Online Health Communities (OHCs) are vital for patient support, but the massive scale of user-generated content makes manual monitoring of user well-being and engagement drivers impossible. This study integrates Self-Determination Theory with a large-scale computational analysis of${1. 0 3 \mathrm{M}}$questions and${6. 0 \mathrm{M}}$replies, applying a fine-tuned DistilRoBERTa affect model ($\kappa=0.74$vs. human) and Poisson regression (pseudo$\mathrm{R} 2={0. 0 3}$) to quantify how emotional tone and informational intent predict engagement of over one million posts from the MedHelp platform. Our results show that, while informational contributions consistently predict higher engagement, the impact of emotional expression is highly context-dependent. For instance, expressions of sadness and anger, which have a minor effect on engagement overall, are strongly associated with increased community response in high-stakes forums like the Cancer community. These findings demonstrate the need for context-aware moderation tools and provide a scalable framework for designing more responsive and effective digital health interventions.

The increasing integration of capacitive fingerprint recognition sensors in IoT devices presents new challenges in digital forensics, particularly in the context of advanced fingerprint spoofing. Previous research has highlighted the effectiveness of materials such as latex and silicone in deceiving biometric systems. In this study, we introduce Alginate, a biopolymer derived from brown seaweed, as a novel material with the potential for spoofing IoT-specific capacitive fingerprint sensors. Our research uses Alginate and cutting-edge image recognition techniques to unveil a nuanced IoT vulnerability that raises significant security and privacy concerns. Our proof-of-concept experiments employed authentic fingerprint molds to create Alginate replicas, which exhibited remarkable visual and tactile similarities to real fingerprints. The conductivity and resistivity properties of Alginate, closely resembling human skin, make it a subject of interest in the digital forensics field, especially regarding its ability to spoof IoT device sensors. This study calls upon the digital forensics community to develop advanced anti-spoofing strategies to protect the evolving IoT infrastructure against such sophisticated threats.

The speed and volume at which misinformation spreads on social media have motivated efforts to automate fact-checking which begins with stance detection. For fake news stance detection, for example, many classification-based models have been proposed often with high complexity and hand-crafted features. Although these models can achieve high accuracy scores on a targeted small corpus of fake news, few are evaluated on a larger corpus of fake and conspiracy sites due to efficiency limitations and the lack of compatibility with the actual fact-checking process. In this article, we propose a practical two-stage stance detection model that is tailored to the real-life problem. Specifically, we integrate an information retrieval system with an end to end memory network model to sort articles based on their relevance to the claim and then identify the fine-grained stance of each relevant article towards its given claim. We evaluate our model on the Fake News Challenge dataset (FNC-1). The results show that the performance of our model is comparable to those of the state-of-the-art models, average weighted accuracy of 82.1, while it closely follows the real-life process of fact-checking. We also validate our model with a large dataset from a real-life fact-checking website (i.e.,Snopes.com), and the findings demonstrate the capability of the model in distinguishing false from true news headlines.