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Differential social media affordances: an actor type-centric, intermediate-level approach using the case of social movements
(2025-11-10) Baden, Christian; Heft, Annett; Vaughan, Michael; Pfetsch, Barbara
Social media have profoundly changed social communication practices across a vast range of contexts. To theorize these changes, numerous authors have proposed digital affordances as a conceptual lens. Yet, to date, most accounts of digital affordances either gloss broadly over cross-platform or use-dependent differences in practices; or they are highly context-specific, obstructing theoretical integration. In this article, we conceptualize social media affordances on an intermediate level of abstraction that foregrounds consequential differences in how digital social media platforms structure social communication practices. Focusing on the characteristic communication needs of social movements as an exemplary case, we identify how social media platforms present users with differential affordances for articulating public claims, building collective identities, and mobilizing contentious performances. We examine how key contextual conditions alter the value of differential affordances, potentially resulting in differential communication practices and platform preferences. We conclude by discussing key opportunities of our approach for comparative research and theory building.
Attributing Coordinated Social Media Manipulation: A Theoretical Model and Typology.
(2025) Thiele, Daniel; Milzner, Miriam; Gong, Baoning; Pfetsch, Barbara; Heft, Annett
Social media are key arenas for public opinion formation, but are susceptible to coordinated social media manipulation (CSMM), that is, the orchestrated activity of multiple accounts to increase content visibility and deceive audiences. Despite advances in detecting and characterizing CSMM, the attribution problem—identifying the principals behind CSMM campaigns—has received little scholarly attention. In this article, we address this gap by synthesizing existing research and developing a theoretical model for understanding CSMM. We propose a consolidated definition of CSMM, identify its key observable and hidden characteristics, and present a rational choice model for inferring principals’ strategic decisions from campaign features. In addition, we present a typology of CSMM campaigns, linking variations in scale, elaborateness, and disguise to principals’ resources, stakes, and influence strategies. Our contribution provides researchers with conceptual and heuristic tools for attribution and invites interdisciplinary and comparative research on CSMM campaigns.
LGDE: Local Graph-based Dictionary Expansion
(2025) Schindler, Juni; Jha, Sneha; Zhang, Xixuan; Bühling, Kilian; Heft, Annett; Barahona, Mauricio
We present Local Graph-based Dictionary Expansion (LGDE), a method for data-driven discovery of the semantic neighbourhood of words using tools from manifold learning and network science. At the heart of LGDE lies the creation of a word similarity graph from the geometry of word embeddings followed by local community detection based on graph diffusion. The diffusion in the local graph manifold allows the exploration of the complex nonlinear geometry of word embeddings to capture word similarities based on paths of semantic association, over and above direct pairwise similarities. Exploiting such semantic neighbourhoods enables the expansion of dictionaries of pre-selected keywords, an important step for tasks in information retrieval, such as database queries and online data collection. We validate LGDE on two user-generated English-language corpora and show that LGDE enriches the list of keywords with improved performance relative to methods based on direct word similarities or co-occurrences. We further demonstrate our method through a real-world use case from communication science, where LGDE is evaluated quantitatively on the expansion of a conspiracy-related dictionary from online data collected and analysed by domain experts. Our empirical results and expert user assessment indicate that LGDE expands the seed dictionary with more useful keywords due to the manifold-learning-based similarity network.
Veiled conspiracism: Particularities and convergence in the styles and functions of conspiracy-related communication across digital platforms
(2025) Bühling, Kilian; Zhang, Xixuan; Heft, Annett
Digital communication venues are essential infrastructures for anti-democratic actors to spread harmful content such as conspiracy theories. Capitalizing on platform affordances, they leverage conspiracy theories to mainstream their political views in broader public discourse. We compared the word choice, language style, and communicative function of conspiracy-related content to understand its platform-dependent differences and convergence. Our cases are the conspiracy theories of the New World Order and Great Replacement, which we analyzed on 4chan/pol/, Twitter, and seven alternative US news media longitudinally from 2011 to 2021. The conspiracy-related texts were comparatively analyzed using a multi-method approach of computational and quantitative text analyses. Our results show that conspiracy narrations are increasingly present in all venues. While language differs vastly between platforms, we observed a style convergence between Twitter and 4chan. The results show how more coded language veils the spread of racist and antisemitic content beyond the so-called dark platforms.
Challenges 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, Miriam
Taking 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.
Right Topic, Right Source? Source Diversity and Balance in Right-Wing Alternative News Content Across Topics
(2024) Heft, Annett; Ramsland, Tim; Mayerhöffer, Eva
This article investigates how the hybrid nature of right-wing alternative news media striving for journalistic legitimacy and partisan credibility plays out on source and topical diversity and balance in article content. The article draws on a sample of 1000 randomly selected articles published by 20 right-wing alternative online news media from six countries (the US, the UK, Germany, Austria, Denmark, and Sweden) from March 2019 to February 2020 (i.e., in “routine” pre-COVID-19 times). The results show that most of the alternative media outlets in the sample cover relatively broad topical spectra. More speciﬁcally, US and UK media primarily focus on politics and policy issues, whereas Scandinavian media are more heavily geared toward societal issues and crime coverage. Overall, right-wing alternative news content is characterized by a variety of partisan and non-partisan sources. However, core partisan topic areas, such as politics and mass media, are more likely to include partisan and especially right-wing sources. Often, with respect to these topics, right-wing sources are evaluated positively, and left-wing sources are evaluated negatively. Finally, right-wing and non-right-wing sources often appear in separate articles rather than in direct confrontation.
