Accepted Workshops
Important Dates
- Paper submission:Mid December - Early January (please check the workshop websites for specific deadlines)
- Paper notification:January 13, 2026
- Camera ready: February 2, 2026
All submission deadlines are end-of-day in Anywhere on Earth (AoE) time zone.
Description: As in previous years, the objective of this workshop is to provide a venue for researchers of all domains (IE/IR, Web mining, etc.) where the temporal dimension opens an entirely new range of challenges and possibilities. The workshop's ambition is to keep shaping a community of interest on the research challenges and possibilities resulting from the introduction of the time dimension in Web analysis. The maturity of the Web, the emergence of large-scale repositories of Web material, makes this very timely and a growing number of research projects and services are emerging that have this focus in common. Having a dedicated workshop will help, we believe, to take a rich and cross-domain approach to this continuous research challenge with a strong focus on the temporal dimension. TempWeb focuses on investigating infrastructures, scalable methods, and innovative software for aggregating, querying, and analyzing heterogeneous data at Internet scale. Emphasis will be given to temporal data analysis along the time dimension for Web data that has been collected over extended time periods. A major challenge in this regard is the sheer size of the data it exposes and the ability to make sense of it in a useful and meaningful manner for its users. It is worth noting that this trend of using big data to make inferences is not specific to Web content analytics. A now-common strategy in post-genomic biology is to measure, quantitatively, the action of all (or as many as possible) of the genes at the level of the transcriptome, proteome, metabolome and phenotype, and to use computerized methods to infer gene function via various kinds of pattern recognition techniques. On the Web, to a large extent, we have also reached this point. Web scale data analytics therefore needs to develop infrastructures and extended analytical tools to make sense of these.
Organizers: Marc Spaniol (University of Caen Normandy Caen), France Omar Alonso (Amazon Santa Clara, CA, USA), Ricardo Baeza-Yates (KTH, Royal Institute of Technology Stockholm, Sweden)
Website: https://temporalweb.net/
Description: The rise of foundation models (FMs) amplifies the importance and relevance of federated learning (FL) as a crucial research direction. With FMs becoming the norm in machine learning development, the focus shifts from model architecture design to tackling the issues surrounding privacy-preserving and distributed learning. Advancements in FL methods have the potential to unlock the use of FMs, enabling efficient and scalable training while safeguarding sensitive data. With this in mind, we invite original research contributions, position papers, and work-in-progress reports on various aspects of federated learning in the era of foundation models. Since the emergence of foundation models has been a relatively recent phenomenon, their full impact on federated learning has not yet been well explored or understood. We hope to provide a platform to facilitate interaction among students, scholars, and industry professionals from around the world to discuss the latest advancements, share insights, and identify future directions in this exciting field.
Organizers: Irwin King (The Chinese University of Hong Kong), Guodong Long (University of Technology Sydney), Zenglin Xu (Fudan University), Han Yu (Nanyang Technological University), Xiaoli Tang (Nanyang Technological University), Liping Yi (Tianjin University)
Description: TIME brings together domain experts and research students to share insights, practical guidance, and evaluations on key topics, including social network analysis, graph algorithms, web mining, semantics and knowledge, security, privacy, fairness, and ethics on the web. We invite survey, evaluation, or review papers that critically analyze models and datasets from diverse perspectives. These papers serve as essential resources by (i) providing quick reference guides for researchers and practitioners, (ii) enhancing accessibility for newcomers, and (iii) distilling key insights into actionable knowledge. Complementing these contributions, invited talks from experts and industry leaders will offer practical perspectives, fostering cross-domain collaboration in web technologies. Through thought-provoking discussions and networking opportunities, the workshop bridges research and real-world applications, setting a new standard for interdisciplinary exchange in the field.
