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Author: search.filters.author.Ohme, Jakob

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Now showing 1 - 10 of 25
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    D[X]IM—the Dynamic Intermediary Model of communicative transaction on digital platforms in a networked public sphere
    (2025-10-31) Ohme, Jakob; Mayer, Anna-Theresa; Charlton-Czaplicki, Timothy; Gaisbauer, Felix; Wedel, Lion; Fan, Yangliu; Neuberger, Christoph
    This study introduces the Dynamic Intermediary Model (D[X]IM) to address how knowledge processes have evolved with digital platforms by shifting from a dyadic to a triadic communication model of content flow with a potential intermediary. This intermediary, which can be a journalist, influencer, artificial agent, or another platform actor, provides services to the source and recipient of a message, thereby transforming traditional direct communication. It aims to better understand information diffusion in the networked public sphere by recognizing the intermediary’s role in altering source-recipient dynamics. The D[X]IM applies across different communication levels (macro, meso, and micro) and is designed for empirical research using diverse methodologies. It focuses on single instances of platform communication to explore the impact of intermediated communication. The article concludes with a research agenda and examples of how D[X]IM can be applied in empirical research.
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    The platform matters: cross-platform differences in data donation willingness, behavior, and bias
    (Taylor & Francis, 2025-12) Wedel, Lion; Ohme, Jakob; Mayer, Anna-Theresa; Gaisbauer, Felix; Fan, Yangliu
    Data donations are a method to access user-level digital trace data, as they provide fine-grained measures of content exposure on social media. The interest in data donation as a data collection method is accompanied by a broad uncertainty about the reasons that drive the donation of data by users. The current literature lacks comparative analysis across various platforms. This study investigates platform-specific predictors for data donation behavior of a non-probability quota sample of German social media users (N = 2,296) for YouTube, Facebook, Instagram, and TikTok and the resulting non-response biases. Based on the analysis of 340 data donation packages, we find that participants are less likely to donate TikTok data compared to the other platforms. Gender is the main driver during the willingness step for drop offs, while political leaning is a key predictor for all platforms except Facebook during the donation stage. Data donors tend to self-report less active social media usage with news and political content than those who do not donate data. Our findings highlight the importance of considering platform-specific differences in expected donation rates, biases, and the potential for discrepancies between indicated willingness and actual donation behavior when designing and interpreting data donation studies.
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    Bursting Self-reports? Comparing Sampling Frequency Effects of Mobile Experience Sampling Method on Compliance, Attrition, and Sample Biases
    (2025) Ohme, Jakob; Charlton, Timothy; Toth, Roland; Araujo, Theo; De Vreese, Claes H.
    In-situ measurements, using the experience sampling method (ESM), can provide insight into behaviors and contextual factors by allowing individuals to self-report them via text or push messages on a smartphone close to the behavior of interest. However, more is needed to know about the data quality of these measures, particularly the impact of sampling frequency. This study aims to examine the effects of different sampling frequencies on compliance, sample biases, and reactivity of measures in the context of digital media use. In July 2021, a group of Dutch citizens (n=250) was randomly assigned to either a standard daily-intensive burst measure (DI-BM; seven surveys across the day) or hourly-intensive burst measure (HI-BM; 12 surveys over two hours per day) condition and surveyed across seven consecutive days, resulting in a total number of 16,135 surveys sent. Results indicate higher compliance in the standard ESM condition than in the burst ESM condition.
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    Digital turn without digital methods? Mapping the journey of journalism studies
    (2025) Fan, Yangliu; Ohme, Jakob; Neuberger, Christoph
    Recent years have seen a growing diversity in journalism studies, primarily ascribed to digital transformation in the contemporary context. Analyzing 6,770 publications from the five major journalism journals—*Journalism*, *Journalism & Mass Communication Quarterly*, *Journalism Practice*, *Journalism Studies*, and *Digital Journalism*—between 1995 and 2022, we find new evidence that the digital turn is highly visible in journalism studies. Using document co-citation analysis, we first have identified distinct and coherent, yet loosely integrated, research clusters that focus on different journalistic topics, i.e., specialties. Second, we find that digital journalism has not only been integrated into the research agendas within the field but has also formed stand-alone and distinct research clusters. We further show that field structure has developed over the years in response to digital transformation. Yet, digital and computational methods remain in the stark minority compared with the more traditional methods. Our results suggest that journalism studies could benefit from novel inter-cluster communications and methodological innovations.
