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The article addresses the issue of adapting printed women’s press to new conditions of consumption via social media. The subject of the study was women’s multi-subject periodicals and official Instagram profiles related to them. The aim of the study was to determine: whether in view of the above-mentioned magazines, a multi-platform strategy was used, based on the simultaneous operation of a print magazine and an Instagram profile and, based on the content, what role the account plays in the multi-platform distribution of content. For this purpose, the media content analysis method was used. All posts published on the profiles of 10 multi-subject women’s magazines between January 1 and March 31, 2022 were analyzed. Based on the results of the analysis, the author comes to the conclusion that the introduction of a new social distribution channel is not a priority for all discussed sub-segments of women’s press. It was established that the main purpose of the “Świat Kobiety”, “Pani” and “Twój Styl” profiles is to promote other brand channels (the printed issue and the magazine’s website), while the content of the “Glamour”, “Elle”, “Zwierciadło”, “Women’s Health”, “Claudia” and “Olivia” profiles is to contribute to the growth of the account and compete with other Instagram profiles. The article fills the gap related to research on women’s press in the era of platformization of print media. It may be of cognitive value to press experts and new media researchers dealing with the issue of multi-platform strategies.

Content delivery networks (CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning (DRL) offers a promising solution for intelligent zero-touch network governance. However, the black-box nature of DRL models poses challenges in understanding and making trusting decisions. In this paper, we propose an explainable reinforcement learning (XRL)-based intelligent edge service caching approach, namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence (XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent’s Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental inputs. The results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service (QoS), and space utilization.

A defining property of the World Wide Web is a content site's ability to place virtually costless hyperlinks to third-party content as a substitute or complement to its own content. Costless hyperlinking has enabled new types of players, usually referred to as content aggregators, to successfully enter content ecosystems, attracting traffic and revenue by hosting links to the content of others. This, in turn, has sparked a heated controversy between content creators and aggregators regarding the legitimacy and costs/benefits of uninhibited free linking. To our knowledge, this work is the first to model the complex interplay between content and links in settings where a set of sites compete for traffic. We develop a series of analytical models that distill how hyperlinking affects the (a) incentives of content nodes to produce quality content versus link to third-party content, (b) profits of the various stakeholders, (c) average quality of content that becomes available to consumers, and (d) impact of content aggregators. Our results provide a nuanced view of the so-called link economy, highlighting both the beneficial consequences and the drawbacks of free hyperlinks for content creators and consumers. This paper was accepted by Lorin Hitt, information systems.

Twitch users watched over 1.2 billion hours of streaming video in a single month in 2020, with the vast majority of these hours devoted to videogames. The most popular streamers who create this content are often powerful influencers in a rapidly growing industry, and many industries now see videogame influencer marketing as a key aspect of their marketing mix. However, while some streamers have amassed incredible popularity on Twitch, the factors that drive live-streaming viewership remain poorly understood. This study empirically examines a large population of Twitch streamers to explore this existing gap in the current research and explain how potential viewers make the decision to patronize a Twitch streamer. Using panel data on the actions and characteristics of Twitch streamers combined with other sources, the study identifies the heuristic cues most associated with successful Twitch streamers. Ultimately, the study identifies and evaluates multiple heuristics around Twitch content delivery practices, with significant implications for any live-streaming context.

Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge–fog–cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge–fog–cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.

Web 2.0, the second phase in the Web's evolution, is attracting the attention of IT professionals, businesses, and Web users. Web 2.0 is also called the wisdom Web, people-centric Web, participative Web, and read/write Web. Web 2.0 harnesses the Web in a more interactive and collaborative manner, emphasizing peers' social interaction and collective intelligence, and presents new opportunities for leveraging the Web and engaging its users more effectively. Within the last two to three years, Web 2.0, ignited by successful Web 2.0 based social applications such as MySpace, Flickr, and YouTube, has been forging new applications that were previously unimaginable.

