Trace Data Research Articles

The Dynamical Hypothesis in Situ: Challenges and Opportunities for a Dynamical Social Approach to Interpersonal Coordination.

Over the past three decades, Van Gelder's dynamical hypothesis has been instrumental in reconceptualizing the ways in which perception-action-cognition unfolds over time and in context. Here, I examine how the dynamical approach has enriched the theoretical understanding of social dynamics within cognitive science, with a particular focus on interpersonal coordination. I frame this review around seven principles in dynamical systems: three that are well-represented in interpersonal coordination research to date (emergent behavior, context-sensitive behavior, and attractors) and four that could be useful opportunities for future growth (hysteresis, sensitivity to initial conditions, equifinality, and reciprocal compensation). In addition to identifying specific promising lines of theoretical inquiry, I focus on the significant potential afforded by computationally intensive science-especially in naturally occurring data or trace data. Building on the foundation laid over the past three decades, I argue that looking to increasingly situated and naturalistic settings (and data) is not only necessary to realize the full commitment to the dynamical hypothesis but is also critical to building parsimonious and principled theories of social phenomena.

Tracing data flows in Norway and Austria

The increased importance of datafication in different domains of society, and health in particular, has generated much attention in STS, specifically in the Nordic context. While much of this literature tackles newly emerging forms of data governance, we focus on a historically established and mundane data practice: that of recording vaccinations in vaccine registries. We mobilise the concept of data flows to compare the link between registry practices and governance in two countries: Norway – a data intensive welfare state - and Austria, which we label ‘data hesitant’. We ask: What is the role of registries in vaccination governance? How do data practices shape and reflect relations between citizens, health providers and the state? We show that the governance of immunity is interlocked with the material and political circumstances that make data flow. The paper makes visible the benefits of doing situated comparisons for better understandings of data practices across countries.

Integrating Communication Science and Computational Methods to Study Content-Based Social Media Effects

ABSTRACT A pressing societal and scientific question is how social media use affects our cognitions, emotions, and behaviors. To answer this question, fine-grained insight into the content of individuals’ social media use is needed. It is difficult to study content-based social media effects with traditional survey methods because such methods are incapable of capturing the extreme volume and variety of social media content that is shared and received. Therefore, this special issue aims to illustrate how content-based social media effects could be examined by integrating communication sciences and computational methods. We describe a three-step method to investigate content-based media effects, which involves (a) collecting digital trace data, (b) performing automated textual and visual content analysis, and (c) conducting linkage analysis. This Special Issue zooms in on these steps and describes the strengths and weaknesses of different computational methods. We conclude with some challenges that need to be addressed in future research.

Quantifying the Systematic Bias in the Accessibility and Inaccessibility of Web Scraping Content From URL-Logged Web-Browsing Digital Trace Data

Social scientists and computer scientists are increasingly using observational digital trace data and analyzing these data post hoc to understand the content people are exposed to online. However, these content collection efforts may be systematically biased when the entirety of the data cannot be captured retroactively. We call this often unstated assumption the problematic assumption of accessibility. To examine the extent to which this assumption may be problematic, we identify 107k hard news and misinformation web pages visited by a representative panel of 1,238 American adults and record the degree to which the web pages individuals visited were accessible via successful web scrapes or inaccessible via unsuccessful scrapes. While we find that the URLs collected are largely accessible and with unrestricted content, we find there are systematic biases in which URLs are restricted, return an error, or are inaccessible. For example, conservative misinformation URLs are more likely to be inaccessible than other types of misinformation. We suggest how social scientists should capture and report digital trace and web scraping data.

