Digital Traces Research Articles

Expert Errors Transformation in the Context of New Digital Objects Emergence

The emergence of digital traces as potentially new and complex objects in forensic expert science has led to the natural appearance of several errors made by experts in their research. The monitoring and analysis of judicial and expert practice made it possible to formulate the most obvious subjective and objective errors of judicial experts dictated by a number of both external and internal factors. The essence of each of the errors, the content, theoretical and practical significance of their identification and elimination for expert practice and forensic expert science in general are described.

Assessing the Interplay Between Public Attention and Government Responsiveness With Digital Trace Data: Navigating Leadership and Followership in China’s COVID-19 Vaccination Campaign

China’s digital campaign to “vaccinate all who can be vaccinated,” officially launched in December 2020, carries global implications. Grounded in the agenda-setting framework to reveal how the responses of different groups are shaped and their agendas interact, this study analyzed two years of Sina Weibo (the largest Chinese microblogging service) and Baidu (the leading search engine in China) search index data to investigate the interrelationships among different groups’ issue foci and the effects of sentiment, rationality, and moral motivation in the agenda-setting process. Large-scale computational analyses were conducted to determine the extent to which the Chinese government followed the public’s issue preferences and identify which segment of the public had a stronger ability to set the agenda. The results indicated that as the central government transitioned from leading to following the public, regional governments had a greater impact on the public agenda compared to the central government or media. The government, public, and media differed in their usage of sentiment and moral motivation on social media during the vaccination campaign, and this varied depending on the campaign’s stage. Notably, all stakeholders emphasized individual-centered values over community-centered values when addressing vaccination. The findings shed light on effective strategies for social mobilization through targeted public health messaging.

Influence of the Digitalization Process on the Transformation of the Competence of a Forensic Scientist

The article considers digital footprints as objects of various kinds (types) of forensic examinations. Based on the analysis, the author reasonably concludes that the specificity of digital traces necessitates the development of basic knowledge by experts in the field of working with electronic and digital media.The thesis is substantiated that today virtually every expert should possess the knowledge, skills and abilities to work with electronic and digital media, since otherwise it is not possible to ensure the safety and immutability of the object submitted for examination. According to the results of the study, the author comes to the conclusion that the realities of modern investigative and judicial practice determine the need for experts of various kinds (types) to acquire competence to work with electronic and digital media, the implementation of which is possible both within the framework of mastering the Federal State Educational Standard for Higher Education in the specialty “Forensic Examination”, and by improving the skills of existing experts within the framework of additional professional training education.

When Science Meets Digital Branding

The science media ecosystem continues to dramatically change with the proliferation of digital media channels and platforms. While there are many different types of digital media filling this void, the use of online videos, particularly on YouTube, has become one of the main sources for information, specifically for science and environmental issues. While most researchers examine the content of online science news and information, we show that digital trace data, including hyperlinks, “likes”, and comments that are unique to participatory online media, can reveal interesting patterns about how and why people communicate and engage with content as well as other community members. Using climate change as a case study, we demonstrate how researchers can harness hyperlinks to understand who produces controversial science news and information online, and the motivations underlying how content creators use of hyperlinks for science communication and public engagement. While many existing studies in science communication have focused on understanding the political dimension of online science news and information, our paper reveals that digital branding is a crucial part of science communication on video-based platforms such as YouTube and TikTok. We find that amateur content creators and new media outlets use hyperlinks to promote their channel and other social media presences to raise revenue. While self-promotion through citing links to one’s other social media platforms is associated with more views, linking to revenue raising websites is not associated with more views. We discuss the implications of these contexts for the online science news and information ecosystem.