Organizers: Dr. Lei Wang (Griffith University & Data61/CSIRO), Dr. Md Zakir Hossain (Curtin University), Prof. Tom Gedeon (Curtin University), Dr. Syed Mohammed Shamsul Islam (ECU), Prof. Rafiqul Islam (Charles Sturt University), Dr. Yasmeen George (Monash University), Dr. Shreya Ghosh (The University of Queensland)
Website: https://time.griffith.edu.au/workshop/time2026/
Link to submission site: https://sites.google.com/view/aiofai-2026/home
Description: TThe emerging Web era of Artificial Intelligence (AI) presents a paradox: the same innovations that threaten security and truth also offer unprecedented solutions. AI technologies are becoming ever more interwoven in the fabric of online systems, presenting potential that can be used both for constructive and destructive purposes. In the post-truth era, generating persuasive yet deceptive content and automating social engineering attacks and the spread of disinformation has never been easier since the conception of the World Wide Web. At the same time, there exist exceptional opportunities for innovation and creating powerful tools to mitigate these threats, thanks to intrusion detection, threat modeling, and context-aware access control, etc. The AiOfAi workshop, which has had three prior editions at the International Joint Conference on Artificial Intelligence (IJCAI), aims to highlight the double-edged nature of AI in the digital age, examining how it can be exploited to undermine trust, privacy, and integrity, while also serving as a foundation for more secure, ethical, and resilient digital ecosystems. We will discuss the societal impact of widespread adoption of AI tools, especially with the advent of Generative AI and its consequences, ranging from the erosion of public trust to the blurring privacy lines. AiOfAi will also address the ethical and legal frameworks needed to guide responsible AI deployment, which embodies fairness, transparent decision-making, and privacy preservation. We aim to bring together researchers, practitioners, and enthusiasts interested in AI, cybersecurity, ethics, law, and human computer interaction to discuss methodologies, case studies, and tools that address the complex tradeoffs between AI capabilities and vulnerabilities. We welcome original contributions that present innovative ideas, proof of concepts, and use cases to tackle the challenges of the AI-powered Web.
Organizers: Esma Aïmeur (Université de Montréal), Rim Ben Salem (Polytechnique Montréal), Dorsaf Sallami (Université de Montréal), Julita Vassileva (University of Saskatchewan), Nora Boulahia-Cuppens (Polytechnique Montréal)
Website: https://sites.google.com/view/aiofai-2026/home
Link to submission site: https://easychair.org/conferences?conf=aiofai2026
Description: Expressing opinions and interacting with others on the Web has led to the production of an abundance of online discourse data, such as claims and viewpoints on controversial topics, their sources and contexts (events, entities). This data constitutes a valuable source of insights for studies into misinformation spread, bias reinforcement, echo chambers or political agenda setting. Computational methods, mostly from the field of NLP, have emerged that tackle a wide range of tasks in this context, including argument and opinion mining, claim detection, checkworthiness detection, stance detection or fact verification. However, computational models require robust definitions of classes and concepts under investigation. Thus, these computational tasks require a strong interdisciplinary and epistemological foundation, specifically with respect to the underlying definitions of key concepts such as claims, arguments, stances, check-worthiness or veracity. This requires a highly interdisciplinary approach combining expertise from fields such as communication studies, computational linguistics and computer science. As opposed to facts, claims are inherently more complex. Their interpretation strongly depends on the context and a variety of intentional or unintended meanings, where terminology and conceptual understandings strongly diverge across communities. From a computational perspective, in order to address this complexity, the synergy of multiple approaches, coming both from symbolic (knowledge representation) and statistical AI seem to be promising to tackle such challenges. This workshop aims at strengthening the relations between these communities, providing a forum for shared works on the modeling, extraction and analysis of discourse on the Web. It will address the need for a shared understanding and structured knowledge about discourse data in order to enable machine-interpretation, discoverability and reuse, in support of scientific or journalistic studies into the analysis of societal debates on the Web. Beyond research into information and knowledge extraction, data consolidation and modeling for knowledge graphs building, the workshop targets communities focusing on the analysis of online discourse, relying on methods from machine learning, natural language processing, large language models and Web data mining.
