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- ItemAugmenting Data Download Packages – Integrating Data Donations, Video Metadata, and the Multimodal Nature of Audio-visual Content(2024) Wedel, Lion; Ohme, Jakob; Araujo, TheoThis research explores the potential of augmented Data Download Packages (aDDPs) as a novel approach to analyze digital trace data, using TikTok as a use case to demonstrate the broader applicability of the method. The study demonstrates how these data packages can be used in social science research to understand better user behavior, content consumption patterns, and the relationship between self-reported preferences and actual digital behavior.We introduce the concept of aDDPs, which extend the conventional Data Download Packages (DDPs) by augmenting the collected data with survey data, metadata, content data, and multimodal content embeddings, among other possibilities - rendering aDDPs an unprecedentedly rich data source for social science research. This work provides an overview and guidance on collecting, augmenting DDPs, and analyzing the resulting aDDPs.In a pilot study on 18 aDDPs, we use the combination of data components in aDDPs to facilitate research on user engagement behavior and content classification. We showcase the potential of the information breadth and depth that aDDPs depict by exploiting the combination of multimodal content embeddings, the users’ watch history, and survey data. To do so, we train and compare uni- and multimodal classifiers, classify the 18 aDDPs’ videos, and investigate the extent to which user engagement behavior impacts future content suggestions. Furthermore, we compare the users retrieved content with the users’ self-reported content consumption.
- ItemA Common Effort: New Divisions of Labor Between Journalism and OSINT Communities on Digital Platforms(2024) Charlton, Timothy; Mayer, Anna-Theresa; Ohme, JakobThis article explores the interactions between journalistic actors and emerging open-source intelligence and investigation (OSINT) communities. It employs qualitative content analysis of discourse from two OSINT communities surrounding three events following the Russian invasion of Ukraine in 2022, which received substantial coverage in news media. OSINT practices are rapidly becoming a mainstay of the contemporary political process by allowing ordinary citizens to verify information shared through digital platforms, which is traditionally the societal task assigned to journalism. In doing so, they provide a timely factual baseline for opinion formation and political decision-making. This research explores the role constellations resulting from this shift in verification duties from journalistic actors to amateur online communities on digital platforms and maps the fundamental dynamics involved in OSINT. We analyze how information is received and processed in OSINT communities, how digital platforms facilitate the fact-checking process, and how journalism and OSINT interact. Based on our findings, we develop a theoretical framework that distinguishes between the input, throughput, and output phases of OSINT. Our model contributes to a baseline understanding of the crucial and novel partnership between citizens and journalists on digital platforms.
- ItemBook review: Schützeneder, Jonas & Graßl, Michael. (Eds.). (2022). Journalismus und Instagram. Analysen, Strategien, Perspektiven aus Wissenschaft und Praxis [Journalism and Instagram. Analyses, strategies, perspectives from science and practice]. Springer Fachmedien.(2023) Mayer, Anna-TheresaInstagram spielt heute eine wichtige Rolle in der digitalen Medienlandschaft. Laut ARD/ZDF-Onlinestudie 2023 (vgl. Koch, W.: Soziale Medien werden 30 Minuten am Tag genutzt – Instagram ist die Plattform Nummer eins. Ergebnisse der ARD/ZDF-Onlinestudie 2023. Media Perspektiven, 2023, 26, S. 1–8) greifen 25 % der deutschsprachigen Bevölkerung täglich auf die Social-Media-Plattform zurück, 35 % mindestens einmal wöchentlich. Besonders in der jüngeren Altersgruppe ist ihre Beliebtheit erkennbar (täglich: 63 %; mindestens einmal wöchentlich: 79 %). Auch journalistische Inhalte sind längst auf Instagram verfügbar. Während man bereits im internationalen Kontext erste empirische Ansätze zur Erforschung und in der Praxisliteratur einige Reflektionen über das Zusammenspiel von Journalismus und Instagram findet, sind systematische Übersichtswerke zum (deutschen) Journalismus auf Instagram noch rar. Hier setzt der Sammelband „Journalismus und Instagram. Analysen, Strategie, Perspektive aus Wissenschaft und Praxis“ mit der übergreifenden Fragestellung an, „wie sich Journalismus und Instagram aus kommunikationswissenschaftlicher Perspektive analysieren lassen und welche praktischen Beispiele hierzu dienlich sind“ (S. 2–3). Das Ergebnis sind 17 Beiträge von insgesamt 27 Autorinnen und Autoren, die sich in drei Zugänge einteilen lassen.
- ItemChallenges of and approaches to data collection across platforms and time: Conspiracy-related digital traces as examples of political contention(2024) Heft, Annett; Bühling, Kilian; Zhang, Xixuan; Schindler, Dominik; Milzner, MiriamTaking the example of conspiracy-related communication online as one form of contentious politics, this study examines the data collection challenges for multidimensional comparative research across platforms, time, and cultural embeddings. It compares the architectures and features relevant to data collection, access regimes, and use cultures for a set of digital platforms and communication venues. Differentiating between actor- and content-based strategies, this study discusses the potentials and limitations of these approaches, considering differences in platforms, temporal dynamics, and cultural embeddings as well as several layers of equivalence. The discussion highlights crucial insights into designing data collection strategies in multidimensional comparative studies.
- ItemGrounding force-directed network layouts with latent space models(2023) Gaisbauer, Felix; Pournaki, Armin; Banisch, Sven; Olbrich, EckehardForce-directed layout algorithms are ubiquitously used tools for network visualization. However, existing algorithms either lack clear interpretation, or they are based on techniques of dimensionality reduction which simply seek to preserve network-immanent topological features, such as geodesic distance. We propose an alternative layout algorithm. The forces of the algorithm are derived from latent space models, which assume that the probability of nodes forming a tie depends on their distance in an unobserved latent space. As opposed to previous approaches, this grounds the algorithm in a plausible interaction mechanism. The forces infer positions which maximise the likelihood of the given network under the latent space model. We implement these forces for unweighted, multi-tie, and weighted networks. We then showcase the algorithm by applying it to Facebook friendship, and Twitter follower and retweet networks; we also explore the possibility of visualizing data traditionally not seen as network data, such as survey data. Comparison to existing layout algorithms reveals that node groups are placed in similar configurations, while said algorithms show a stronger intra-cluster separation of nodes, as well as a tendency to separate clusters more strongly in multi-tie networks, such as Twitter retweet networks.