This article analyzes the effects of net neutrality regulation on investment incentives for Internet service providers (ISPs) and content providers (CPs), and their implications for social welfare. Concerning the ISPs' investment incentives, we find that capacity expansion decreases the sale price of the priority right under the discriminatory regime. Thus, contrary to ISPs' claims that net neutrality regulations would have a chilling effect on their incentive to invest, we cannot dismiss the possibility of the opposite. A discriminatory regime can also weaken CPs' investment incentives because of CPs' concern that the ISP would expropriate some of the investment benefits.

Along with the continuing evolution of the Internet and its applications, Content Delivery Networks (CDNs) have become a hot topic with both opportunities and challenges. CDNs were mainly proposed to solve content availability and download time issues by delivering content through edge cache servers deployed around the world. In our previous work, we presented a novel CDN architecture based on a Fog computing environment as a promising solution for real-time applications. In such architecture, we proposed to use a name-based routing protocol following the Information Centric Networking (ICN) approach, with a popularity-based caching strategy to guarantee overall delivery performance. To validate our design principle, we have implemented the proposed Fog-based CDN architecture with its major protocol components and evaluated its performance, as shown through this article. On the one hand, we have extended the Optimized Link-State Routing (OLSR) protocol to be content aware (CA-OLSR), i.e., so that it uses content names as routing labels. Then, we have integrated CA-OLSR with the popularity-based caching strategy, which caches only the most popular content (MPC). On the other hand, we have considered two similar architectures for conducting performance comparative studies. The first is pure Fog-based CDN implemented by the original OLSR (IP-based routing) protocol along with the default caching strategy. The second is a classical cloud-based CDN implemented by the original OLSR. Through extensive simulation experiments, we have shown that our Fog-based CDN architecture outperforms the other compared architectures. CA-OLSR achieves the highest packet delivery ratio (PDR) and the lowest delay for all simulated numbers of connected users. Furthermore, the MPC caching strategy shows higher cache hit rates with fewer numbers of caching operations compared to the existing default caching strategy, which caches all the pass-by content.

Content Delivery Networks (CDNs) have grown in popularity as a result of the ongoing development of the Internet and its applications. The workload on streaming media service systems can be significantly decreased with the help of the cooperative edge-cloud computing architecture. In the traditional works, a different types of content placement and routing algorithms are developed for improving the content delivery of cloud systems with reduced delay and cost. But, the majority of existing algorithms facing complexities in terms of increased resource usage, ineffective delivery, and high system designing complexity. Therefore, the proposed work aims to develop a new framework, named as, Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud CDNs. Here, the Chaotic Krill Herd Optimization (CKHO) method is used to optimize the resource usage for content placement. Then, a Hierarchical Probability Routing (HPR) model is employed to enable a dependable end-to-end data transmission with an optimized routing path. The performance of the proposed HORCP model is validated and compared by using several performance metrics. The obtained results are also compared with current state-of-the-art methodologies in order to show the superiority of the proposed HORCP model. By using the HORCP mechanism, the overall memory usage of the network is reduced to 80%, CPU usage is reduced to 20%, response is minimized to 2 s, and total congestion cost with respect to the network load level is reduced to 100.

Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function chain deployment in the particular context of Live-Streaming in virtualized content delivery networks using deep reinforcement learning. We use an Enhanced Exploration, Dense-reward mechanism over a Dueling Double Deep Q Network (E2-D4QN). Our model assumes to use network function virtualization at the container level. We carefully model processing times as a function of current resource utilization in data ingestion and streaming processes. We assess the performance of our algorithm under bounded network resource conditions to build a safe exploration strategy that enables the market entry of new bounded-budget vCDN players. Trace-driven simulations with real-world data reveal that our approach is the only one to adapt to the complexity of the particular context of Live-Video delivery concerning the state-of-art algorithms designed for general-case service function chain deployment. In particular, our simulation test revealed a substantial QoS/QoE performance improvement in terms of session acceptance ratio against the compared algorithms while keeping operational costs within proper bounds.