Simulation gesellschaftlicher Medienwirkungsprozesse am Beispiel der Schweigespirale

Der Beitrag stellt mit der agentenbasierten Modellierung (ABM) eine Methode zur Diskussion, mit der sich dynamische Medienwirkungsprozesse auf mehreren Ebenen modellieren und simulieren lassen. Dazu wird das Mikro-Makro-Problem in der Medienwirkungsforschung genauer erläutert und aus Sicht der Komplexitätstheorie interpretiert. Die Methode der Computersimulation sozialer Prozesse, speziell mit-tels ABM, wird erläutert. Schließlich wird die ABM am Beispiel der Schweigespira-le vorgestellt, um ihre Eignung für die Untersuchung dynamischer, gesellschaftlicher Medienwirkungsprozesse zu demonstrieren. Hierzu werden die Annahmen der Schweigespirale nach Noelle- Neumann in einem Computermodell formalisiert und in ihrer Dynamik simuliert. Nach der Darstellung zentraler Simulationsergebnisse werden abschließend Chancen und Grenzen der Simulationsmethode für die Medi-enwirkungsforschung diskutiert

How News Audiences Allocate Trust in the Digital Age: A Figuration Perspective

The article enriches the understanding of trust in news at a time when mass and interpersonal communication have merged in the digital sphere. We propose disentangling individual-level patterns of trust allocation (i.e., trust figurations) across journalistic media, social media, and peers to reflect the multiplicity among modern news audiences. A latent class analysis of a representative survey among German young adults revealed four figurations: traditionalists, indifferentials, optimists, and cynics. Political characteristics and education corresponded with substantial heterogeneity in individuals’ trust in news sources, their inclination to differentiate between sources, and the ways of integrating trust in journalistic and non-journalistic sources

Goodbye, gender stereotypes?: trait attributions to politicians in 11 years of news coverage

In this study, we examine gender differences in political news coverage to determine whether the media employ stereotypical traits in portrayals of 1,095 U.S. politicians. Using a sample of over 5 million U.S. news stories published from 2010 to 2020, we study the media’s attribution of gender-linked (feminine, masculine) and political (leadership, competence, integrity, empathy) traits to U.S. politicians and present new longitudinal evidence for political gender stereotyping in the news. Our findings show that certain gender differences are present in news coverage (e.g., physical traits), some of which have remained unchanged over the past decade (e.g., integrity traits)

Social Media Literacy Among Adolescents and Young Adults: Results From a Cross-Country Validation Study

When being online, young users are often confronted with insulting, hateful, or misleading messages. To handle these dark forms of participation, it is essential to equip them with resources that support their social literacy in today’s complex online environments. In the present article, we deployed a previously established scale on self-perceived participatory-moral literacy and conducted a broad online survey study with 1,489 adolescents and young adults aged 16–22 years (M = 19.74; SD = 1.65; 51% female) across eight different European countries (Austria, France, Germany, Hungary, Italy, Poland, Slovakia, and the United Kingdom). The results provided a configural identical model of participatory-moral abilities, motivation, and behavior across the considered European countries. We could confirm weak invariance, satisfactory psychometric qualities, and convergent validity of the scale across the different countries. Implications for digital literacy research are discussed

Search Engine Use for Health-Related Purposes: Behavioral Data on Online Health Information-Seeking in Germany