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    Exploring temporal dynamics in digital trace data: mining user-sequences for communication research
    (arXiv, 2025) Fan, Yangliu; Ohme, Jakob; Wedel, Lion
    Communication is commonly considered a process that is dynamically situated in a temporal context. However, there remains a disconnection between such theoretical dynamicality and the non-dynamical character of communication scholars' preferred methodologies. In this paper, we argue for a new research framework that uses computational approaches to leverage the fine-grained timestamps recorded in digital trace data. In particular, we propose to maintain the hyper-longitudinal information in the trace data and analyze time-evolving 'user-sequences,' which provide rich information about user activity with high temporal resolution. To illustrate our proposed framework, we present a case study that applied six approaches (e.g., sequence analysis, process mining, and language-based models) to real-world user-sequences containing 1,262,775 timestamped traces from 309 unique users, gathered via data donations. Overall, our study suggests a conceptual reorientation towards a better understanding of the temporal dimension in communication processes, resting on the exploding supply of digital trace data and the technical advances in analytical approaches.
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    A Common Effort: New Divisions of Labor Between Journalism and OSINT Communities on Digital Platforms
    (2024) Charlton, Timothy; Mayer, Anna-Theresa; Ohme, Jakob
    This 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.
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    Augmenting Data Download Packages – Integrating Data Donations, Video Metadata, and the Multimodal Nature of Audio-visual Content
    (2024) Wedel, Lion; Ohme, Jakob; Araujo, Theo
    This 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.
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    Cascades or salmons? Longitudinal upstream and downstream effects of political participation
    (2024) Ohme, Jakob; Azrout, Rachid; Moeller, Judith
    Digitally networked and new, unconventional activities allow citizens to participate politically in activities that are low in the effort and risks they bear. At the same time, low-effort types of participation are more loosely connected to democratic political systems, thereby challenging established modes of political decision-making. This can set in motion two competing dynamics: While some citizens move closer to the political system in their activities (upstream effects), others engage in political activities more distant from it (downstream effects). This study investigates non-electoral participation trajectories and tests intra-individual change in political participation types over time, exploring whether such dynamics depend on citizens’ exposure to political information. Utilizing a three-wave panel survey (n = 3490) and random intercept cross-lagged panel models with SEM, we find more evidence for downstream effects but detect overall diverse participation trajectories over time and a potentially crucial role of elections for non-electoral participation trajectories.
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    Headlines, Pictures, Likes: Attention to Social Media Newsfeed Post Elements on Smartphones and in Public
    (SAGE Publications, 2024-04) Mayer, Anna-Theresa; Ohme, Jakob; Maslowska, Ewa; Segijn, Claire M.
    Scrolling through a social media newsfeed has become almost ubiquitous. Yet, it remains unknown what specific post elements people pay attention to and whether this varies depending on how they access social media newsfeeds. In an eye-tracking experiment among university students (N = 201), we compare user attention to specific post elements like source, title, or picture, in a dynamic Facebook newsfeed by device (desktop vs. mobile) and smartphone usage environment (private vs. public). Significant attentional differences occur at the level of the newsfeed post elements. Users pay less attention to visual information on the mobile newsfeed and more attention to textual post elements in a public setting.
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    Chatting about the unaccepted: Self-disclosure of unaccepted news exposure behaviour to a chatbot
    (2023) Ischen, Carolin; Butler, Janice; Ohme, Jakob
    Conversational technologies such as chatbots have shown to be promising in eliciting self-disclosure in several contexts. Implementing such a technology that fosters self-disclosure can help to assess sensitive topics such as behaviours that are perceived as unaccepted by others, i.e. the exposure to unaccepted (alternative) news sources. This study tests whether a conversational (chatbot) format, compared to a traditional web-based survey, can enhance self-disclosure in the political news context by implementing a two-week longitudinal, experimental research design (n = 193). Results show that users disclose unaccepted news exposure significantly more often to a chatbot, compared to a traditional web-based survey, providing evidence for a chatbots’ ability to foster the disclosure of sensitive behaviours. Unlike our hypotheses, our study also shows that social presence, intimacy, and enjoyment cannot explain self-disclosure in this context, and that self-disclosure generally decreases over time.