Bilateral Filtering Graph Convolutional Network for Multi-relational Social Recommendation in the Power-law Networks

In recent years, advances in Graph Convolutional Networks (GCNs) have given new insights into the development of social recommendation. However, many existing GCN-based social recommendation methods often directly apply GCN to capture user-item and user-user interactions, which probably have two main limitations: (a) Due to the power-law property of the degree distribution, the vanilla GCN with static normalized adjacency matrix has limitations in learning node representations, especially for the long-tail nodes; (b) multi-typed social relationships between users that are ubiquitous in the real world are rarely considered. In this article, we propose a novel Bilateral Filtering Heterogeneous Attention Network (BFHAN), which improves long-tail node representations and leverages multi-typed social relationships between user nodes. First, we propose a novel graph convolutional filter for the user-item bipartite network and extend it to the user-user homogeneous network. Further, we theoretically analyze the correlation between the convergence values of different graph convolutional filters and node degrees after stacking multiple layers. Second, we model multi-relational social interactions between users as the multiplex network and further propose a multiplex attention network to capture distinctive inter-layer influences for user representations. Last but not least, the experimental results demonstrate that our proposed method outperforms several state-of-the-art GCN-based methods for social recommendation tasks.

Modeling Global and Local Interactions for Online Conversation Recommendation

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions , represented by topic and discourse word clusters to reflect users’ content and pragmatic preferences, and (2) local interactions , encoding replying relations and chronological order of conversation turns to characterize users’ prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users’ topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.

Enactive Principles for the Ethics of User Interactions on Social Media: How to Overcome Systematic Misunderstandings Through Shared Meaning-Making

This paper proposes three principles for the ethical design of online social environments aiming to minimise the unintended harms caused by users while interacting online, specifically by enhancing the users’ awareness of the moral load of their interactions. Such principles would need to account for the strong mediation of the digital environment and the particular nature of user interactions: disembodied, asynchronous, and ambiguous intent about the target audience. I argue that, by contrast to face to face interactions, additional factors make it more difficult for users to exercise moral sensitivity in an online environment. An ethics for social media user interactions is ultimately an ethics of human relations mediated by a particular environment; hence I look towards an enactive inspired ethics in formulating principles for human interactions online to enhance or at least do not hinder a user’s moral sensitivity. This enactive take on social media ethics supplements classical moral frameworks by asking us to focus on the relations established through the interactions and the environment created by those interactions.

Evaluating a Social Media Campaign for a Parent Educational Video on Bronchiolitis

Bronchiolitis, or lower airway swelling, is a common cause of pediatric hospital admissions. Parents have expressed wishes for more information regarding bronchiolitis but had difficulty finding reliable information, suggesting the need for more effective and easily accessible information resources. Knowledge translation (KT) tools like videos provide research-based information and may be conveniently disseminated to large audiences through social media. The purpose of this project was to evaluate the effectiveness of a social media campaign to promote a video on bronchiolitis. A social media campaign was conducted from 14 October to 30 November 2019. User interactions were recorded for the Facebook and Twitter accounts, website, and YouTube of Evidence in Child Health to Enhance Outcomes (ECHO), Alberta Research Centre for Health Evidence (ARCHE), and Translating Emergency Knowledge for Kids (TREKK). Baseline metrics were collected from 1 August to 30 September 2019 and post-campaign metrics were collected from 1 December 2019 to 31 March 2020. Mean monthly changes, standard deviations, and percent changes between periods were generated for the baseline, campaign, and post-campaign periods. Overall, there was a visible increase in user interactions throughout the campaign period. There was an overall downward trend in user interactions following the campaign. These findings suggest that social media may be a useful method of KT tool dissemination when consistently used. The downward trend post-campaign highlights the need for further research to investigate methods to maintain continuous interaction following a campaign.

Digital Transformation and the Evolution of the Platform Economy

This chapter provides an overview of the evolution of the platform economy through the lens of digital transformation and transit from Industry 3.0 (I3.0) to Industry 4.0 (I4.0). The platform economy belongs to both I3.0 and I4.0 and goes through two cycles of digital transformation within them. In I3.0, the starting point of the platform economy is the digitization of social and commercial interactions over user-generated content. The resulting issues of trust and regulation of user interactions find solutions in new business models based on online reputation systems and algorithmic regulation. The specificity of I4.0 is the tendency to platform products, homes, factories, and cities through broad digitization of interactions between humans and things, and things and things. For the platform economy, the new cycle of digital transformation in the context of I4.0 means creating business models based on the ultimate customization of both the production and consumption of product-as-platforms and the rental of digital product models.

