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This paper examines the role of newsmaking on the digital platform YouTube during the 2021 Climate Change Summit. Drawing on data collected with the software NodeXL Pro, we assess the connected communities, authorities, and popularity within a YouTube comment network. The data set of the investigation was assembled by the query ’COP26’. Drawing on the measures of influence in-degree, out-degree and PageRank, were ranked the YouTube channels of the researched network. In addition, the layout algorithm Force Atlas 2 was employed to detect network clusters in the software program Gephi. Finally, the study drew on Natural Language Processing to analyze the comment corpus. Our key findings suggest that YouTube channels of well-established, professional news corporations, such as BBC News, DWNews, and Sky News, reached high scores for the PageRank analysis. Based on the evidence collected, we argue that professional news corporations acted as influencers during the coverage of the 2021 Climate Summit on YouTube. This investigation contributes to understanding the role of digital platforms in the science communication on climate change and the negotiations of (post)colonial pasts. Its findings imply that claims for climate justice are increasingly played out within a diverse, global transmedia ecology. Climate justice is increasingly intertwined with decolonial voices.

This chapter assesses how luxury travel imaginaries were modified in the aftermath of the outbreak of the coronavirus pandemic. Drawing on long-term fieldwork among travel influencers, the chapter presents their response strategies to the COVID-19 pandemic. Based on an analysis of evidence from participant observation in tourist sites, network visualisations, in-depth interviews and platform profiles, I trace the transformations luxury travel imaginaries have undergone since the beginning of 2020. Before this global crisis, travel influencers became new puissant players in the highly globalised tourism industry as they regularly received assignments from tourism boards and hotels. Although brand sponsorship was considered a substantial source of revenue for travel influencers, their collaborations in travel destinations and the monetisation of travel content on YouTube were further assets to secure a livelihood. The coronavirus outbreak, however, turned their life-worlds upside down. This ethnographic investigation identified three main responses of travel influencers to the current long-term crisis of tourism: (1) diversification of content creation and orientation towards other influencer genres, (2) support for local tourism organisations and online promotion of staycations and (3), finally, travel to tourist sites for circulating online content on safe travel standards. Digital platforms became a major arena where the future of tourism has been re-negotiated in the wake of the COVID-19 outbreak. The in-depth investigation suggests that travel influencers were in a position to create new powerful representations of luxury as safe travel since they acquired the skills to establish stable storyworlds for their travel experiences, which attracted the attention of large platform audiences.

Quality control refers to the continuous surveillance of a process to detect its potential anomalies and has many applications in a large variety of fields. Typical examples include the supervision of manufactured products in industries or the control of energy production over time. Different methods based on traditional control charts or machine learning approaches can be used for that purpose. Such typical monitoring problems become however much more complicated when the data are non-normally distributed, correlated along time and experience a wide range of deviations that vary in shapes and sizes. In this context, this thesis aims at exploring non-parametric methods to monitor panels of time-series. The proposed control schemes are tailored to cope with the various autocorrelation structures of the data as well as potential missing values and low signal-to-noise ratios. Although general, those methods are applied here to one main particular example related to the sunspot counts, which are at the basis of the International Sunspot Number (ISN), the world reference for modelling long-term solar activity. This index is one of the most intensively used time-series of astrophysics and appears in a large variety of models including those of the Earth climate. The proposed methods are designed to identify early any potential inconsistencies that may compromise the stability and accuracy of the series in the future. Moreover, those methods allow the identification of many deviations in past data, whose corrections will lead to a more precise reconstruction of the ISN., (SC - Sciences) -- UCL, 2021

Solar activity is a driver of long-term climate trends and must be accounted for in climate models. Unfortunately, direct measurements of this quantity over long periods do not exist. The only observation related to solar activity whose records reach back to the seventeenth century are sunspots. Surprisingly, determining the number of sunspots consistently over time remains a challenging statistical problem. It arises from the need of consolidating observations from multiple stations in a context of low signal-to-noise ratios, non-stationarity, missing data, non-standard distributions and many errors. In this paper, we propose a systematic and thorough statistical approach for monitoring these data. It consists of smoothing on multiple timescales, monitoring using block bootstrap calibrated CUSUM charts, and classifying of out-of-control situations by support vector techniques. This approach allows us to scan for a wide range of deviations many of which are observer or equipment related. Their detection and identification will help improve future observations, their elimination or correction in past data will lead to a more precise reconstruction of the International Sunspot Number, the world reference for solar activity. Our research provides a toolbox of statistical techniques of general use to monitor evolving panels of timeseries which occur in many disciplines.

Ihr Forschungsprojekt ist exemplarisch für transdisziplinäres Arbeiten am Collegium Helveticum: Die Historikerin Monika Dommann und der Künstler Hannes Rickli im Gespräch mit Barbara Bleisch über das Projekt Digitale Infrastrukturen und über transdisziplinäres Arbeiten.

A research project exemplifying the Collegium Helveticum’s transdisciplinary approach: historian Monika Dommann and artist Hannes Rickli talk to Barbara Bleisch about the project Digital Infrastructures and transdisciplinary ways of working.

Contemporary everyday cultures are increasingly pervaded by digital platforms, making a rethinking of observational research into sociocultural phenomena necessary. Observational practices that remain confined to physical localities face various impasses in the era of digital platforms. Combining participant observation in workplaces where digital platforms are accessed with technical walkthroughs of their interfaces, I make a case for renewing interface ethnography. On one hand, technical walkthroughs reveal the sociocultural assumptions embedded in interfaces and the ways in which their affordances generate symbolic meanings. However, on the other hand, participant observation foregrounds emic perspectives on digital platforms within communities of practice. Based on an ethnographic study of a Norwegian software firm, I argue that the integration of the walkthrough method with participant observation enables researchers to trace the twofold meaning construction inherent in communities of practice whose skilled labor is orientated toward digital platforms. This fusion of methods expands the scope of ethnographic knowledge across the digital-physical continuum.