Digital formation processes: A high-frequency, large-scale investigation

Large sources of digital trace data (i.e. “Big Data”) have become increasingly important in the study of material culture. However, akin to the offline material culture traditionally studied by archaeologists, digital trace data is rarely a passive reflection of human behavior – it is a complex palimpsest produced through a variety of erasure and accretion formation processes. To better understand how digital trace palimpsests are formed and how digital formation processes influence and inform our ability to interpret the offline material processes they index, we introduce a computational method – high-frequency archaeological survey – which allows us to observe digital formation processes at a high temporal resolution, as well as a large spatial scale. Using this method every hour for one month, we surveyed posts from across the United States in Craigslist's “Free Stuff” category (popularly called “Curb Alert”), a user-generated source of big digital trace data, indexing material things that have been placed on users' curbs for removal by scavengers or trash collectors. For each post, we observed its time-to-erasure and any edits that were made during the study period – finding that the posts that survive represent a biased sample of those that were posted over the course of the month, conditioned by how recently and on what day the post is posted, the material characteristics of things that are posted about, as well as regional variation. Far from only being evidence of biased end-of-month data, however, we show that further analysis of identified digital formation processes can be an important object of study in its own right – in this case, shedding new light on social scientific questions linking the exchange of “free stuff” with the process of social stratification and urban inequality in the United States. Overall, our findings suggest the importance of accounting for and explicitly analyzing digital formation processes in studies that utilize digital trace data.

Privacy and security concerns with passively collected location data for digital contact tracing among U.S. college students.

People continue to use technology in new ways, and how governments harness digital information should consider privacy and security concerns. During COVID19, numerous countries deployed digital contact tracing that collect location data from user's smartphones. However, these apps had low adoption rates and faced opposition. We launched an interdisciplinary study to evaluate smartphone location data concerns among college students in the US. Using interviews and a large survey, we find that college students have higher concerns regarding privacy, and place greater trust in local government with their location data. We discuss policy recommendations for implementing improved contact tracing efforts.

Mapping cognitive processes in video-based learning by combining trace and think-aloud data

AimsThis study maps differences in cognitive levels of processing when learning from interactive videos and how these are related to differences in learning outcomes. SampleParticipants were 37 higher education students. MethodsParticipants were randomly assigned to either the deep or surface condition in a between-subjects design. The conditions contained the same videos but had differing task demands to induce different cognitive levels of processing. Trace-data as well as cued-retrospective think aloud data of all participants was gathered. Participants filled out a multi-layered post-test measure. Data was analysed with the Bayesian framework. ResultsResults suggest that students in the deep condition spent more time on key information and processed both details and key information in a deeper way. Students in the surface condition spent more time on details and factual knowledge while also rehearsing them more. Students in the deep condition scored higher on the amount and coherence of information they recalled from the videos. ConclusionsThe use of multiple data sources and multi-layered post-test measures is a crucial step in better understanding and adequately measuring differences in cognitive processes when learning from interactive videos.

Self-Attention-Based Uplink Radio Resource Prediction in 5G Dual Connectivity

Mobile communication technology is evolving rapidly and becoming increasingly ubiquitous, thereby increasing the demand for uplink data-intensive applications (e.g., personal broadcasting and live augmented/virtual reality videos). Recently, to facilitate a cost-effective and smooth transition from 4G to 5G networks, most carriers leverage existing 4G infrastructures using a dual connectivity (DC) feature. DC increases uplink throughput and mobility robustness; however, it also causes unprecedented dynamic fluctuations in radio channels due to the coverage discrepancy between 4G and 5G networks. Thus, in this paper, we propose a self-attention-based deep learning model to predict uplink radio resources in 5G DC. We trained the proposed model on commercial 5G DC traffic data from three major carriers in South Korea and obtained an average prediction accuracy of 95.08% under various mobility and cell-load conditions. The proposed model explains the rationale for the obtained predictions by highlighting the parts of the input time-series data that are important to realize accurate prediction. We also demonstrate the usability of the proposed model using a network emulator based on real-world 5G trace data. Extensive evaluations demonstrate that the existing congestion control algorithms can achieve excellent performance when used with the proposed model.

Generating impactful situated explanations through digital trace data

Progressively, information systems (IS) researchers draw on digital trace data to capture the emergent dynamics of today’s digitalized world. Digital trace data enable researchers to generate highly context-specific insights into the features and dynamics of socio-technical phenomena. We suggest how IS researchers can use digital trace data to develop situated explanations, that is, explanations that capture the idiosyncratic features of real-world problems in order to generate impactful solutions to these problems. We outline five key principles to build situated explanations based on digital trace data. We make several suggestions on how the information system field can adjust its research and publication practices to embrace the development and dissemination of situated explanations.

