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Climate Change Discourse on TikTok
No full textDataset of TikTok climate change discussions, referred to the submission "Climate Change Discourse on TikTok" to the "Understudied Networks in Computational Social Science" satellite at NetSci 2025
Agda files for LMCS paper "What monads can and cannot do with a few extra pages"
No full textThese agda files accompany the paper "What monads can and cannot do with a few extra pages", by Rasmus Møgelberg and Maaike Zwart. In the agda files, claims about existing distributive laws and algebraic combinations of the delay monad with the reader, writer, state, and selection monad are proven
Practical Insertion-Only Convex Hull
No full textCode for the Paper > Practical Insertion-Only Convex Hull by Ivor van der Hoog, Henrik Reinstädtler and Eva Rotenberg to be published in ALENEX'26. This zip archive contains the vendored version of the code, being fully offline compilable. The most up-to-date code for this project can be found at github.com/henrixapp/PracticalConvexHulles
Understanding complex casual leisure information needs: an analysis of search requests for books, games, movies and music.
No full textIn this paper, we introduce the CRISPS (CRoss-domaIn relevance aSPects Scheme) coding scheme for complex information needs in the four leisure domains of books, games, movies and music. It categorizes the relevance aspects people consider when searching for these resources. The coding scheme and findings help search engines to better support complex information needs, both by prioritizing which aspects are easier to classify automatically and by determining which information sources should be considered.A cross-domain classification scheme for relevance aspects and information needs in casual leisure domains (CRISPS) is developed and applied. The paper provides the documentation of the scheme development and annotation process as well as a detailed, large-scale analysis of 2000 requests (500 per domain) and relevance aspects for four domains as expressed in complex search requests in everyday life information seeking posted to online forums.We identify and discuss relevance aspect frequencies, information need types and the described search process of the requests. Furthermore, the coding scheme development and the annotation process are documented and reflected on.This is the first categorization and analysis of complex information needs in these four leisure domains combined. The coding scheme and findings can be used to develop new types of search interfaces that incorporate the kinds of relevance aspects identified in the scheme, allowing to express complex needs in the form of structured queries
From Sound to Sight: Towards AI-authored Music Videos
No full textConventional music visualisation systems rely on handcrafted ad hoc transformations of shapes and colours that offer only limited expressiveness. We propose two novel pipelines for automatically generating music videos from any user-specified, vocal or instrumental song using off-the-shelf deep learning models. Inspired by the manual workflows of music video producers, we experiment on how well latent feature-based techniques can analyse audio to detect musical qualities, such as emotional cues and instrumental patterns, and distil them into textual scene descriptions using a language model. Next, we employ a generative model to produce the corresponding video clips. To assess the generated videos, we identify several critical aspects and design and conduct a preliminary user evaluation that demonstrates storytelling potential, visual coherency and emotional alignment with the music. Our findings underscore the potential of latent feature techniques and deep generative models to expand music visualisation beyond traditional approaches
Overview of the SISAP 2025 Indexing Challenge.
Full text linkThis paper summarizes the innovative solutions presented at the third edition of the SISAP Indexing Challenge held at SISAP 2025.The challenge featured two distinct tasks involving vector embeddings derived from a large corpus using neural encoders. It proposed the following two tasks under strict memory and computational constraints:– Task 1: Approximate nearest neighbor search achieving an averagerecall of at least 0.7 for 30-NN, using out-of-distribution objects asqueries.– Task 2: k-NN (k = 15) graph construction for large datasets, requiring an average recall of at least 0.8.Both tasks required solutions to operate within strict resource limits: 16 GB of RAM, 8 virtual CPUs, and a 12-hour wall-clock time for the end-to-end pipeline (including data loading, pre-processing, indexing, and searching). Each task imposes different minimum quality requirements and ranking specifications. Participants developed strategies such as data compression, optimized indexing, and efficient search algorithms to meet these constraints. This paper details the challenge design, explains the evaluation framework, and provides an overview of the submitted solutions
Leveraging VLLMs for Visual Clustering: Image-to-Text Mapping Shows Increased Semantic Capabilities and Interpretability
No full textAs visual content becomes increasingly prominent on social media, automated image categorization is vital for computational social science efforts to identify emerging visual themes and narratives in online debates. However, the methods based on convolutional neural networks (CNNs) currently used in the field are unable to fully capture the connotative meaning of images, and struggle to produce easily interpretable clusters. In response to these challenges, we test an approach that leverages the ability of Vision-and-Large-Language-Models (VLLMs) to generate image descriptions that incorporate connotative interpretations of the input images. In particular, we use a VLLM to generate connotative textual descriptions of a set of images related to climate debate, and cluster the images based on these textual descriptions. In parallel, we cluster the same images using a more traditional approach based on CNNs. In doing so, we compare the connotative semantic validity of clusters generated using VLLMs with those produced using CNNs, and assess their interpretability. The results show that the approach based on VLLMs greatly improves the quality score for connotative clustering. Moreover, VLLM-based approaches, leveraging textual information as a step towards clustering, offer a high level of interpretability of the results
“A Network of Mutualities of Being”: Socio-material Archaeological Networks and Biological Ties at Çatalhöyük
Full text linkRecent advances in archaeogenomics have granted access to previously unavailable biological information with the potential to further our understanding of past social dynamics at a range of scales. However, to properly integrate these data within archaeological narratives, new methodological and theoretical tools are required. Effort must be put into finding new methods for weaving together different datasets where material culture and archaeogenomic data are both constitutive elements. This is true on a small scale, when we study relationships at the individual level, and at a larger scale when we deal with social and population dynamics. Specifically, in the study of kinship systems it is essential to contextualize and make sense of biological relatedness through social relations, which, in archaeology, is achieved by using material culture as a proxy. In this paper we propose a Network Science framework to integrate archaeogenomic data and material culture at an intrasite scale to study biological relatedness and social organization at the Neolithic site of Çatalhöyük. Methodologically, we propose the use of network variance to investigate the concentration of biological relatedness and material culture within networks of houses. This approach allowed us to observe how material culture similarity between buildings gives valuable information on potential biological relationships between individuals and how biogenetic ties concentrate at specific localities on site