Even though generative artificial intelligence (GenAI) is increasingly integrated into user-facing technologies like social media, its impact on content marketing remains unverified. Early evidence suggests that language models (LLMs) can generate content that rivals human-created content (HCC) in terms of appeal. However, the question of adapting such content for various social media platforms remains unanswered. This study examines the effectiveness of an LLM, GPT-4, in customizing cross-platform content for Facebook, Instagram, and X. A total of 892 participants evaluated 30 pairs of AI-created content (ACC) and HCC. The findings reveal that ACC was preferred by users, delivered stronger calls to action, and elicited more user engagement than HCC, especially on Facebook, with a less pronounced effect for shorter posts on X and Instagram. We further generated six data-driven user personas of the 892 participants, illustrating the differences between those who preferred ACC or HCC on the three platforms. The results indicate that GPT-4 can adapt content to platform-specific requirements and maintain high perceived quality, making LLMs applicable for cross-platform content creation for user engagement. Findings contribute to understanding user engagement with AI-generated content across platforms. We also discuss the role of LLMs in content creation, including their ethical implications.

Affiliate marketing is a growing performance-based marketing arrangement in which affiliates are rewarded for getting users to register, purchase, or visit a shopping website [ 16 , 38 ]. This marketing strategy is valued at $18.5 billion USD for 2025 [ 31 , 67 ]. Deal-sharing platforms take advantage of this marketing strategy by acting as storefronts for sellers that showcase promotions and deals. As the service is free to use, they earn commissions through affiliate links [ 36 ] when they lead to sales. We perform an in-depth, end-to-end study of the tracking techniques leveraged by Pepper [ 1 ] and its extended environment, a key player in deal-sharing platforms. Through a systematic 1-month crawl, we analyze the tracking ecosystem of 10 deal-sharing websites active in a diverse range of countries abiding by different privacy laws. Our analysis reveals that a significant part of the tracking occurs during redirect chains [ 40 ] between the deal-sharing platform and the shopping website. We quantify the tracking-specific use of cookies, CNAME cloaking [ 14 ], and link decorations [ 52 , 58 ] within redirect chains. We find that 67.9% of redirect chains leverage at least one of these additional tracking techniques. We show that redirect chains examined in prior work (limited to HTTP-based redirects) are significantly more constrained than those observed in our study (HTTP-, HTML-, and JS-based), which enable more aggressive behavior by dynamically loading additional tracking resources at runtime. Finally, by analyzing the ecosystem of third-party services and the privacy policies of deal-sharing websites, we reveal the omission of numerous actors involved in redirect chains.

Understanding structural regularities across layers in multi-layer networks is essential for uncovering their underlying generative mechanisms. While link prediction has been widely explored in multi-layer networks, it is typically treated as an isolated technical problem, often missing its broader implications for network structure and the mechanisms driving edge formation. In this article, we investigate the extent to which network layers exhibit shared generative regularities. By examining the alignment of latent representations across layers, we assess the similarity of their underlying mechanisms and leverage this alignment to improve predictive performance. To facilitate this, we introduce a new metric, C ross- L ayer G enerative C onsistency ( CLGC ), which quantitatively captures the degree of structural and generative alignment between network layers. CLGC is grounded in the shared-latent space framework, positing that layers generated by similar mechanisms will produce compatible latent representations. To realize this approach, we present SupportNet – Support prediction and consistency analysis in multi-layer Net works–a GCN-based model augmented with adversarial training to effectively learn robust shared-latent space representations. These representations support both accurate link prediction and interpretable evaluation of cross-layer generative consistency. Experiments on real-world multi-layer networks demonstrate that SupportNet delivers strong link prediction results improving AUC by 17.47%, AP by 40.41% and AUPR by 39.59% on the Kapferer dataset, while CLGC reveals significant patterns of structural and generative alignment among layers.

Given a graph G defined in a domain \(\mathcal {G}\) , we investigate locally differentially private mechanisms to release a degree sequence on \(\mathcal {G}\) that accurately approximates the actual degree distribution. Existing solutions for this problem mostly use graph projection techniques based on edge deletion process, using a threshold parameter \(\theta\) to bound node degrees. However, this approach presents a fundamental trade-off in threshold parameter selection. While large \(\theta\) values introduce substantial noise in the released degree sequence, small \(\theta\) values result in more edges removed than necessary. Furthermore, \(\theta\) selection leads to an excessive communication cost. To remedy existing solutions’ deficiencies, we present CADR-LDP, an efficient framework incorporating encryption techniques and differentially private mechanisms to release the degree sequence. In CADR-LDP, we first use the crypto-assisted Optimal- \(\theta\) -Selection method to select the optimal parameter with a low communication cost. Then, we use the LPEA-LOW method to add some edges for each node with the edge addition process in local projection. LPEA-LOW prioritizes the projection with low-degree nodes, which can retain more edges for such nodes and reduce the projection error. Theoretical analysis shows that CADR-LDP satisfies \(\epsilon\) -node local differential privacy. The experimental results on eight graph datasets show that our solution outperforms existing methods.