Artificial Intelligence Application with Contact Tracing for Post COVID -19 Epidemic Management

INTRODUCTION: Post COVID -19 epidemics is in a critical situation which has to be properly managed with right preventive and curative measures to protect the economy and welfare of the Human beings. OBJECTIVES: Effective management of this terrific situation may be possible through the help of contact tracing and its application of AI mechanism. Here the authors as taken the available data for the testing of the significance of AI approach for contract tracing proper management of the post COVID epidemic situation. METHODS: Here contact tracing data are collected analysed interpreted and validity is tested with the help of statistical tools like egression, coefficient and Annova for the testing of the available data with its further application. R ESULTS: AI application creates more awareness, vaccination, self-testing, isolation and intake medicine CONCLUSION: Artificial Intelligence &social media plays a vital role for the creation of social awareness and proper manage of post COVID-19 epidemics.

Provenance blockchain for ensuring IT security in cloud manufacturing

Provenance blockchain is an evolving concept for protection of production, logistics, and supply chain networks from rogue Industrial Internet of Things (IIoT) devices. Such rogue IIoT devices are a recognized threat in the cloud manufacturing networks. In extreme cases, they can be used to cause industrial accidents. A brief version of provenance is about end-to-end tracking and tracing of data and the nodes involved in creating, modifying, transmitting, storing, and deleting it at specific times and locations. It provides an end-to-end verifiable and controlled computation for ensuring trustworthiness, quality, reliability, and validity of data. Provenance has existed in computing using logging software systems. This research is focused on threats to food supply chains between two countries. A scenario for protecting food supply chain from India to UAE has been modeled. This research recognized the threat of harmful food items getting mixed with flow of genuine products in a supply chain. The IIoT devices used to control the flow can be authenticated using the evolving provenance blockchain technology. With the help of recent design recommendations in the literature, a model design has been created and simulated in this research. Observations from the simulation revealed that TCP congestions and unpredictable turnaround time for assigning cryptographic keys to IIoT device sessions may have to be explored in future. A collaborative design between the two nations has been proposed. All IIoT devices not supporting cryptography will be eliminated from the cloud manufacturing and supply chain networks. Currently, this design may be used for one time registration only. Future studies may provide improved versions in which, repeated authentication and keys replacements may be implemented.

Community transmission of SARS-CoV-2 during the Delta wave in New York City

BackgroundUnderstanding community transmission of SARS-CoV-2 variants of concern (VOCs) is critical for disease control in the post pandemic era. The Delta variant (B.1.617.2) emerged in late 2020 and became the dominant VOC globally in the summer of 2021. While the epidemiological features of the Delta variant have been extensively studied, how those characteristics shaped community transmission in urban settings remains poorly understood.MethodsUsing high-resolution contact tracing data and testing records, we analyze the transmission of SARS-CoV-2 during the Delta wave within New York City (NYC) from May 2021 to October 2021. We reconstruct transmission networks at the individual level and across 177 ZIP code areas, examine network structure and spatial spread patterns, and use statistical analysis to estimate the effects of factors associated with COVID-19 spread.ResultsWe find considerable individual variations in reported contacts and secondary infections, consistent with the pre-Delta period. Compared with earlier waves, Delta-period has more frequent long-range transmission events across ZIP codes. Using socioeconomic, mobility and COVID-19 surveillance data at the ZIP code level, we find that a larger number of cumulative cases in a ZIP code area is associated with reduced within- and cross-ZIP code transmission and the number of visitors to each ZIP code is positively associated with the number of non-household infections identified through contact tracing and testing.ConclusionsThe Delta variant produced greater long-range spatial transmission across NYC ZIP code areas, likely caused by its increased transmissibility and elevated human mobility during the study period. Our findings highlight the potential role of population immunity in reducing transmission of VOCs. Quantifying variability of immunity is critical for identifying subpopulations susceptible to future VOCs. In addition, non-pharmaceutical interventions limiting human mobility likely reduced SARS-CoV-2 spread over successive pandemic waves and should be encouraged for reducing transmission of future VOCs.

PCR testing of traced contacts for SARS-CoV-2 in England, January to July 2021.