The influencing mechanism of scenic spot online attention and tourists’ purchase behavior: an AISAS model based investigation

IntroductionIn the era of the Internet, online digital traces have become a new way to study the online attention of scenic spots and tourists’ purchase behavior. The public’s information search on major search platforms is a series of manifestations of potential tourists’ attention and interest in scenic spots, but there are few studies on how attention, interest and information search affect potential tourists to generate real purchase behavior.MethodThis paper selects four dimensions of short video platform, travel website, search engine and social media to comprehensively measure the online attention of high-quality scenic spots in Yunnan Province, and then establishes a gray association analytic hierarchy process based on the relevant variables of the AISAS model to empirically analyze the primary and secondary factors affecting tourists’ purchase behavior.Results(1) From the perspective of the online attention of scenic spots on different platforms, the intensity of the public’s scenic spots online attention on the four types of media platforms is in the order of travel websites, search engines, short videos and social media (2) From the perspective of spatial distribution characteristics, the online attention of high-quality scenic spots in Yunnan Province is unevenly distributed, that is, there is a big difference between the attention of higher star scenic spots and their star rating and popularity, while the attention of low-star scenic spots is not much different from their star rating and popularity (3) From the perspective of spatial agglomeration characteristics, the comprehensive online attention of high-quality scenic spots in Yunnan Province presents the spatial agglomeration characteristics of “the multi-core linkage of high-density in the east and north, and sub-high-density in the south” (4) The factors influencing the purchase behavior of potential tourists are sharing experience, attracting attention, generating interest and searching information.DiscussionBy exploring the formation mechanism of high-quality scenic spots online attention in Yunnan Province and the mechanism of its spatial differentiation, this study not only enriches the logical chain of “tourism information source → potential tourists → demand driven → tourism information search → travel preference → destination selection → purchase decision → travel experience → real tourists → feelings after traveling → focus on feedback → tourism information source,” but also broadens the application scenarios and application boundaries of travel preference theory and AISAS behavior model to a certain extent.

Comparative Evaluation of Digital Cephalometric Tracing Applications on Mobile Devices and Manual Tracing.

BACKGROUND Cephalometric radiography evaluates facial skeleton development and aids in diagnosis and treatment phases (pre and post) in orthodontics. This study aimed to compare digital cephalometric tracing using a smartphone application (App), a tablet-based platform, and manual tracing in 30 orthodontic patients. MATERIAL AND METHODS Thirty orthodontic pretreatment, criteria based, lateral cephalometric radiographs were analyzed/grouped for Steiner analysis parameters (5 skeletal, 3 dentals, 1 soft tissue) by 3 tracing methods [manual - group (Gp M), smartphone (Android - OS9) - Gp S, tablet (Apple - IOS13) - Gp T) after mandatory standardization/calibration. Measurements include 5 angular (SNA, SNB, ANB, SNMPA, SNOP), 3 linear U1NA, L1NB, U1L1, and 1 soft tissue (S line) (millimeters and degrees). Inter-examiner rating was determined using Dahlberg's test. After normality distribution testing (Shapiro-Wilk), data were analyzed using one-way analysis of variance (ANOVA) for group differences. Homogeneity of variance was verified using the Levene test. Differences were determined on probability value of (p≤0.05). RESULTS The results showed that Steiner's analysis parameters were similar in all groups with homogenous variances. Highest differences in mean values were found for L1NB, U1L1, and S line measurement, with higher values being observed in Gp S tracings. However, these differences were not statistically significant (p≤0.05). All parameters, irrespective of being measured in either degrees or millimeters, had means comparable to each other. CONCLUSIONS Smartphone and tablet-based applications produced tracings that were comparable and reliable when compared to conventional manual tracings. Standardization of images, processing, printing, and calibration of devices is important to achieve good results.

Academic urban legend, Agbogbloshie: Sweeping away the "World's Largest E-Waste Dumpsite"

Agbogbloshie, once an internationally recognised toxic e-waste site, was thrust into prominence by sensationalised media and literature. Despite various e-waste initiatives by the government and development partners, the municipality unexpectedly demolished the site in 2021. This action can be attributed partly to the municipality's inability to effectively manage informality, and partly to the propagation of questionable ‘facts’ based on inadequate data, journalistic views, and distortions, which fueled an urban myth and intensified extreme perceptions about Agbogbloshie and its policy trajectory. The emergence and downfall of this academic urban legend are demonstrated through an examination of Wikipedia activity related to the Agbogbloshie page, Google citations, other digital traces, and publication patterns. The Agbogbloshie case highlights the risks of globalizing extreme urban cases, underscores the need for more consistent and aligned urban policies and actions in Accra that are not only consistent and aligned but also cognizant of the complexities of informality, viewing it as a manifestation of underlying socio-economic challenges. It also calls for strategies that avoid displacement and fragmentation, recognising the potential hazards of relocating informal challenges to other urban and peri-urban areas following slum demolitions.

