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In this study, we theoretically conceptualize and empirically investigate translocal spatial arrangements of networked public spheres on social media. In digital communication networks, actors easily connect with others globally, crossing the borders of cities, nations and languages. However, the spatial notions evoked in public sphere research to date remain largely territorial. We propose a theoretical framework drawing on Löw's sociology of space, which highlights the relational and translocal nature of spatial arrangements. In a case study of the translocal interaction network of Berlin Twitter users, we demonstrate how this framework can be leveraged empirically using network analysis. Despite the overall network of Berlin's Twittersphere spanning the whole world, we find territorialized as well as deterritorialized translocal communities. This points to the simultaneity of territorial and networked spatial logics in digital public spheres. [ABSTRACT FROM AUTHOR]

Background Numerous prognostic scores have been published to support risk stratification for patients with coronavirus disease 2019 (COVID-19). Methods We performed a systematic review to identify the scores for confirmed or clinically assumed COVID-19 cases. An in-depth assessment and risk of bias (ROB) analysis (Prediction model Risk Of Bias ASsessment Tool [PROBAST]) was conducted for scores fulfilling predefined criteria ([I] area under the curve [AUC)] ≥ 0.75; [II] a separate validation cohort present; [III] training data from a multicenter setting [≥2 centers]; [IV] point-scale scoring system). Results Out of 1522 studies extracted from MEDLINE/Web of Science (20/02/2023), we identified 242 scores for COVID-19 outcome prognosis (mortality 109, severity 116, hospitalization 14, long-term sequelae 3). Most scores were developed using retrospective (75.2%) or single-center (57.1%) cohorts. Predictor analysis revealed the primary use of laboratory data and sociodemographic information in mortality and severity scores. Forty-nine scores were included in the in-depth analysis. The results indicated heterogeneous quality and predictor selection, with only five scores featuring low ROB. Among those, based on the number and heterogeneity of validation studies, only the 4C Mortality Score can be recommended for clinical application so far. Conclusions The application and translation of most existing COVID scores appear unreliable. Guided development and predictor selection would have improved the generalizability of the scores and may enhance pandemic preparedness in the future. [ABSTRACT FROM AUTHOR]

As a result of current digitalization of society, organizations have been increasingly investing in Artificial Intelligence (AI) technologies in recent years. AI describes the self-learning ability and capability of making humanintervention-independent decisions of digital technologies which offers immense potential in practical business application by realizing higher automation, cost reduction and increased customer satisfaction. Especially the real estate sector is heavily influenced by AI enabled preventive maintenance and failure analysis capabilities. The question of research is whether the AI diffusion within the company and the maturity of the applied AI solutions can already be measured. Furthermore, it raises the question if this extent and level is quantifiable and whether the different measurement approaches allow comparisons with other organizations and institutions. This study is therefore in the context of current research in the field of digital technologies and can be assigned to the area of information systems (IS) research. To answer the research question, the relevant scientific IS literature is examined and linked to various economic theories, such as the Resource Based View (RBV), the Dynamic Capability View (DCV) and the Technology Readiness Index (TRI), the Technology Readiness Level scale (TRL), the Upper Echelon Theory (UET) and the golden triangle. By doing so, the findings of the primary technology-intensive IS literature is combined with the findings of the organization-focused field of strategic management (SM) research enabling a holistic and multifaceted overview about the topic. As a result, it can be stated that there are already various approaches that allow a qualitative self-evaluation of the organizations' Artificial Intelligence maturity, while in parallel there is no measurement procedure available that allows holistic, comparative, and bias-free comparisons. There is therefore a lack of quantitative approaches to measure AI maturity and a lack of objective criteria to examine the AI maturity of an organization. [ABSTRACT FROM AUTHOR]

This study investigates the roles of feminist actors in the Twitter discourse about sexualized violence that came up during the confirmation of Brett Kavanaugh as a U.S. Supreme Court justice in October 2018. We theorize the discourse from the perspective of feminist counterpublics because it helps in understanding the extant power relations in public debate that are also manifest in digital networks. In this empirical study, we reconstruct the Twitter network discourse about the judge's confirmation and examine the role of feminist actors and their counterpublic characteristics. Our findings indicate that the discourse network was highly polarized, but feminist voices were well integrated within the (progressive) center. Although feminists acted in the role of challengers seeking the attention of established political actors and news media, they did not remain isolated. Instead, they positioned themselves in the center of the progressive network and therefore influenced the general public discourse. [ABSTRACT FROM AUTHOR]

Energy calibration via correlation has proven to be a powerful calibration method especially in the case of calibration data sets with low counting statistics. This work reports on an evolved version of this approach that uses an adaptive mesh refinement to sample the parameter space which results in the same calibration performance but in a considerably reduced computation time.

Artificial Intelligence poses a consortium of multiple digital technologies able to perform tasks which were thought about that they can only be done by humans. To do so, it applies complex learning and decision-making processes based on analysis of structured and unstructured data. Currently, AI is assumed to have massive benefits in the areas of efficiency and performance of companies, although the impact on financial key performance indicators (KPI) is still unexplored. The underlying thesis of this research is that the financial impact of AI can already be seen in practice. The research question is whether there is an impact of company-driven AI efforts on financial KPI, like the return on assets (ROA) and the market capitalization. To obtain the intended results, a theoretical and empirical analysis was chosen as particular approach. Firstly, the existing scientific research is examined regarding already measurable financial impacts of digital technologies. In a second step, a regression model for panel data will be applied on a dataset containing financial data of the forty biggest German companies and their respective AI effort per year as a binary variable over a time period of seven years. As a result, a financial influence of AI cannot be verified yet on a statistically significant level. Despite of this, an increasing number of AI efforts over the last years can be confirmed. [ABSTRACT FROM AUTHOR]