BackgroundThe NHS Test and Trace (NHSTT) programme was established in May 2020 in England to deliver SARS-CoV-2 testing and contact tracing in order to identify infected individuals and reduce COVID-19 spread. To further control transmission, people identified as contacts were asked to self-isolate for 10 days and test only if they became symptomatic. From March 2021, eligibility criteria for PCR testing expanded to include asymptomatic contacts of confirmed cases.AimTo analyse testing patterns of contacts before and after the change in testing guidance in England to assess the impact on PCR testing behaviour with respect to symptom status and contact type.MethodsTesting and contact tracing data were extracted from the national data systems and linked. Subsequently, descriptive statistical analysis was applied to identify trends in testing behaviour.ResultsBetween 1 January and 31 July 2021, over 5 million contacts were identified and reached by contact tracers; 42.3% took a PCR test around the time they were traced. Overall positivity rate was 44.3% and consistently higher in symptomatic (60-70%) than asymptomatic (around 20%, March-June) contacts. The proportion of tests taken by asymptomatic contacts increased over time, especially after the change in testing guidance. No link was observed between uptake of PCR tests and vaccination coverage. Fully vaccinated contacts showed lower positivity (23.8%) than those with one dose (37.2%) or unvaccinated (51.0%).ConclusionAlmost 1 million asymptomatic contacts were tested for SARS-CoV-2, identifying 214,056 positive cases, demonstrating the value of offering PCR testing to this group.

Network Analytics to Unveil Links of Learning Strategies, Time Management, and Academic Performance in a Flipped Classroom

Preparatory learning tasks are considered critical for student success in flipped classroom courses. However, less is known regarding which learning strategies students use and when they use those strategies in a flipped classroom course. In this study, we aimed to address this research gap. In particular, we investigated mutual connections between learning strategies and time management, and their combined effects on students’ performance in flipped classrooms. To this end, we harnessed a network analytic approach based on epistemic network analysis (ENA) to analyze student trace data collected in an undergraduate engineering course (N = 290) with a flipped classroom design. Our findings suggest that high-performing students effectively used their study time and enacted learning strategies mainly linked to formative and summative assessment tasks. The students in the low-performing group enacted less diverse learning strategies and typically focused on video watching. We discuss several implications for research and instructional practice.

Parameter estimation for contact tracing in graph-based models.

We adopt a maximum-likelihood framework to estimate parameters of a stochastic susceptible-infected-recovered (SIR) model with contact tracing on a rooted random tree. Given the number of detectees per index case, our estimator allows to determine the degree distribution of the random tree as well as the tracing probability. Since we do not discover all infectees via contact tracing, this estimation is non-trivial. To keep things simple and stable, we develop an approximation suited for realistic situations (contract tracing probability small, or the probability for the detection of index cases small). In this approximation, the only epidemiological parameter entering the estimator is R0. The estimator is tested in a simulation study and is furthermore applied to COVID-19 contact tracing data from India. The simulation study underlines the efficiency of the method. For the empirical COVID-19 data, we compare different degree distributions and perform a sensitivity analysis. We find that particularly a power-law and a negative binomial degree distribution fit the data well and that the tracing probability is rather large. The sensitivity analysis shows no strong dependency of the estimates on the reproduction number. Finally, we discuss the relevance of our findings.

Juridicity of Oil and Gas Management in Indonesia as State Control Right (HMN)

The writing intends and aims as study material to find out the constitutional basis of oil and gas natural resources which are important and strategic for the right to control the state (HMN) for the greatest prosperity and welfare of the people. Materials and data in this writing were obtained both from the 1945 Constitution, the Oil and Gas Law, and other organic regulations relating to Oil and Gas, Oil and Gas Data in the relevant Ministries. Materials and data were also obtained through reference searches or literature that has relevance to this writing, namely in the form of books and laws and regulations related to the issues raised in this writing. Based on the results of material processing and data tracing of laws and regulations, policies, and references that are relevant to this writing, it is obtained, (1) the state's right to control oil and gas as an important resource and strategy for major contributions to the state budget, national development, and livelihood people.