More than news! Mapping the deliberative potential of a political online ecosystem with digital trace data

Does the internet facilitate everyday public deliberation? Previous research on this question has largely focused on specific aspects, such as online news media diets or political discussions on social media. However, increasingly complex media environments are composed of different arenas with different respective potential for democracy. While previous work extensively dealt with the quality of political discussion online, it is a necessary but overlooked step, to consider the upstream features of digital infrastructure and usage. Using digital trace data from Germany, this study maps out which websites are relevant for online public discourse, introduces a measure of deliberative potential along six dimensions (information, communication, participation, connectivity, inclusivity and heterogeneity), and explores different types of websites alongside high level usage patterns. Besides a class of mainstream informational hubs, a class of quality information providers that includes most established public broadcasting sites was found. A third class of niche online forums hosts political discussions among more tightly-knit online communities, supporting previous findings of incidental exposure to political content online. While the mainstream information hubs in the sample attract a much larger volume of clicks, users spend relatively more time consuming political information on quality information sites as well as on niche online forums to engage with politics online. This project takes a more holistic perspective of the diverse ecosystem of online deliberation, while presenting a first quantitative exploration of a deliberative system.

Multimedia Data Mining and Processing for News Source Attribution

The desire for unbiased journalism that effectively counters disinformation is widely recognised. News consumers are not only interested in news, but they also want unbiased journalism that cuts through disinformation, and they want it from trusted news sources. Consequently, media researchers need to explore ways to facilitate news-source identification, irrespective of the platform used. However, the availability of multimedia data sources has seen a remarkable surge in recent years, encompassing demographic data, social media data, geodata, and pervasive digital trace data. Multimedia data mining is a procedure of discovering stimulating trends via media data using video, text, and audio that are not generally available by simple enquiries and related outputs. Researchers face the challenge of integrating these diverse sources to enhance news source attribution in multimedia data including platforms like Facebook, WhatsApp and Instagram. The paper presents a review of multimedia data approaches and their application to news source attribution research. Also, the examination of the benefits and limitations of these techniques and discussion on future directions were mentioned. Consideration was on machine learning and statistical approaches to multimedia data, which include deep learning, and probabilistic modelling. Similarly, a discussion on the importance of data privacy and ethics in news source attribution research was stated. The contribution of this study is highly relevant for news media research groups striving to improve their capability to attribute sources in multimedia data, thereby combatting disinformation and amplifying trusted media brands. Keywords: Data mining, Multimedia, Data process, News source attribution, Unbiased journalism

Digital contact tracing technology in the COVID-19 pandemic: a systematic review

Abstract Introduction The COVID-19 pandemic prompted public health teams across the world to emphasize case identification, contact tracing, and isolation in outbreak management strategies. Contact tracing was advanced by global access to mobile phones to develop and implement digital contact tracing (DCT) technology with the objective of increasing the rate of contact tracing while reducing the resources required. Purpose This study aimed to describe the DCT technology used during the COVID-19 pandemic across the world, and to identify differences and similarities between characteristics and uses. Methods This review followed the PRISMA (2020) guidelines for systematic reviews. Literature searches were conducted using Embase, MEDLINE, and PubMed and were restricted to English studies published between 2019 to 2023. Studies were excluded if they did not report findings for DCT during COVID-19, did not provide data for technology characteristics or outcomes, or were a study design listed for exclusion. Results Sixty one studies were included in the review producing results for 147 digital contact tracing technologies across 83 countries. The majority of digital contact tracing (DCT) technologies are government-owned (75.6%), primarily developed for COVID-19 tracing (96.4%). Bluetooth is the most favored technology (70%) used in their development, followed by GPS (30%) and QR codes (22%). Applications are the preferred platform (90.9%), with a few using applets (6.3%) and wearable devices (1.4%). Only 2 DCT technologies have achieved over 100 million downloads or uses (3.1%). Most DCTs fall into the 1–9.99 million downloads range (27.7%) and 10,000–99,999 downloads (20%). The majority of DCTs are voluntarily used by the population (63.6%), while 27.3% are mandated for use. Conclusions Digital contact tracing technologies were developed and implemented globally as a strategy in emergency outbreak management to reduce the spread of disease. This review describes the use of DCT across the world by identifying key features and characteristics that will serve as a lesson learned for improvement of existing DCT technologies for other emergency response outbreak management.