Organizers: Stefan Dietze (Heinrich-Heine-University Düsseldorf & GESIS, Germany), stefan.dietze[[AT]]gesis.org, Dimitar Dimitrov (GESIS, Germany), dimitar.dimitrov[[AT]]gesis.org, Pavlos Fafalios (Technical University of Crete & FORTH-ICS, Greece), pfafalios[[AT]]tuc.gr, Konstantin Todorov (University of Montpellier / LIRMM / CNRS, France), todorov[[AT]]lirmm.fr
Description: The exponential increase in scientific, technical, and legal data available on the Web, including research articles, patents, standards, and technical reports, has made their large-scale semantic processing, interlinking, and knowledge extraction a central challenge for the Web community. These data sources are heterogeneous, semi-structured, and domain-specific, containing complex elements such as text, tables, equations, and diagrams that make traditional data integration and analysis difficult. Yet, they hold immense potential for advancing knowledge discovery, open science, and evidence-based innovation. As the Web evolves into a vast ecosystem of human- and machine-generated content, there is a growing need to develop scalable AI models and semantic interoperable representations that transform this fragmented information into interconnected, machine-interpretable knowledge. In this context, the SemTech 2026 workshop focuses on methods that combine Semantic Web technologies, Natural Language Processing, Large Language Models (LLMs), and other AI technologies to model knowledge across scientific, technical, and legal domains. The workshop invites research on knowledge graph creation, semantic annotation, LLM–KG hybrid reasoning, and trustworthy AI pipelines that enhance the reliability, interpretability, and reuse of Web data. This is particularly timely as the Web community seeks robust approaches to integrate symbolic and sub-symbolic methods for managing and understanding the growing body of domain-specific knowledge on the Web.
Organizers: Rima Dessi' (Higher College of Technologies, United Arab Emirates), Jeenu Joy (FIZ-Karlsruhe, Germany), Danilo Dessi' (University of Sharjah, United Arab Emirates), Francesco Osborne (Knowledge Media Institute, The Open University, United Kingdom), Hidir Aras (FIZ-Karlsruhe, Germany)
Website: https://semtech4stld.github.io/
Description: The Agentic Web is emerging as billions of AI agents discover, communicate, and coordinate across the open Web, marking a shift toward systems that no longer operate as isolated models but as Web-integrated entities. This workshop unifies architectures and standards with the foundations of interoperable ecosystems, convening researchers and practitioners to chart a shared agenda. We focus on web-native building blocks: agent registries and resolution, identity and credentials (DIDs/VCs), authorization (OAuth 2.0), discovery (DNS-SD), and federation patterns (e.g., ActivityPub), and their interfaces with application-level protocols (A2A, MCP, OpenAPI/REST/GraphQL/gRPC) and corresponding architectural models. Beyond wiring, we examine economic mechanisms (reputation under adversarial conditions, knowledge pricing, transaction protocols) and societal coordination (governance, accountability, evaluation at population scale). Topics include capability representation and matching, cross-protocol bridges, privacy and provenance, workflow orchestration, and reliable tool use over heterogeneous data and services spanning cloud, edge/IoT, and enterprise environments, as well as emerging patterns that let AI systems operate across distributed Web environments. These directions align with the emerging need to support function calling, tool use, and multi-agent coordination over heterogeneous Web resources, leveraging open and lightweight protocols to interact securely with distributed data and services. The program blends keynote, invited talks, papers, and a panel to surface design principles, pitfalls, and testbed practices. By aligning standards, infrastructure, and incentives, the workshop seeks to ensure the Agentic Web remains open, trustworthy, and sustainable.