Understanding the Impact of COVID-19 on Online Mental Health Forums

Like many of the disasters that have preceded it, the COVID-19 pandemic is likely to have a profound impact on people’s mental health. Understanding its impact can inform strategies for mitigating negative consequences. This work seeks to better understand the impacts of COVID-19 on mental health by examining how discussions on mental health subreddits have changed in the three months following the WHO’s declaration of a global pandemic. First, the rate at which the pandemic is discussed in each community is quantified. Then, volume of activity is measured to determine whether the number of people with mental health concerns has risen, and user interactions are analyzed to determine how they have changed during the pandemic. Finally, the content of the discussions is analyzed. Each of these metrics is considered with respect to a set of control subreddits to better understand if the changes present are specific to mental health subreddits or are representative of Reddit as a whole. There are numerous changes in the three mental health subreddits that we consider, r/Anxiety, r/depression, r/SuicideWatch; there is reduced posting activity in most cases, and there are significant changes in discussion of some topics such as work and anxiety. The results suggest that there is not an overwhelming increase in online mental health support-seeking on Reddit during the pandemic, but that discussion content related to mental health has changed.

Fundamentals of a Novel Debugging Mechanism for Orchestrated Cloud Infrastructures with Macrosteps and Active Control

In Infrastructure-as-a-Service (IaaS) clouds, the development process of a ready-to-use and reliable infrastructure might be a complex task due to the interconnected and dependent services that are deployed (and operated later on) in a concurrent way on virtual machines. Different timing conditions may change the overall initialisation method, which can lead to abnormal behaviour or failure in the non-deterministic environment. The overall motivation of our research is to improve the reliability of cloud-based infrastructures with minimal user interactions and significantly accelerate the time-consuming debugging process. This paper focuses on the behaviour of cloud-based infrastructures during their deployment phase and introduces the adaption of a replay, and active control enriched debugging technique, called macrostep, in the field of cloud orchestration in order to provide support for developers troubleshooting deployment-related errors. The fundamental macrostep mechanisms, including the generation of collective breakpoint sets as well as the traversal method for such consistent global states, have been combined with the Occopus cloud orchestrator and the Neo4J graph database. The paper describes the novel approach, the design choices as well as the implementation of the experimental debugger tool with a use case for validation purposes by providing some preliminary numerical results.

Agility in Interior Architecture: An investigation through Prototypical Design for Interactive Spatial Dynamics

<p>This paper identifies and discusses designing interior building dynamics that, through user interaction, can be physically manipulated and maneuvered to suit a changing situation in spatial requirements/preferences. Designers have partially realised this architectural vision through both mobile and dynamic interior elements, and relocatable construction systems. Here lies the potential for a digitally manufactured modular system for spatial dynamics, providing interactive interior architecture with embedded spatial fluidity. Providing occupants of these interior spaces with the capacity to determine the spatial conditions how and when they require. Leveraging modern digital fabrication techniques like CNC timber milling and consideration of factors such as assembly/disassembly, this thesis explores ideas of tactility and kinetics of interior space and how the user interactions can exact spatial change. This research develops a modular tectonic language, with low operational - mechanical and construction - complexity. A manipulatable interior tectonic such as this would be possible to complement existing structures or other fixed designed architectural elements to provide an enhanced level of building function through a immediately influenceable spatial conditions. The research undertaken explores a series of experimental modular prototypes, each a unique response for spatial dynamics.</p>

Agility in Interior Architecture: An investigation through Prototypical Design for Interactive Spatial Dynamics

<p>This paper identifies and discusses designing interior building dynamics that, through user interaction, can be physically manipulated and maneuvered to suit a changing situation in spatial requirements/preferences. Designers have partially realised this architectural vision through both mobile and dynamic interior elements, and relocatable construction systems. Here lies the potential for a digitally manufactured modular system for spatial dynamics, providing interactive interior architecture with embedded spatial fluidity. Providing occupants of these interior spaces with the capacity to determine the spatial conditions how and when they require. Leveraging modern digital fabrication techniques like CNC timber milling and consideration of factors such as assembly/disassembly, this thesis explores ideas of tactility and kinetics of interior space and how the user interactions can exact spatial change. This research develops a modular tectonic language, with low operational - mechanical and construction - complexity. A manipulatable interior tectonic such as this would be possible to complement existing structures or other fixed designed architectural elements to provide an enhanced level of building function through a immediately influenceable spatial conditions. The research undertaken explores a series of experimental modular prototypes, each a unique response for spatial dynamics.</p>