Developing a Model for Understanding the Factors that Affect Individuals' Intentions to Share Personal Information

With the outbreak of COVID-19, digital contact tracing (DCT) applications have been implemented to track the transmission of the highly contagious disease. However, there has been significant resistance to using these apps due to individuals' concerns about disclosing personal information. This study aimed to examine individuals' intentions to reveal their details on DCT applications. A conceptual framework was developed incorporating theories of dual calculus, Hofstede's cultural theory, information boundary theory, and individual factors. A quantitative approach was employed, using a random sampling methodology to gather data from 533 respondents. The proposed framework was validated through partial least squares path modeling. The findings revealed that COVID-19-related stress, lack of transparency, and uncertainty avoidance negatively predicted intentions to disclose personal information. Conversely, collectivism, expected community-related outcomes of sharing information, expected personal outcomes of sharing information, and information privacy concerns positively predicted intentions to disclose personal information. This research provides insights into the factors that influence the widespread acceptance of DCT applications and can assist related authorities in ensuring their successful implementation.

(In)accuracy and convergent validity of daily end-of-day and single-time self-reported estimations of smartphone use among adolescents

Understanding the measurement inaccuracy and bias introduced by self-reports of smartphone use is essential for making meaningful inferences about smartphone use and its effects. Evidence for the self-reports of smartphone use in intensive longitudinal studies is largely missing. Based on self-reported and digital trace data from 137 Czech adolescents (41% girls, Mage = 14.95 years), this study examined the accuracy, directional bias, and convergent validity of daily end-of-day and single-time reports of screen time and phone-checking behavior. Overall, the study found considerable discrepancies between self-reported smartphone use and digital trace and low between-person convergent validity for all self-reports considered for the study. Respondents usually reported shorter screen time and lower frequency of phone-checking behavior as compared to digital trace, both in daily and single-time self-reports. The within-person convergent validity between daily reports and digital tracking was low, indicating poor self-reports ability to capture the actual day-to-day fluctuations in smartphone use. This study adds to the existing evidence showing that self-reports based insights into how people use smartphones differ considerably from digital trace data and shows that both person and situational levels contribute to explaining the discrepancy between digital trace and self-report data among adolescents.

Expert Errors Made in the Study of Traditional and Digital Traces

Expert Errors Made in the Study of Traditional and Digital Traces

Assessing Data Quality in the Age of Digital Social Research: A Systematic Review

While survey data has long been the focus of quantitative social science analyses, observational and content data, although long-established, are gaining renewed attention; especially when this type of data is obtained by and for observing digital content and behavior. Today, digital technologies allow social scientists to track “everyday behavior” and to extract opinions from public discussions on online platforms. These new types of digital traces of human behavior, together with computational methods for analyzing them, have opened new avenues for analyzing, understanding, and addressing social science research questions. However, even the most innovative and extensive amounts of data are hollow if they are not of high quality. But what does data quality mean for modern social science data? To investigate this rather abstract question the present study focuses on four objectives. First, we provide researchers with a decision tree to identify appropriate data quality frameworks for a given use case. Second, we determine which data types and quality dimensions are already addressed in the existing frameworks. Third, we identify gaps with respect to different data types and data quality dimensions within the existing frameworks which need to be filled. And fourth, we provide a detailed literature overview for the intrinsic and extrinsic perspectives on data quality. By conducting a systematic literature review based on text mining methods, we identified and reviewed 58 data quality frameworks. In our decision tree, the three categories, namely, data type, the perspective it takes, and its level of granularity, help researchers to find appropriate data quality frameworks. We, furthermore, discovered gaps in the available frameworks with respect to visual and especially linked data and point out in our review that even famous frameworks might miss important aspects. The article ends with a critical discussion of the current state of the literature and potential future research avenues.

Data, death, and what comes nextThe Afterlife of Data: What Happens to Your Information When You Die and Why You Should Care Carl Öhman University of Chicago Press, 2024. 200 pp.