The 2016 United States presidential election was marked by the abuse of targeted advertising on Facebook. Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Meta in Brazil. To do that we first adapted a browser plugin to gather ads from the timeline of volunteers using Facebook. We managed to convince more than 2,000 volunteers to help our project and install our tool. Then, we use a Convolution Neural Network (CNN) to detect political Meta ads using word embeddings. To evaluate our approach, we manually label a data collection of 10k ads as political or non-political and then we provide an in-depth evaluation of proposed approach for identifying political ads by comparing it with classic supervised machine learning methods. Finally, we deployed a real system that shows the ads identified as related to politics during the 2018 National Brazilian elections. We also investigated early electoral advertisement before the 2020 local Brazilian elections using our model on unsponsored content (regular posts in groups and pages). We noticed that not all political ads we detected were present in the Meta Ad Library for political ads on 2018. Additionally, we found possible early electoral advertisements in 2020, which is forbidden in Brazil. Our results emphasize the importance of enforcement mechanisms for declaring political ads and the need for independent auditing platforms.

The Website Accessibility Conformance Evaluation Methodology (WCAG-EM) directs an auditor to select samples of web pages for accessibility evaluation. However, the sampling method of WCAG-EM cannot statistically claim to represent pages outside of the selected sample. To generate an optimal representative sample, we previously proposed OPTIMAL-EM. In this article, we apply the framework to explore the relationships between two metrics in the framework: web page complexity and accessibility. We first cluster pages by their structure by employing t-distributed Stochastic Neighbour Embedding (t-SNE) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) by representing pages using two approaches: one including all HTML elements and another focusing on block-level HTML elements. We then compare the average complexity of a cluster and the variance of the complexity of a given cluster against the average accessibility of that cluster. To measure their correlations, we conducted a two-stage evaluation: an initial experiment with a website of 388 pages and a validation study with three additional random sites of 500 pages. Our experiments demonstrate that clusters characterised by lower variability in complexity tend to exhibit fewer accessibility barriers, suggesting that standardised and templated web design could be more beneficial to accessibility than more complex pages. Furthermore, the main contribution of this article is showing that by identifying and focusing on clusters with high complexity variance, auditors can target their efforts more effectively, prioritising areas more likely to present accessibility challenges. By providing a more systematic and scalable sampling method, our approach can optimise conformance evaluations of large websites by reducing the human effort involved, focusing on a more targeted assessment of more representative pages.

This work introduces a novel construct we call dream that enables deletable content in otherwise immutable, decentralised storage systems such as Swarm. As an equitable and feasible response to the challenge of data destruction, our solution reconciles the inherent persistence of content-addressed storage with the practical needs, user preferences, and legal requirements of permanent revocation of access. The paper contributes a new deletion model based on access revocation, and presents a mechanism that allows data-sharing users to grant — and later rescind — access to specific data for specific consumers. The solution leverages Swarm’s distributed storage architecture to introduce a procedural way to retrieve decryption keys, realised through a network protocol. The resulting system achieves the desired properties of deniability, revocability, expirability, addressability, and malleability without requiring trusted intermediaries or complex cryptographic primitives. The approach preserves the censorship resistance of decentralised systems allowing for sovereign control over data access by the original uploader, but not by any other party, thus avoiding re-centralisation in order to achieve deletion. Security analysis shows that revocation remains effective even under pervasive adversarial control.

Template engines play a pivotal role in modern web application development by enabling the dynamic rendering of content, products, and user interfaces. Today, they are essential for any website that handles dynamic data, from e-commerce to social media. However, their widespread adoption also makes them attractive targets for attackers seeking to exploit vulnerabilities and gain unauthorized access to web servers. This paper presents a comprehensive assessment of the risks associated with template engines, with a particular focus on the consequences of Server-Side Template Injection (SSTI) and the ease with which such vulnerabilities can escalate to Remote Code Execution (RCE), a critical security concern in web application development.

This editorial introduces the first part of the Special Issue on “Advanced Technologies in the Decentralized Web.” As the Internet evolves toward more user-centric and resilient architectures, concepts like Web3 and Web 3.0 have gained significant prominence. This issue explores key advancements including decentralized machine learning, AI-blockchain integration, identity management, and data sovereignty. We provide an overview of the selected papers, highlighting their contributions to creating a more transparent and secure decentralized digital future.

With the development of Web3.0, decentralized identity and other blockchain-based identity empower users with control, forming the foundational infrastructure for Web3.0 ecosystems. However, existing identity frameworks remain inadequate in addressing critical challenges such as on-chain privacy during identity management and utilization. While prior works like CanDID, Hades, and CertChain explore blockchain-based identity solutions, they fail to meet the specific reqirements of DApps. Moreover, users on blockchain always store their identity data and digital assets across multiple accounts and DApps, but current identity schemes cannot support the cross-account and DApp identity privacy-preserving utilization. To bridge this gap, we propose Web3ID, the first fully DApp-oriented identity framework. By analyzing Ethereum identity proposals and user behavior patterns, we design the Web3ID featuring: on-chain privacy-preserving identity aggregation protocol, provably secure attribute-based access control model, and zk-rollup enhanced off-chain identity management. Experiments demonstrate that Web3ID enables privacy-preserving identity management and authentication on-chain, and guaranteeing access control completeness. The prototype system achieves a 100× improvement in proof/verification efficiency and reduces storage overhead by 85× compared to pure on-chain implementation through off-chain optimization techniques. Moreover, in comparison with other identity privacy solutions, Web3ID exhibits the lowest gas consumption during on-chain utilization and shows strong scalability. As a fully decentralized identity framework supporting end-to-end DApp integration, Web3ID advances Web3.0’s vision of user sovereignty, decentralization, and interoperability. This work establishes both theoretical and practical foundations for on-chain identity systems in Web3.0 ecosystems.