Internet searches for health-related purposes are common, with search engines like Google being the most popular starting point. However, results on the popularity of health information-seeking behaviors are based on self-report data, often criticized for suffering from incomplete recall, overreporting, and low reliability. Therefore, the current study builds on user-centric tracking of Internet use to reveal how individuals actually behave online. We conducted a secondary analysis of passively recorded Internet use logs to examine the prevalence of health-related search engine use, the types of health information searched for, and the sources visited after the searches. The analysis revealed two key findings. 1) We largely support earlier survey-based findings on the prevalence of online health information seeking with search engines and the relatively minor differences in information-seeking behaviors between socio-demographic groups. 2) We provide a more granular picture of the process of HISB using search engines by identifying different selection patterns depending on the scope of the searches.In dieser Studie wird auf Grundlage verknüpfter Web Tracking- und Befragungsdaten untersucht, wie sich die Menschen in Deutschland im Internet über Gesundheitsfragen informieren. Gegenüber bisherigen Studien auf Basis von Selbstauskünften in Befragungen eröffnen die Ergebnisse ein umfassenderes und differenziertes Bild des Informationsverhaltens im Internet. Die Ergebnisse zeigen, dass es sich bei Suche nach Gesundheitsinformationen im Internet um ein verbreitetes Phänomen handelt, das über verschiedene Altersgruppen und Bevölkerungsschichten hinweg gängig ist. Dabei werden Suchmaschinen wie Google auf ganz unterschiedliche Weise für die Suche nach gesundheitsbezogenen Informationen genutzt. Neben der gezielten Suche nach spezifischen Informationen zu den Symptomen, Ursachen und Behandlungsmöglichkeiten von Krankheiten spielte die Suche nach Informationen zu allgemeinen Fragen der gesunden Lebensführung und Ernährung eine wichtige Rolle. Die Studie verdeutlicht das breite Einsatzspektrum verknüpfter Web Tracking- und Befragungsdaten jenseits ihrer bisheriger Anwendungsschwerpunkte in der Nachrichten- und politischen Kommunikationsforschung

Search Engine Use for Health-Related Purposes: Behavioral Data on Online Health Information-Seeking in Germany

Internet searches for health-related purposes are common, with search engines like Google being the most popular starting point. However, results on the popularity of health information-seeking behaviors are based on self-report data, often criticized for suffering from incomplete recall, overreporting, and low reliability. Therefore, the current study builds on user-centric tracking of Internet use to reveal how individuals actually behave online. We conducted a secondary analysis of passively recorded Internet use logs to examine the prevalence of health-related search engine use, the types of health information searched for, and the sources visited after the searches. The analysis revealed two key findings. 1) We largely support earlier survey-based findings on the prevalence of online health information seeking with search engines and the relatively minor differences in information-seeking behaviors between socio-demographic groups. 2) We provide a more granular picture of the process of HISB using search engines by identifying different selection patterns depending on the scope of the searches

Moving from Offline to Online: How COVID-19 Affected Research in the Social and Behavioral Sciences

During the COVID-19 pandemic, researchers have faced a lot of challenges related to their daily work. This article introduces a special issue of the American Behavioral Scientist, which particularly focuses on methodological challenges caused by the COVID-19 pandemic. Based on a brief review of the literature as well as the studies in this issue, we argue that the pandemic has sparked significant methodological innovations with respect to design, data collection, study documentation, and scholarly collaboration. We distinguish two types of innovations, both conceptualized as the outcome of an unprecedented external shock. First, “methodological compromises” that enabled data collection during the pandemic, but are inferior to established approaches. These methodological compromises, therefore, may be abandoned in post-pandemic times. Second, there are also “methodological game changers” that are superior to classic approaches and thus may prevail in the long run. Regardless of the type, we call scholars in the social and behavioral sciences to systematically test, compare, and evaluate the methodological innovations brought to us as a result of the COVID-19 pandemic

Alte und neue Qualitätskriterien für die Inhaltsanalyse: Eine kritische Perspektive auf die zentrale Methode der Kommunikationswissenschaft

Content analysis is one of the core methods of communication science. However, it is currently confronted with several challenges, such as the influx of procedures, data, and measurements emerging from computational methods. To understand how communication science adapts its methods while simultaneously reassuring their ongoing functionality, the six contributions in this Special Issue focus on (re)established quality criteria for content analysis. They showcase the fact that while manual content analysis (and human coders) is still at the core of our methodology, traditional quality criteria are being reinterpreted and approximated, often in light of open science practices and computational text analysis. Therefore, we call for further reflection on conceptual clarity and methodological approaches related to traditional quality criteria (validity, reliability), how they may be reestablished (reproducibility, robustness, and replicability), as well as criteria that have recently come into focus (e.g., ethics). By bringing together leading scholars in this Special Issue, we aim to contribute to moving content analysis forward as a method based on insights from both inside and outside our discipline

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

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