Accurate ECG monitoring by Gaussian feature streaming

Wearable cardiac monitors can usefully contribute to early detection of potential cardiovascular pathologies. However ECG trace data streaming over wireless links creates some significant challenges, due to the amount of data to be transmitted. We employ a signal analysis approach based on a Gaussian dictionary to model ECG traces in a compressed way. The algorithm operates on fixed-length segments, and achieves effective compression for wireless data transmission, associating just 6 bytes to each Gaussian feature. At the same time it enables accurate reconstruction of ECG traces from the reduced data set. We tested our method on a set of 46 ECG recordings taken from the Physionet MIT-BIH Arrhythmia Database, obtaining 90% data compression rates, while percent relative deviation of reconstructed traces is always below 5%.

Predicting time‐management skills from learning analytics

AbstractBackgroundTechnological innovations such as Learning Management Systems (LMS) are becoming more and more prevalent in the learning environments of students. Distilling and acting on knowledge gathered from these systems, the field known as learning analytics, allows educators to hone their craft and support students more effectively by providing timely interventions.ObjectivesWhile most learning analytics studies focus on using LMS data to predict performance, this study instead predicts students' self‐reported time‐management skills using trace data from the Canvas LMS. This is done for courses at one Dutch technical university with in total 462 students.MethodsLinear regression and multi‐level regression models are constructed using both theory and findings from previous research. The predictions made by these models are compared to previously filled in questionnaire data to validate the results.ResultsOur results show that models can be constructed and time‐management can be predicted for individual courses. Furthermore, there are several predictors that are significant in multiple models. However, these models and predictions are not immediately transferable to other courses.ConclusionsThe study therefore emphasizes the need for further research, using multiple sources of data or more theoretically grounded predictors, to investigate the extent of the portability issues with these predictive models. Despite this we were able to predict the students' self‐reported time‐management skills in multiple different courses using Learning Analytics, and managed to identify multiple consistently predictive trace data variables.

Topic modeling and clustering in the trace data-driven analysis of job demands among teachers

Psychosocial work environment characteristics like job demands have traditionally been studied using survey data. We propose an alternative approach utilizing work related trace data collected from the information systems that employees use to achieve organizational goals. We analyze the job demands of teachers from two universities of applied sciences using trace data collected from the educational online platform Moodle over a period of 90 weeks. The data contain pairs of targets and actions (like message_sent) performed by teachers on Moodle. The timestamps of the target-action pairs allow us to study the dynamic nature of job demands, which is not possible by using periodically collected survey data. We show how trace data can be used to analyze processes related to job demands using data-driven approaches. We have identified topics, themes, temporal processes, and employee clusters from Moodle data representing the work tasks of teachers. The information obtained is action-oriented, context-specific, and dynamic, meeting the current needs for information about changing working life. The approach we have provided could be widely utilized in organizations as well as in research on occupational wellbeing. It is useful in identifying targets for intervention and it could be expanded to include prediction models on different outcomes.

Key performance indicators of COVID-19 contact tracing in Belgium from September 2020 to December 2021.

The goal of tracing, testing, and quarantining contacts of infected individuals is to contain the spread of infectious diseases, a strategy widely used during the COVID-19 pandemic. However, limited research exists on the effectiveness of contact tracing, especially with regard to key performance indicators (KPIs), such as the proportion of cases arising from previously identified contacts. In our study, we analyzed contact tracing data from Belgium collected between September 2020 and December 2021 to assess the impact of contact tracing on SARS-CoV-2 transmission and understand its characteristics. Among confirmed cases involved in contact tracing in the Flemish and Brussels-Capital regions, 19.1% were previously identified as close contacts and were aware of prior exposure. These cases, referred to as 'known' to contact tracing operators, reported on average fewer close contacts compared to newly identified individuals (0.80 versus 1.05), resulting in fewer secondary cases (0.23 versus 0.28). Additionally, we calculated the secondary attack rate, representing infections per contact, which was on average lower for the 'known' cases (0.22 versus 0.25) between December 2020 and August 2021. These findings indicate the effectiveness of contact tracing in Belgium in reducing SARS-CoV-2 transmission. Although we were unable to quantify the exact number of prevented cases, our findings emphasize the importance of contact tracing as a public health measure. In addition, contact tracing data provide indications of potential shifts in transmission patterns among different age groups associated with emerging variants of concern and increasing vaccination rates.