supports the work of energy advisors who give face-to-face advice to low-income households in the UK. We focus on how the system and the data it produced are articulated in the interactions between professional energy advisors and their clients, and how they collaboratively anticipate, rehearse, and perform data work. In addition to documenting how the system was appropriated in advisory work, we elaborate the 'overhead cost' of building collaborative action into connected devices and sensing systems, and the commensurate need to support discrete workflows and accountability systems to enable the methodical incorporation of the IoT into collaborative action. We contribute an elaboration of the social, collaborative methods of data work relevant to those who seek to design and study collaborative IoT systems.

Increasing self-regulatory energy using an Internet-based training application delivered by smartphone technology.

Self-control resources can be defined in terms of "energy." Repeated attempts to override desires and impulses can result in a state of reduced self-control energy termed "ego depletion" leading to a reduced capacity to regulate future self-control behaviors effectively. Regular practice or "training" on self-control tasks may improve an individual's capacity to overcome ego depletion effectively. The current research tested the effectiveness of training using a novel Internet-based smartphone application to improve self-control and reduce ego depletion. In two experiments, participants were randomly assigned to either an experimental group, which received a daily program of self-control training using a modified Stroop-task Internet-based application delivered via smartphone to participants over a 4-week period, or a no-training control group. Participants assigned to the experimental group performed significantly better on post-training laboratory self-control tasks relative to participants in the control group. Findings support the hypothesized training effect on self-control and highlight the effectiveness of a novel Internet-based application delivered by smartphone as a practical means to administer and monitor a self-control training program. The smartphone training application has considerable advantages over other means to train self-control adopted in previous studies in that it has increased ecological validity and enables effective monitoring of compliance with the training program.