Organizers: Abderrahmane Maaradji (University of Doha for Science and Technology, Qatar), Abul Ehtesham (Kent State University, USA), Aditi Singh (Cleveland State University, USA), Boualem Benatallah (Dublin City University, Ireland), Fatma Outay (Zayed University, UAE), Luca Muscariello (Cisco Systems, France), Pradyumna Chari (MIT Media Lab, USA), Ramesh Raskar (MIT Media Lab, USA), Sabrina Senatore (University of Salerno, Italy), Yacine Sam (University of Tours, France)
Website: https://faaw.univ-tours.fr/
Description: The rapid proliferation of digital technologies and the ascent of large language models (LLMs) and agent-based AI systems have fundamentally altered the information landscape, creating a dual-use paradigm where advanced tools facilitate both the detection and the sophisticated generation of harmful content. While these technologies offer significant benefits for automated fact-checking and claim verification, they simultaneously enable the scalable production of deepfakes, personalized propaganda, and multimodal misinformation across diverse languages and platforms. Current research efforts remain largely fragmented often restricted to specific platforms, monolingual contexts, or isolated issues such as hate speech or cyber-bullying, rendering traditional counter-measures like blocking or down-ranking increasingly ineffective against actors who seamlessly switch platforms to maintain reach. To address these evolving complexities, it is no longer sufficient to rely on historical assumptions regarding content propagation; rather, the community must adopt interdisciplinary frameworks that integrate novel agentic architectures for generation, retrieval, verification, and explanation. Our DHOW-MiLLA workshop aims to consolidate these disparate research directions under one umbrella, bridging the gap between academia and industry to develop robust, transparent, and adaptive systems for a safer Internet. By focusing on critical contemporary issues, including the impact of AI-generated content on elections and geopolitical conflicts, the workshop seeks to mobilize a cohesive research community. Through rich dialogue and knowledge exchange, this initiative strives to reorient the research agenda, fostering the development of cross-platform, multilingual solutions that not only mitigate emerging risks but actively harness AI capabilities to counteract the multifaceted nature of modern misinformation.
Organizers: Thomas Mandl (University of Hildesheim, Germany), Haiming Liu (University of Southampton, UK), Gautam Kishore Shahi (University of Duisburg-Essen, Germany), Amit Kumar Jaiswal (Indian Institute of Technology (BHU) Varanasi, India), Durgesh Nandini (University of Bayreuth, Germany), Luis-Daniel Ibáñez (University of Southampton, UK), Dr. Junichi Suga (Fujitsu Research Japan), Dr. Dai Yamamoto (Fujitsu Research Japan), Dr. Rahul Mishra (Fujitsu Research India), Dr. Rajakrishnan P. Rajkumar (IIIT Hyderabad, India), Dr. Sagar Uprety (University College London (UCL), UK), Dr. Sujit Kumar (Nanyang Technological University (NTU), Singapore), Dr. Bornali Phukon (University of Illinois Urbana-Champaign (UIUC), USA)
Description: Recommendation systems power much of the web economy, influencing how people discover products, content, and services. With the emergence of Large Language Models and autonomous agents, these systems are undergoing a major shift from traditional centralized pipelines to agentic ecosystems that can plan, reason, negotiate, and interact across the entire journey of discovery, personalization, and fulfillment. Large Language Models introduce new capabilities for multimodal reasoning, natural language grounding, and conversational personalization, enabling systems that adapt and respond with greater context and coherence. At the same time, multi agent systems create opportunities for richer collaboration among buyers, sellers, and platforms, while also raising important challenges in interpretability, robustness, governance, and evaluation. Conventional short term metrics such as clicks and conversions are no longer sufficient to measure coordination quality, long horizon impact, trust, or fairness across stakeholders. This workshop brings together researchers and practitioners in recommendation systems, multi agent learning, information retrieval, and mechanism design to explore principles for transparent, scalable, and responsible agent driven personalization. Through contributions on architectures, evaluation, trust, fairness, and real world deployments, the workshop aims to shape the next generation of adaptive, explainable, and societally aligned recommendation ecosystems.