Politicization and Right-Wing Normalization on YouTube: A Topic-Based Analysis of the “Alternative Influence Network”
(2023) Knüpfer, Curd Benjamin; Schwemmer, Carsten; Heft, Annett
Scholarship has highlighted the rise of political influencer networks on YouTube, raising concerns about the platform’s propensity to spread and even incentivize politically extreme content. While many studies have focused on YouTube’s algorithmic infrastructure, limited research exists on the actual content in these networks. Building on Lewis’s (2018) classification of an “alternative influencer” network, we apply structural topic modeling across all text-based autocaptions from her study’s sample to identify common topics featured on these channels. This allows us to gauge which topics appear together and to trace politicization over time. Through network analysis, we determine channel similarities and evaluate whether deplatformed channels influenced topic shifts. We find that political topics increasingly dominate the focus of all analyzed channels. The convergence of culture and politics occurs mostly about identity-driven issues. Furthermore, more extreme channels do not form distinct clusters but blend into the larger content-based network. Our findings illustrate how political topics may function as connective ties across an initially more diverse network of YouTube influencer channels.
Mapping a Dark Space: Challenges in Sampling and Classifying Non-Institutionalized Actors on Telegram
(2023) Jost, Pablo; Heft, Annett; Bühling, Kilian; Zehring, Maximilian; Schulze, Heidi; Bitzmann, Hendrik; Domahidi, Emese
Crafted as an open communication platform characterized by high anonymity and minimal moderation, Telegram has garnered increasing popularity among activists operating within repressive political contexts, as well as among political extremists and conspiracy theorists. While Telegram offers valuable data access to research non-institutionalized activism, scholars studying the latter on Telegram face unique theoretical and methodological challenges in systematically defining, selecting, sampling, and classifying relevant actors and content. This literature review addresses these issues by considering a wide range of recent research. In particular, it discusses the methodological challenges of sampling and classifying heterogeneous groups of (often non-institutionalized) actors. Drawing on social movement research, we first identify challenges specific to the characteristics of non-institutionalized actors and how they become interlaced with Telegram’s platform infrastructure and requirements. We then discuss strategies from previous Telegram research for the identification and sampling of a study population through multistage sampling procedures and the classification of actors. Finally, we derive challenges and potential strategies for future research and discuss ethical challenges.
Pandemic protesters on Telegram: How platform affordances and information ecosystems shape digital counterpublics
(2023) Bühling, Kilian; Heft, Annett
This study analyzes how platform affordances, their appropriation by movement actors, and these actors’ leveraging of information ecosystems—in combination—helped form a digital counterpublic during the COVID-19 pandemic. It draws on public communication data sent by more than 300 Telegram channels and group chats affiliated with the Querdenken movement over a 2-year period, and combines automated and manual text classification with network analysis. The study demonstrates how Telegram afforded connective and collective action in distinct ways that reflected the movement’s organizational structure and aims, as well as the impact of individual information-sharing on the process of movement-building itself. Accounting for time-dependent dynamics, the study also found that different parts of the counterpublic latched onto and sustained distinct information ecosystems to articulate their claims and mobilize contentious action.
Die Nationale Forschungsdateninfrastruktur – eine Lösung infrastruktureller Bedarfe für die Inhaltsanalyse?
(2023) Heft, Annett; Jünger, Jakob; Niemann-Lenz, Julia; Possler, Daniel
Obwohl die Inhaltsanalyse eine zentrale Stellung in der Kommunikations- und Medienforschung besitzt, existieren kaum Forschungsinfrastrukturen für diese Methode. Gleichzeitig werden in Deutschland seit 2018 große Dateninfrastrukturen in den 27 Konsortien der Nationalen Forschungsdateninfrastruktur (NFDI) aufgebaut. In diesem Beitrag gehen wir aus Perspektive der Forschenden der Frage nach, inwiefern die NFDI-Konsortien Lösungen für die infrastrukturellen Anforderungen in Bezug auf Inhaltsanalysen bieten. Zunächst beleuchten wir diese Anforderungen entlang des Forschungsdaten-Lebenszyklus und identifizieren Leerstellen. Dann explorieren wir, welche Bedarfe die NFDI-Konsortien decken können. Der Schwerpunkt liegt auf Konsortien, die sich auf die Sammlung und Aufbereitung von Text oder multimodalen Daten konzentrieren: KonsortSWD, BERD@NFDI, Text+, NFDI4Memory, NFDI4Culture und NFDI4DataScience. Unsere Untersuchung zeigt, dass die Konsortien bereits viele der Bedarfe abdecken. Allerdings gibt es weder ein Konsortium, in dem Kommunikationswissenschaftler:innen treibende Kräfte sind, noch wird die Inhaltsanalyse explizit berücksichtigt. Wir diskutieren, wie sich Forschungsinfrastrukturen für die Inhaltsanalyse durch die NFDI-Strukturen weiterentwickeln ließen. , Abstract Content analysis has a central position in media and communication research, yet research infrastructures for the method are still scarce. At the same time, the 27 consortia of the National Research Data Infrastructure (NFDI) have started to establish large data infrastructures in Germany since 2018. In this paper, we explore from the perspective of researchers whether the NFDI consortia provide solutions to the infrastructural needs of content analysis. First, we illustrate these needs throughout the research data lifecycle and identify shortcomings. We then explore whether the NFDI consortia can meet these needs. The focus lies on consortia that concentrate on the collection and processing of text or multimodal data: KonsortSWD, BERD@NFDI, Text+, NFDI4Memory, NFDI4Culture, and NFDI4DataScience . Our exploration shows that many of the needs are already being addressed by the consortia. However, there is no consortium in which communication scholars are the driving force and content analysis does not receive explicit consideration. We discuss how research infrastructures for content analysis can be further developed through the NFDI structures.

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