Opinion-based Homogeneity on YouTube : Combining Sentiment and Social Network Analysis

The growing complexity of political communication online goes along with increasing methodological challenges to process communication data properly in order to investigate public concerns such as the existence of echo chambers. To cover the full range of political diversity in online communication, we argue that it is necessary to focus on specific political issues. This study proposes an innovative combination of computational methods, including natural language processing and social network analysis, that serves as a model for future research on the evolution of opinion climates in online networks. Data were gathered on YouTube, enabling the assessment of users’ expressed opinions on two political issues. Results provided very limited evidence for the existence of opinion-based homogeneity on YouTube. This was true even when the whole network was divided into sub-networks. Findings are discussed in light of current computational communication research and the vigorous debate on echo chambers in online networks

Audio-as-Data Tools: Replicating Computational Data Processing

The rise of audio-as-data in social science research accentuates a fundamental challenge: establishing reproducible and reliable methodologies to guide this emerging area of study. In this study, we focus on the reproducibility of audio-as-data preparation methods in computational communication research and evaluate the accuracy of popular audio-as-data tools. We analyze automated transcription and computational phonology tools applied to 200 episodes of conservative talk shows hosted by Rush Limbaugh and Alex Jones. Our findings reveal that the tools we tested are highly accurate. However, despite different transcription and audio signal processing tools yield similar results, subtle yet significant variations could impact the findings’ reproducibility. Specifically, we find that discrepancies in automated transcriptions and auditory features such as pitch and intensity underscore the need for meticulous reproduction of data preparation procedures. These insights into the variability introduced by different tools stress the importance of detailed methodological reporting and consistent processing techniques to ensure the replicability of research outcomes. Our study contributes to the broader discourse on replicability and reproducibility by highlighting the nuances of audio data preparation and advocating for more transparent and standardized practices in this area

Ethics in Computational Communication Science: Between values and perspectives

In Computational Communication Science (CCS) researchers grapple with intricate ethical challenges arising from the collection and analysis of complex data sets often including sensitive or copyrighted data. Rooted in two opposing lines of philosophical arguments - deontology and consequentialism - we argue that CCS research is particularly difficult to be projected onto this ethical spectrum. Our study aims to empirically assess the nature and prevalence of provided arguments and influencing factors for ethical decision-making in CCS research. Through a manual content analysis of 476 CCS studies, sampled from a corpus of 22,375 collected communication science articles, we shed light on data sharing practices and ethical reflections of CCS researchers. Findings indicate large room for maneuver. The majority of studies (89.50%) chose not to share their data, while 6.93% chose to share their data either full or partially. Only 5.88% of studies explicitly addressed general ethical considerations. Ethical review processes were mentioned by 6.51% of studies, with the majority pointing at ethical procedures such as obtaining informed consent, data anonymization measures, or debriefing. This suggests that researchers in CCS prioritize context-specific ethical procedures in the absence of field-specific standards, emphasizing the importance of flexibility in addressing ethical considerations

Computational Communication Science in a Digital Society

Computational methods have added new approaches to the way many communication scientists do their work. We identify four developments that accelerated the adaption of computational methods: the increasing availability of digital data, the surge of large amounts of user-created data, the need to study new artefacts, and the improved accessibility of computational resources. We describe new data acquisition techniques, new research designs, and new analytical approaches that characterise the field. After discussing contributions to the open source community, to the methodological toolbox, as well as to the testing and development of theories, we sketch in broad strokes a research agenda for the coming years

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

New Formats, New Methods: Computational Approaches as a Way Forward for Media Entertainment Research

The rise of new technologies and platforms, such as mobile devices and streaming services, has substantially changed the media entertainment landscape and continues to do so. Since its subject of study is changing constantly and rapidly, research on media entertainment has to be quick to adapt. This need to quickly react and adapt not only relates to the questions researchers need to ask but also to the methods they need to employ to answer those questions. Over the last few years, the field of computational social science has been developing and using methods for the collection and analysis of data that can be used to study the use, content, and effects of entertainment media. These methods provide ample opportunities for this area of research and can help in overcoming some of the limitations of self-report data and manual content analyses that most of the research on media entertainment is based on. However, they also have their own set of challenges that researchers need to be aware of and address to make (full) use of them. This thematic issue brings together studies employing computational methods to investigate different types and facets of media entertainment. These studies cover a wide range of entertainment media, data types, and analysis methods, and clearly highlight the potential of computational approaches to media entertainment research. At the same time, the articles also include a critical perspective, openly discuss the challenges and limitations of computational methods, and provide useful suggestions for moving this nascent field forward