Organizers: Keerthi Gopalakrishnan (Walmart Global Tech, USA), Qi Xu (Meta AI, USA), Aysenur Inan (Walmart Global Tech, USA), Zhigang Hua (Meta AI, USA), Shuang Yang (Meta AI, USA), Luyi Ma (Walmart Global Tech, USA)
Description: ZABAPAD (Zero-knowledge proof And Blockchain for WEB 4.0: Advancing the Post-quantum And Decentralized Era) is a workshop focusing on zero-knowledge technologies, blockchain infrastructure, and post-quantum readiness for the emerging Web 4.0 ecosystem. This half-day, single-track event emphasizes real-world deployments, empirical measurements, and interoperability across Web and non-Web domains. In particular, ZABAPAD explores the convergence of AIoT and ZKP—redefining identity and trust models beyond SIM in mobile networks, IP in Web 2.0, and NFT in Web 3.0. As AIoT systems evolve toward decentralized, post-quantum infrastructures, ZKP-based authentication and AIoT SIM functionalities are emerging as key enablers of secure, privacy-preserving, and verifiable connectivity among intelligent devices, vehicles, and edge services. This theme extends to ZKML, Layer-2 proving/verification, TEE+ZK integration for verifiable compute, and post-quantum migration of identities, wallets, ledgers, and protocols. Expected outcomes include: (1) a practitioner-oriented adoption playbook, (2) an interoperability and standards checklist, (3) a curated set of reproducible benchmarks and datasets, and (4) a catalog of failure modes and mitigations for domains such as finance, mobility, healthcare, AIoT, public services, supply chain, and AI/ML. ZABAPAD complements the Web Conference and Web 4.0 communities by uniting global researchers and developers to chart actionable, trustworthy pathways toward the post-quantum, decentralized, and intelligent Internet.
Organizers: Shiho Kim (Yonsei University, South Korea), Ho Suk (Yonsei University, South Korea), Roberto Di Pietro (King Abdullah University of Science and Technology, Saudi Arabia), Davor Svetinovic (Khalifa University, United Arab Emirates), KyungHi Chang (Inha University, South Korea), Madhusudan Singh (Pennsylvania State University, USA), Rakesh Shrestha (Research Institutes of Sweden, Sweden)
Website: https://zabapad.github.io
Description: This is the 12th edition of the annual workshop series labeled “WebAndTheCity – The Web and Smart Cities”, which is the successor of the series of workshops that started in Florence in 2015 and has continued taking place every year in conjunction with the WWW conference series. Each year, the focus of the workshop is actualized, and this year, the workshop focuses on data-driven smart cities. In the era of IoT, AI, and agentic AI integration, the citiverse (metaverse for people-centric cities), cities are being transformed into urban environments that use data as a foundational asset to improve decision-making, optimize services, and enhance citizen well-being. At the same time, data is processed using various techniques and methods, which influence the outcomes. This workshop aims to explore how the Web supports this transformation and how technologies can improve smart cities.
Organizers: Leonidas Anthopoulos (Professor, University of Thessaly, Greece); Marijn Janssen (Professor, Delft University of Technology, The Netherlands); Vishanth Weerakkody (Professor, University of Bradford, United Kingdom)
Website: https://webandthecity.home.blog/
Description:
The recent achievements and availability of Large Language Models have paved the road to a new range of applications and use-cases. Pre-trained language models are now being involved at-scale across numerous fields, where they were previously unutilized. More specifically, the recent progress in generative models has paved the way to using them easily through textual instructions aka prompts. Unfortunately, the performances of these models are highly dependent on the exact phrasing used in prompts and therefore practitioners need to adopt fail-retry strategies.
This third international workshop on prompt engineering aims at gathering practitioners (both from Academia and Industry) to exchange about good practices, optimizations, results and novel paradigms about the designing of efficient prompts and context-building to make use of LLMs.
Organizers: Damien Graux (EcoVadis); Sébastien Montella (Huawei Technologies Ltd.); Hajira Jabeen (UniKlinik Cologne)
Website: https://prompteng-ws.github.io/2026/
Description: The recent growth of LLMs has expanded possibilities in data management, enabling powerful natural language access, reasoning, and decision support. However, reliability and trustworthiness remain major challenges when deploying LLMs in sensitive domains. Graph-based representations of knowledge and data (e.g., knowledge graphs and property graphs) provide a promising avenue to address these challenges. LLMs generate fluent responses but often struggle with factuality, bias, hallucinations, and a lack of explainability. Graphs, on the other hand, provide structured, interconnected representations that can serve as grounding and validation layers for LLM-based systems. Exploring the synergies between LLMs and graphs is critical to building data-driven applications where correctness and accountability are necessary.
Organizers: Gianluca Bonifazi (Marche Polytechnic University), Stefano Cirillo (University of Salerno), Eliana Pastor (Polytechnic University of Turin), Luca Virgili (Marche Polytechnic University)
Description:
Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics—accuracy, clicks, and engagement—no longer capture what truly matters to humans. The workshop on Human-Centered Recommender Systems (HCRS) calls for a paradigm shift from optimizing engagement toward designing systems that truly understand, involve, and benefit people.
