Towards WebSub 2.0

Social Web Incubator Community Group,

Hello. I am pleased to share some ideas with this Community Group about next-gen PubSub and PubSubHubbub topics. I am hoping to catalyze some brainstorming and discussion towards envisioning WebSub 2.0.
A motivating use case involves that, instead of or in addition to journalists and government watchdog organizations’ personnel having to search for and scour through troves of continuously arriving public-sector documents and datasets, competing services powered by artificial-intelligence technologies could process these and intelligently route these to them.

Artificial Intelligence and Information Retrieval
Examples of “pull-based” information retrieval technologies include search engines and question-answering systems.
Examples of “push-based” information retrieval technologies include change detection and notification systems, intelligent content-routing systems, and recommender systems.
Examples of “social” information retrieval technologies include instant messaging applications, team and collaboration software, social-media platforms, and other communication channels. People can share discovered content with one another via these technologies.

Intelligent Content-routing Systems
Artificial-intelligence technologies, e.g., foundation models, could be of use for intelligently routing content to interested users or groups of users based upon their models, stereotypes, roles, or responsibilities.
Explainable intelligent content-routing system nodes could utilize foundation models to answer natural-language questions, potentially executing dialogical workflows, about multimedia content. Such questions might resemble: “Would a journalist interested in climate, the environment, or environmental policy find this document to be interesting? Why?”
Focusing on the "why" portion of that example question, explanations for routing and recommending content could accompany items through intelligent content-routing systems in the form of item metadata. Such metadata could be utilized by subsequent content-routing system nodes, by users’ virtual-assistant agents, and returned when providing detailed feedback to information-retrieval services.

Machine Learning
Users could express their dynamic interests to their virtual-assistant agents and their push-based information-retrieval services, e.g., in receiving more content like items discovered through either pull-based, push-based, or social information-retrieval technologies.
Explicit and implicit user feedback with respect to routed and recommended content, thumbs up or thumbs down buttons or otherwise detecting usage, can enhance recommender systems and intelligent content-routing systems.
Users’ feedback options could be beyond thumbs-up and thumbs-down signals, potentially including input forms and/or natural-language comments.

Human-computer Interaction
With respect to user interfaces and user experiences, pertinent technologies to consider include syndication feeds, computer-generated digests, and agentic conversational user interfaces.
Artificial-intelligence technologies could collate, organize, and prioritize incoming items into computer-generated digests resembling newsletters or magazines.
Users’ virtual-assistant agents could, in a configurable manner, perform attention-management on their behalf – coordinating with their natural-language-described tasks and determining whether and when to interrupt these tasks with alerts or notifications about routed and recommended content.

Dashboards for Analyzing, Evaluating, and Comparing Services
Users could be provided with unified dashboards to view their feedback histories and other usage analytics with respect to multiple competing push-based information-retrieval services.
This would be useful for customers wanting to conduct cost-benefit analyses with respect to their multiple competing service subscriptions.

Use Cases
The intersections of open government, civic technology, artificial intelligence, and information retrieval present many exciting opportunities for innovation. For instance, instead of or in addition to journalists and government-watchdog organizations’ personnel having to search for and scour through troves of continuously arriving public-sector documents and datasets, competing services powered by artificial-intelligence technologies could process these and intelligently route these to them.
Envisioned are systems which would spider and/or receive pings about new public-sector documents and datasets and use artificial-intelligence technologies to process these documents and datasets to intelligently route them to interested independent journalists, journalism organizations, nonprofits, government watchdog organizations, or any others intending to gather, analyze, and disseminate public-sector content and data to their audiences.
There are also other interesting and important use cases for these technologies to consider and discuss.

Conclusion
Existing and new standards and recommendations could be of use including with respect to: (1) combinable streams of extensible content items from multiple push-based information-retrieval services (RSS, Atom), (2) metadata accompanying items routed and recommended through intelligent content-routing systems, (3) enabling varieties of feedback from users to their multiple competing information-retrieval services, and (4) enabling users to collect their telemetry, logging, and other statistics to enable unified usage analytics dashboards to analyze, evaluate, and compare multiple competing information-retrieval services and subscriptions, (5) WebSub 2.0.


Best regards,
Adam Sobieski
http://www.phoster.com