The purpose of this article is to identify a potential influence of a Chief Digital Officer (CDO) on company's success. To do so, the CDO position is analyzed and put in context with existing research regarding upper echelons and consequences of digital activities. The research approach is theoretical and analytical and provides an intensive literature analysis concerning the connecting topics. Firstly, general facts and characteristics of CDOs will be presented. Secondly, the monetary influence of digital activities in former studies will be examined. Thirdly, upper echelon theory and attentionbased view theory will be discussed with regard to organizational issues. In a final step, the topics mentioned are put into context with each other. The aim is to prove that the employment of a CDO might be able to influence the financial outcome of a company. As an intermediate finding it can be stated that the CDO Position has an impact on company's success. But further research is needed to clearly prove the extent of influence. [ABSTRACT FROM AUTHOR]

The goal of this paper is to evaluate two methods for the topic modeling of multilingual document collections: (1) machine translation (MT), and (2) the coding of semantic concepts using a multilingual dictionary (MD) prior to topic modeling. We empirically assess the consequences of these approaches based on both a quantitative comparison of models and a qualitative validation of each method's potentials and weaknesses. Our case study uses two text collections (of tweets and news articles) in three languages (English, Hebrew, Arabic), covering the ongoing local conflicts between Israeli authorities, settlers, and Palestinian Bedouins in the West Bank. We find that both methods produce a large share of equivalent topics, especially in the context of fairly homogenous news discourse, yet show limited but systematic differences when applied to highly heterogenous social media discourse. While the MD model delivers a more nuanced picture of conflict-related topics, it misses several more peripheral topics, especially those unrelated to the dictionary's focus, which are picked up by the MT model. Our study is a first step toward instrument validation, indicating that both methods yield valid, comparable results, while method-specific differences remain. [ABSTRACT FROM AUTHOR]

While scholarly attention has been devoted to social media's potential mobilizing function, they may also contribute to demobilization discourses: social communication actively promoting nonvoting. This paper examines discourses around mobilization vs. demobilization in the context of the municipal elections in Jerusalem. As the sweeping majority of East Jerusalem Palestinians have continuously been boycotting Jerusalem's municipal elections, this is a potent case through which to examine how demobilization functions in action, through social media conversations. Using a mixed-methods analysis of Twitter contents as structured by different languages, our findings show how mobilization and demobilization discourses can co-occur during the same election event. Users of different languages – reflecting different social and political identities – interpret the elections in contrasting ways, with tangible implications for (in)equality in political participation. The study thus contributes theoretically to several domains of political communication, including election studies, local politics, and language fragmentation in online political discourse. [ABSTRACT FROM AUTHOR]

Digital communication technologies, social web platforms, and mobile communication have fundamentally altered the way we communicate publicly. They have also changed our perception of space, thus making a re-calibration of a spatial perspective on public communication necessary. We argue that such a new perspective must consider the relational logic of public communication, which stands in stark contrast to the plain territorial notion of space common in communication research. Conceptualising the spatiality of public communication, we draw on Löw's (2016) sociology of space. Her relational concept of space encourages us to pay more attention to (a) the infrastructural basis of communication, (b) the operations of synthesising the relational communication space through discursive practices, and (c) power relations that determine the accessibility of public communication. Thus, focusing on infrastructures and discursive practices means highlighting crucial socio-material preconditions of public communication and considering the effects of the power relations which are inherent in their spatialisation upon the inclusivity of public communication. This new approach serves a dual purpose: Firstly, it works as an analytical perspective to systematically account for the spatiality of public communication. Secondly, the differentiation between infrastructural spaces and spaces of discursive practices adds explanatory value to the perspective of relational communication spaces. [ABSTRACT FROM AUTHOR]

Following calls for a spatial turn in communication studies, we investigate the reach and topography of Twitter communication in two case studies of Berlin and Jerusalem. We theorize on the spatial dimensions of social media communication and their potential to establish a public sphere that can reach from the local to the global level. Empirically, we investigate the scope of Twitter communication of local users in Berlin and Jerusalem and ask to what degree their interactions and issues indicate a local public sphere or extend beyond the local level. We use a combination of topic modeling and a novel localization index to explore the spatial dimensions of the two Twitterspheres. Our data point to a considerable share of locally rooted conversations, but the majority of communication reaches beyond the local. At the intersection of interactions and issues, we uncover complex, semilocal configurations of public communication [ABSTRACT FROM AUTHOR]

Charters granting coats-of-arms have been matters of interest for art historians due to the arms’ painted depictions included in them. However, in spite of a rather standardised formulary often based on established models, the texts are worth analysing from the diplomatic point of view. Particular contents like the blazon of the coats-of-arms granted by them exceed diplomatic routine, and the detail in different wording reflects differences in the assignation or confirmation of arms and the ennoblement. Arengae, dispositive verbs and epitheta allow for personalised versions. [ABSTRACT FROM AUTHOR]

Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of topics to be generated; (c) evaluation of the model's reliability; and (d) the process of validly interpreting the resulting topics. We review the research literature dealing with these questions and propose a methodology that approaches these challenges. Our overall goal is to make LDA topic modeling more accessible to communication researchers and to ensure compliance with disciplinary standards. Consequently, we develop a brief hands-on user guide for applying LDA topic modeling. We demonstrate the value of our approach with empirical data from an ongoing research project. [ABSTRACT FROM AUTHOR]