It brings together researchers in recommender systems, human-computer interaction, AI safety, and social computing to explore how human values such as trust, safety, fairness, transparency, and well-being can be integrated into recommendation processes. Centered around three thematic axes—Human Understanding, Human Involvement, and Human Impact—HCRS features keynotes, panels, and papers covering topics from LLM-based interactive recommenders to societal welfare optimization. By fostering interdisciplinary collaboration, HCRS aims to shape the next decade of responsible and human-aligned recommendation research.
Organizers: Kaike Zhang (University of Chinese Academy of Sciences, China), Jiakai Tang (Renmin University of China, China), Du Su (Institute of Computing Technology, Chinese Academy of Sciences, China), Shuchang Liu (Kuaishou Technology, China), Julian McAuley (University of California, San Diego, USA), Lina Yao (CSIRO Data61, Australia), Qi Cao (Institute of Computing Technology, Chinese Academy of Sciences, China), Yue Feng (University of Birmingham, UK), Fei Sun (University of Chinese Academy of Sciences, China)
Website: https://hcrec.github.io/
Description:
We propose a comprehensive half-day workshop (with at least 8 accepted papers, 3 keynote talks, and over 70 attendees) at WWW 2026, catering to professionals, researchers, and practitioners who are interested in sensing, mining, and understanding big and heterogeneous spatio-temporal data generated from the web (e.g., social media posts, geotagged images, mobility traces) to tackle real-world challenges such as climate change, disaster response, urban planning, and location-based social networks.
The workshop will not only offer a platform for knowledge exchange but also acknowledge outstanding contributions through a distinguished Best Paper Award. Furthermore, we envision the publication of accepted papers in a special issue organized in reputable journals such as ACM Transactions on Big Data and ACM Transactions on Intelligent Systems and Technology.
This workshop would be sponsored by JD Technology, with other committee members from MIT, CMU, UCB, etc. Note that this will be the fifth time that our core members have organized a similar workshop. The previous 14 workshops were hosted with SIGKDD, IJCAI, and WWW, each of which attracted over 70 participants and 30 submissions on average.
Organizers: Yuxuan Liang (The Hong Kong University of Science and Technology (Guangzhou)), Hao Xue (University of New South Wales), Ming Jin (Griffith University), Fei Wang (Institute of Computing Technology, Chinese Academy of Sciences), Qingxiang Liu (The Hong Kong University of Science and Technology (Guangzhou)), Qingsong Wen (Squirrel Ai Learning), Shirui Pan (Griffith University), Flora Salim (University of New South Wales)
Website: https://webst2026.netlify.app/
Description:
The ALTARS 2026 workshop explores recent advances and open challenges in Technology-Assisted Review (TAR) systems and their application to large-scale, high-recall retrieval across the Web. TAR systems, originally designed for domains such as legal discovery and systematic literature review, are now increasingly relevant in Web environments characterized by heterogeneous, dynamic, and multilingual information. As the Web continues to expand with both user-generated and AI-produced content, ensuring transparency, reliability, and fairness in automated review processes has become a central research challenge.
This workshop brings together researchers and practitioners from information retrieval, Web science, artificial intelligence, and data governance to discuss how TAR methodologies can support trustworthy and scalable information access. Topics include intelligent retrieval, human-in-the-loop learning, explainable and responsible AI, and the integration of large language models and knowledge graphs into review workflows. Through paper presentations, keynotes, and interactive sessions, ALTARS 2026 seeks to foster interdisciplinary collaboration and identify future directions for developing transparent, fair, and adaptive Web-scale review systems.
