SIGNALING IN CONTENT SHARING PLATFORMS SIGNALING IN CONTENT SHARING PLATFORMS

A Community Data Trustee for Sensitive Data Sharing in Far-Right Online Research

Data sharing poses a major challenge for digital media and communication research, particularly in sensitive areas such as far-right online studies. This article introduces the innovative concept of a “community data trustee” (CDT), a research infrastructure aimed at fostering collaboration and the sharing of research data, such as digital account lists. Compiling these lists is a critical yet labor-intensive step in many research projects; sharing them could significantly reduce effort and improve data quality. However, especially in sensitive research areas, sharing remains rare due to legal uncertainties and limited incentives. This hinders the traceability and comparability of research findings and threatens overall research quality. To address these challenges, we introduce the CDT as a collective research infrastructure that promotes the shared use of extensive account lists. The CDT views actor directories as a communal asset, developed and utilized based on mutually agreed-upon guidelines. A principle of reciprocity underpins this model: Those who access the lists also contribute to their updates and expansions, returning them to the data pool. An online portal facilitates the exchange and collaborative maintenance of the data. The setup of the CDT includes appropriate technical measures to ensure compliance with data protection and security standards, along with a robust regulatory framework that creates a legally secure environment for sharing personal data. This approach aims to (a) incentivize data sharing, (b) foster trust and legal certainty among research projects, (c) enhance data quality through ongoing maintenance, and (d) enhance researcher safety.

Estimating the Impact of Coordinated Inauthentic Behavior on Content Recommendations in Social Networks

Mechanisms of Platform Algorithm-Driven Content Diffusion in New Media: Evidence from Traffic Allocation Systems

In the era of platformized communication, algorithm-driven recommendation systems have fundamentally reshaped content diffusion on new media platforms. Unlike traditional communication models shaped by editorial gatekeeping and follower-based networks, contemporary platforms such as TikTok and YouTube Shorts rely on traffic-driven algorithms to accelerate content diffusion and maximize user engagement. This study investigates how platform algorithms reshape content diffusion through traffic allocation mechanisms and explores the communication logic underlying algorithmic amplification. Drawing on recent studies in platformization, algorithmic governance, and information diffusion, this study adopts a qualitative research design combining literature review, comparative platform case analysis, and mechanism analysis. The study focuses on three research questions: how algorithmic recommendations accelerate content dissemination, how traffic mechanisms influence visibility and user interaction, and what communicative outcomes emerge from algorithm-driven diffusion. The findings indicate that personalized recommendations, real-time feedback systems, and algorithmic traffic allocation mechanisms significantly enhance content dissemination efficiency while simultaneously reinforcing the concentration of user attention and emotional amplification. The paper argues that platform algorithms have evolved from technical tools into key actors in the communication process, reshaping both the logic of content dissemination and the structure of digital public communication.

Exploring trust signals and pathways on cybercrime-as-a-service markets: The case of remote access trojans

As digital technologies evolve, Cybercrime-as-a-Service (CaaS) markets make sophisticated tools, such as Remote Access Trojans (RATs), available to individuals with limited technical expertise. By reducing technical complexity and perceived risk, these markets may create new pathways into cybercrime. Using Gambetta’s (2009) signaling theory of trust, this study explores how CaaS markets trading RATs facilitate involvement into cybercrime. A multifaceted qualitative approach is used to collect data from 21 expert interviews and covert non-participant observations on six online platforms. The findings suggest that CaaS markets become approachable for newcomers by making RATs easier to access, use, and justify. These markets do not facilitate involvement through one single factor, but through a combination of accessibility, user-friendly design, neutralizing messages, and trust signals. Trust in CaaS markets is established and maintained through two types of signals: structural signals and seller-driven signals. Our study highlights how CaaS markets lower barriers to cybercrime through accessible tools and trust signals, emphasizing the importance of disrupting these mechanisms.

Content-centric Networking for Edge-based Internet Services: a Systematic Literature Review of Scalability, Security, and Quality of Service (2020–2026)

Content-Centric Networking (CCN), together with the wider family of Information-Centric Networking (ICN) and Named Data Networking (NDN), reorganizes communication around named content rather than host addresses, an approach that aligns naturally with the distributed, latency-sensitive character of edge computing. This paper reports the protocol and instrumentation of a systematic literature review examining how CCN supports edge-based Internet services along three intertwined dimensions, scalability, security, and quality of service, across peer-reviewed work published between 2020 and 2026. Guided by the PRISMA 2020 statement, the review specifies a reproducible search across six databases, transparent inclusion and exclusion criteria, a structured data-extraction form, a methodological quality-appraisal rubric, and a thematic coding scheme aligned to four research questions. The Introduction and Methodology are presented in IMRaD form, and the complete research instrument is supplied. The design privileges analytical transparency, replicability, and interpretive depth over aggregate effect estimation

Delay Information Sharing in Two-Sided On-Demand Platforms

Problem Definition: We study delay information disclosure policies for on-demand platforms serving two user classes (customers and providers) who seek matches using the platform. The platform's objective is to maximize the match rate by choosing the level of information—no information, binary information (indicating whether the wait is zero or non-zero), or occupancy information (indicating the expected delay based on the number of users currently in the system)—to disclose to each user class. Users of each class are strategic and decide whether to join or balk based on the delay information disclosed to them. Methodology/results: We consider two user types in each user class: patient users who are willing to wait for a match and impatient users who are not. We use continuous-time Markov chains to model the system as two-sided queues and employ equilibrium analysis to characterize users' joining behavior and the platform's match rate under each information disclosure policy. We show that the two-sided system decouples and disclosure decisions can be analyzed as two one-sided systems only if some level of information (binary or occupancy) is disclosed to both user classes. We find that disclosing binary information dominates disclosing no information or occupancy information to a user class when its patient users are sufficiently patient, while disclosing occupancy information dominates disclosing binary information to a user class when there are enough patient users. Numerical experiments show that disclosing occupancy information to both user classes, while often suboptimal, is typically not suboptimal by much. Finally, we find that compared to the platform's optimal disclosure choice, a user class may prefer more or less granular information for themselves or the other class. Furthermore, this utility analysis does not lend itself to decoupling. Managerial implications: Our findings hold crucial implications for platform managers: carefully evaluating the chosen information-sharing strategy is imperative, and guidelines from the one-sided literature are generally inadequate for making disclosure decisions