A scholar outlines how to respect the digital traces the dead leave behind.

A template for creating and sharing ground truth data in digital forensics.

Ground truth data (GTD) is used by those in the field of digital forensics (DF) for a variety of purposes including to evaluate the functionality of undocumented, new, or emerging technology and services and the digital traces left behind following their usage. Most accepted and reliable trace interpretations must be derived from an examination of relevant GTD, yet despite the importance of it to the DF community, there is little formal guidance available for supporting those who create it, to do so in a way that ensures any data is of good quality, reliable, and therefore usable. In an attempt to address this issue, this work proposes a minimum standard of documentation that must accompany the production of any GTD, particularly when it is intended for use in the process of discovering new knowledge, proposing original interpretations of a digital trace, or determining the functionality of any technology or service. A template structure is discussed and provided in Appendix S1 which sets out a minimum standard for metadata describing any GTD's production process and content. It is suggested that such an approach can support the maintenance of trust in any GTD and improve the shareability of it.

An evaluation of the COVID-19 self-service digital contact tracing system in New Zealand

BackgroundDigital contact tracing (DCT) aims to improve time-to-isolation (timeliness) and find more potentially exposed individuals (sensitivity) to enhance the utility of contact tracing. The aim of this study was to evaluate the public uptake of a DCT self-service survey and its integration with the Bluetooth exposure notification system within the New Zealand Covid Tracer App (NZCTA). MethodsWe adopted a retrospective cohort study design using community COVID-19 cases from February 2022 to August 2022 in New Zealand (1.2 million cases). We examined the proportion of cases completing a self-service survey and the time to complete the survey by age, sex and ethnicity. ResultsOverall, 66 % of cases completed their self-service survey. Completion was influenced by age, sex and ethnicity. The median completion time was 1.8 h (IQR 0.2, 17.2), with 95 % of those completing this survey doing so within 48 h of case identification. Around 13 % of all survey completers also uploaded their Bluetooth data, which resulted in an average of 663 cases per day notifying 4.5 contacts per case. ConclusionThe combination of high public uptake and rapid response times suggest self-service DCT could be a useful tool for future outbreaks, particularly if implemented in conjunction with manual processes and other DCT tools (e.g. Bluetooth) to address issues related to performance (sensitivity, timeliness), effectiveness, and health equity.

Determinants of willingness to donate data from social media platforms

ABSTRACT Social media data donation through data download packages (DDPs) is a promising new way of collecting individual-level digital trace data with informed consent. Nevertheless, given the novelty of this approach, little is known about whether and how people would share their data with researchers, although this could seriously affect selection bias and thus, the outer validity of the results. To study the determinants of data-sharing and help future data donation studies with detecting the conditions, under which the willingness is the highest, we pre-registered two vignette experiments and embedded them in two online surveys conducted in Hungary and the US. In hypothetical requests for donating social media data via DDPs, we manipulated the amount of the monetary incentives (1), the presence or lack of non-monetary incentives (2), the number of requested platforms (3), the estimated upload/download time (4), and the type of requested data (5). The results revealed that data-sharing attitude is strongly subject to the parameters of the actual study, how the request is framed, and some respondent characteristics. Monetary incentives increased willingness to participate in both countries, while other effects were not consistent between the two countries.

The geographies and complexities of online networks in the off-street sex market

Exploitation and human trafficking in sex markets tend to include both online and offline spaces. Understanding the scale, complexity and geography of networks is important in policing human trafficking and online escort adverts are often used to identify organised crime in this context. This article aims to make a methodological contribution to how data relating to online networks in the sex market can be collected and analysed. Through the application of web scraping, social network analysis and principal component analysis, the digital traces of 15,016 online networks operating on an adult services website were analysed in relation to their complexity and geographical patterning. The findings suggest that structural and geographical characteristics are useful for understanding the heterogeneity of online networks. Analysing networks, as opposed to assessing escort adverts, offers a more robust approach to understanding the sex market, which is more sensitive to the continuum of experiences encapsulated therein.

The quantification of everythingThe Ordinal Society Marion Fourcade and Kieran Healy Harvard University Press, 2024. 384 pp.

Digital data traces about our lives and preferences are pervasive and increasingly commodified.