Organizers: Giorgio Maria Di Nunzio (University of Padova, Italy), Evangelos Kanoulas (University of Amsterdam, The Netherlands), Prasenjit Majumder (DAIICT, Gandhinagar, India)
Website: https://altars2026.dei.unipd.it/
Description:
With the rapid proliferation of social media platforms, the web has become a vast repository of diverse multimodal data, including text, images, audio, and video. This rich data offers unprecedented opportunities for comprehensive analysis but also introduces significant challenges in data fusion, information alignment, and robust processing. Large Multimodal Models (LMMs) have shown remarkable success in unimodal domains, yet their application to the complex and noisy nature of social media data necessitates significant innovation, particularly when such models are deployed on public-facing web platforms where performance, robustness, and safety are all critical. There is a growing need for trustworthy multimodal learning methods that can effectively integrate information from different modalities, maintain temporal, spatial, and semantic consistency, and reliably support downstream social media understanding tasks in real-world scenarios.
The International Workshop on Trustworthy Multimodal Learning for Social Media Analysis (TML 2026) aims to bring together researchers, practitioners, and industry experts to discuss the latest advancements, challenges, and future directions in analyzing multimodal social media content using LMMs, as well as to rigorously evaluate their performance and safety for real-world deployment on web platforms. The workshop focuses on two critical and closely related frontiers: (1) multimodal social media content analysis with LMMs, including effective strategies for multimodal fusion and information alignment, and (2) performance and safety evaluation of LMMs, including the quality of generated content, instruction-following ability, model hallucinations, vulnerability to “jailbreak” attacks, and the generation of safe and appropriate content. By showcasing cutting-edge research and fostering discussion on these topics, TML 2026 seeks to shape future research directions in trustworthy multimodal learning for social media and to contribute to the development of more effective and safer multimodal AI systems for the web ecosystem. In addition to attracting high-quality research contributions, the workshop aims to build and mobilize an active community at the intersection of multimodal learning, social media analysis, and responsible AI.
Organizers: Jingwei Sun, ByteDance, China Guosheng Lin, Nanyang Technological University (NTU), Singapore Fengmao Lv, Southwest Jiaotong University, China Tao Liang, ByteDance, China Junlin Fang, Southwest Jiaotong University, China Jianli Wang, Southwest Jiaotong University, China
Description:
The Future of Information Integrity Research (FIIR) Workshop explores the challenges and opportunities of ensuring reliable information in the era of generative AI. While these models can assist users in reasoning and information retrieval, they also introduce risks, including deepfakes, AI-driven persuasion, and large-scale manipulation of information flows. FIIR convenes researchers, practitioners, and policymakers to address these risks across four interconnected modules: Model Design (Core ML), Model Behaviour (Applied ML/AI Safety), Model Impact (Human Factors), and Model Ecosystem (Policy and Socio-Technical Systems).
The workshop features keynotes, expert panels, and breakout discussions designed to foster collaboration and bridge technical research with real-world practice, contributing frameworks and roadmaps for trustworthy information ecosystems.
Organizers: Taylor Lynn Curtis (Mila), Reihaneh Rabbany (McGill University, Mila), Hause Lin (Cornell, MIT, The World Bank), Maximilian Puelma Touzel (Mila), Kellin Pelrine (FAR.AI), Aisha Gurung (University of Bath), Catherine Régis (Mila, University of Montreal), Jean-François Godbout (Mila, University of Montreal), Nikki Lobczowski (McGill University), Bijean Ghafouri (University of Southern California)
Description:
The landscape of web advertising is undergoing a profound transformation, fueled by advancements in emerging technologies that prioritize user privacy, AI-driven personalization, and immersive experiences. As the digital advertising ecosystem evolves, understanding these trends is essential for navigating its dynamic complexities.
This workshop, organized by the Web Standards Group at Samsung Research and Development Institute UK (SRUK), provides a platform for timely, responsible, and open discussions among experts from advertising, privacy, data science, and related fields. Through cutting-edge research presentations and dialogue, the workshop aims to shape the future of web advertising while aligning technological progress with ethical and user-centric principles.
Organizers: Ehsan Toreini (Samsung Research and Development Institute UK), Muadh Al Kalbani (Samsung Research and Development Institute UK)
Description:
The rapid proliferation of foundation models has transformed how information is processed, generated, and consumed on the web. From large language models powering conversational agents to multimodal systems driving content recommendation and retrieval, these models increasingly shape user experiences and web intelligence. Yet, their reliance on massive, uncurated web data raises critical concerns about trustworthiness, including bias propagation, lack of transparency, and vulnerability to manipulation.
