The Web Conference 2026 (Dubai, April 13-17): Registration deadline is March 10, but organizers won't decide if it's in-person or virtual until March 15.
How do you plan travel, pick a registration tier, or get institutional approval without knowing the format?
Our paper in the ACM Web Conference, on Inequality and Concentration of Power in Online Governance
We built and studied a census of the #DAOs ecosystem: 30K blockchain communities, 200K proposals, 22M votes
https://doi.org/10.1145/3589335.3651481
The paper is open access and open licensed, same as the dataset at
https://zenodo.org/records/10794916
The work was led by PhD student Andrea Peña, with Javier Arroyo, Andrew Schwartz, and me.
Starting a thread with the main highlights
#web3 #TheWebConf
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Just finished the presentation of my #TheWebConf History of the Web track paper on "Toward Making Opaque Web Content More Accessible: Accessibility From Adobe Flash to Canvas-Rendered Apps":
📄 Paper: https://goo.gle/opaque-web-content-paper
🖼️ Slides: https://goo.gle/opaque-web-content-slides
Congratulations to Lukas Blübaum and Stefan Heindorf for the acceptance of your paper "Causal Question Answering with Reinforcement Learning" at The Web Conference #TheWebConf 👏🥳
➡️Take a look at the paper here: https://arxiv.org/abs/2311.02760
Causal questions inquire about causal relationships between different events or phenomena. They are important for a variety of use cases, including virtual assistants and search engines. However, many current approaches to causal question answering cannot provide explanations or evidence for their answers. Hence, in this paper, we aim to answer causal questions with a causality graph, a large-scale dataset of causal relations between noun phrases along with the relations' provenance data. Inspired by recent, successful applications of reinforcement learning to knowledge graph tasks, such as link prediction and fact-checking, we explore the application of reinforcement learning on a causality graph for causal question answering. We introduce an Actor-Critic-based agent which learns to search through the graph to answer causal questions. We bootstrap the agent with a supervised learning procedure to deal with large action spaces and sparse rewards. Our evaluation shows that the agent successfully prunes the search space to answer binary causal questions by visiting less than 30 nodes per question compared to over 3,000 nodes by a naive breadth-first search. Our ablation study indicates that our supervised learning strategy provides a strong foundation upon which our reinforcement learning agent improves. The paths returned by our agent explain the mechanisms by which a cause produces an effect. Moreover, for each edge on a path, our causality graph provides its original source allowing for easy verification of paths.
#TheWebConf
ist die Flagship-Konferenz der WWW-Community. Unsere BSI-Kollegen präsentieren dieses Jahr in Austin (Texas, USA) ein Tool, mit dem zukünftig u.a. Ad-Malware-Kampagnen detektiert werden könnten: ➡https://dl.acm.org/doi/10.1145/3543873.3587351
Happy to have presented our research work "Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction" ( ✍ w/ @starquake and @andreatagarelli) at TheWebConf 2023 in Austin, Texas 🤠
Full paper 👉 https://dl.acm.org/doi/abs/10.1145/3543507.3583520
#TheWebConf #NFT #NonFungibleTokens #Blockchain #Web3 #Metaverse #Transformers #AI #ComputerVision #NLP #DeepLearning #Research #Finance
Alexander Loth
ACMSIGWEB
Thomas Steiner 
Samer Hassan
DICE
BSI
Lucio La Cava