Muhammad Taimoor Khan

As computational methods become increasingly central to social science research, there is a growing demand for research software infrastructures that are sustainable, grounded in principles of open science, and embedded in the community. In response, we are introducing the Methods Hub as an open-source, community-driven collection of computational tools designed to support social science research in solving complex data-related tasks. The Methods Hub integrates three complementary components: First, quality-curated content, including computational methods and accompanying tutorials, which are specifically presented from a social science research perspective. Second, the methods and tutorials are hosted on a web portal enabling researchers to discover, apply, learn, and publish computational methods. Third, interactive, browser-based execution backends allow users to directly run, test, and adapt the code of methods and tutorials. These components are guided by a set of core principles: a long-term infrastructure perspective, reproducibility requirements, and a focus on community. The Methods Hub builds a long-term infrastructure that extends beyond the scope of single projects. Further, reproducibility functions as a quality assurance measure while advancing the practices of open science. Finally, the Methods Hub is built on public, community-based submissions by treating research software as citable academic output and providing visibility, accessibility, and formal recognition. We are also broadening the community by fostering learning and ease of access. Looking ahead, the Methods Hub is exploring the potential of AI as both a research tool and to further strengthen the infrastructure, while maintaining transparency, reproducibility, and quality standards for computational social science research.

Topic models extract latent concepts from texts in the form of topics. Lifelong topic models extend topic models by learning topics continuously based on accumulated knowledge from the past which is updated continuously as new information becomes available. Hierarchical topic modeling extends topic modeling by extracting topics and organizing them into a hierarchical structure. In this study, we combine the two and introduce hierarchical lifelong topic models. Hierarchical lifelong topic models not only allow to examine the topics at different levels of granularity but also allows to continuously adjust the granularity of the topics as more information becomes available. A fundamental issue in hierarchical lifelong topic modeling is the extraction of rules that are used to preserve the hierarchical structural information among the rules and will continuously update based on new information. To address this issue, we introduce a network communities based rule mining approach for hierarchical lifelong topic models (NHLTM). The proposed approach extracts hierarchical structural information among the rules by representing textual documents as graphs and analyzing the underlying communities in the graph. Experimental results indicate improvement of the hierarchical topic structures in terms of topic coherence that increases from general to specific topics.

Recurrent behavior is common in web navigation and users find many problems in re-accessing the useful resources and relevant content. A variety of approaches are being introduced to support in re-finding through various features. Among all, back and forward buttons are specific to the current session, while history and browser search is generic to all the links visited throughout. Contextual tabs need a keyword to suggest options of possible pages. Furthermore, implicit bookmarks need less human intervention and empower revisitation through intelligent automatic techniques. Popular implicit bookmarks in browsers are New-Tab speed dials. These thumbnails are few in number, less attractive, usually not ranked as per user interests. They don't provide true support for the fluctuating activity of a user at different times of the day. This paper represents some issues and changeless in existing methods. Moreover, this paper proposed a novel model for making these New-Tab speed dials thumbnails more useful to use. We design a new ranking formula for the thumbnails including temporal and visual context to the layout. Results show that the proposed model improves user satisfaction, relevance to the resources for revisitation.