This workshop aims to advance the discussion on Trustworthy Foundation Models for the Web by introducing a causal perspective—a principled approach to understanding and improving the reliability, interpretability, and fairness of large-scale models. It will convene experts from machine learning, causal inference, web data mining, and social computing to exchange ideas, present recent advances, and identify open challenges. By bridging causal reasoning with web-scale foundation models, the workshop seeks to establish a roadmap toward more robust, transparent, and ethically aligned AI systems that operate responsibly within the web ecosystem.
Organizers: Haoang Chi (Tsinghua University) Qi (Cheems) Wang (Tsinghua University) Jiantong Jiang (The University of Melbourne) Jiangchao Yao (Shanghai Jiaotong University) Feng Liu (The University of Melbourne) Bo Han (Hong Kang Baptist University)
Description:
A full-day interactive workshop exploring how applied AI and multimodal visualization technologies can enhance knowledge representation, decision-making, and human–machine collaboration. It combines paper presentations with brainstorming sessions to foster collaboration and future research directions.
The workshop aims to:
• Showcase cutting-edge research and practical applications at the intersection of AI and multimodal visualization.
• Facilitate interdisciplinary dialogue between researchers, practitioners, and industry stakeholders.
• Explore methodologies and tools to improve human decision-making through multimodal data representation.
• Build collaborative networks to identify future research directions and applied use cases.
Organizers: Prof. Cesar Sanin (Australian Institute of Higher Education / University of New England, Australia), Prof. Edward Szczerbicki (University of Newcastle, Australia / Gdańsk University of Technology, Poland), Dr. Md Rafiqul Islam (Charles Darwin University, Australia)
Description:
The R2CASS workshop advances computational reproducibility in social science, often relying on digital behavioral data from social media platforms. It brings together computer scientists, social scientists, behavioral analysts, and digital policy makers to discuss improvements that make computational social science research more credible, reproducible, and impactful.
Building on the 1st R2CASS edition, this workshop assesses available resources—guidelines, checklists, templates—and services that support reproducibility practices. The workshop presents the Methods Hub as a case study, demonstrating how aligned resources and services can be integrated to lower reproducibility barriers.
Participants will take part in a hands-on session to perform a computational reproducibility task on the Methods Hub platform, followed by feedback discussions. By linking methodological rigor with substantive inquiry, the workshop encourages research related to computational reproducibility and social science.
Organizers: Fakhri Momeni (GESIS – Leibniz Institute for the Social Sciences, Germany), Arnim Bleier (GESIS – Leibniz Institute for the Social Sciences, Germany), Danilo Dessi (University of Sharjah, UAE), Muhammad Taimoor Khan (GESIS – Leibniz Institute for the Social Sciences, Germany)
Description:
In an increasingly interconnected world, where information flows rapidly through various digital platforms, multimodal content analysis has become an immense challenge. The prevalence of memes or text-embedded images has further complicated this issue, as it is often widely shared due to its engaging and easily consumable nature. Whether humorous or thought-provoking, these content forms can quickly go viral, spreading across social networks and online communities with ease.
Their visual appeal, concise messaging, and emotional resonance make them powerful tools for communication—shaping opinions and driving online conversations. However, this ease of sharing creates an urgent need for effective moderation. Moderating multimodal content requires sophisticated techniques capable of processing and understanding both visual and textual information simultaneously.
The Fourth International Workshop on Multimodal Content Analysis for Social Good (MM4SG 2026) aims to address these challenges by bringing together researchers from natural language processing, machine learning, computational social science, and ethics to explore innovative solutions for content moderation. The workshop provides a platform to share cutting-edge research, exchange ideas, and foster collaborations toward robust multimodal content analysis techniques.
Organizers: Usman Naseem (Macquarie University), Surendrabikram Thapa (Virginia Tech), Roy Ka-Wei Lee (Singapore University of Technology and Design), Mehwish Nasim (University of Western Australia)