The rapid growth of big data in social and behavioral domains has led to the accumulation of diverse structured and semi-structured data types, including images, time series, event logs, textual sequences, and tokenized interactions. These data are inherently rich in behavioral semantics—often temporally dependent, hierarchically organized, and socially contextualized. Moreover, they frequently exhibit complex internal structures (e.g., sequential dependencies, spatial patterns) and external interactions (e.g., user–system, peer-to-peer, multi-agent dynamics), reflecting latent cognitive, social, or educational behaviors.

Effectively modeling such behavior-intensive data at scale requires AI systems that can handle structured input modalities, capture dynamic interactions, and incorporate explanatory signals from domain expertise. Beyond generative AI, techniques such as knowledge tracing, graph neural networks, sequence modeling, and multi-modal learning have shown great promise in uncovering hidden behavioral patterns. However, a key challenge remains: how to distill and integrate domain knowledge—such as cognitive theories, learning sciences, and social frameworks—into these data-driven models to improve interpretability, generalization, and behavioral fidelity.

The special session invites submissions addressing, but not limited to, the following areas:

  • Theoretical Foundations
    • Knowledge representation for behavioral data (e.g., knowledge graphs, semantic networks).
    • Integration of domain theories into generative and predictive models.
    • Cognitive and affective modeling informed by structured knowledge.
  • Methodological Innovations
    • Knowledge-enhanced Large Language Models (LLMs) for behavioral analysis.
    • Graph neural networks with embedded sociocultural or psychological structures.
    • Generative modeling (e.g., VAEs, diffusion models) augmented by expert rules or prior knowledge.
    • Knowledge tracing frameworks for personalized behavioral understanding in education and training.
  • Applications in Behavioral and Social Computing
    • Educational systems: Personalized learning pathways and engagement modeling.
    • Mental health: Early detection and intervention strategies using multimodal behavioral data.
    • Social platforms: Generative modeling of misinformation spread and user behavior dynamics.
    • Public policy: Knowledge-informed simulation of urban behaviors and decision-making processes.
  • Ethical and Societal Considerations
    • Transparency and explainability in generative AI for behavioral modeling.
    • Responsible use of behavioral data in knowledge-enhanced AI systems.
    • Mitigating algorithmic bias through knowledge-guided modeling.
  • Benchmarks and Future Directions
    • Benchmark datasets and evaluation metrics for knowledge + data-driven social models.
    • Surveys on hybrid modeling approaches in behavioral computing.
    • Roadmaps for next-generation intelligent systems for social and behavioral analysis.

Important Dates

  • Special Session Papers Submission: 15 July 2025
  • Acceptance Notification: 15 August 2025
  • Camera-Ready Submission: 01 September 2025
  • Conference Date: 16-18 Oct 2025

Paper submission instruction

Paper submission system is available at: https://cmt3.research.microsoft.com/BESC2025.

All papers will be reviewed by the Program Committee on the basis of technical quality, relevance to BESC 2025, originality, significance and clarity.

Please note:

  • All submissions should use IEEE two-column style. Templates are available from here.
  • All papers must be submitted electronically through the paper submission system in PDF format only. BESC 2025 accepts special session papers in 6 pages. The page count excludes the references.
  • Paper review will be double-blind, and submissions not properly anonymized will be desk-rejected without review.
  • Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings.
  • Papers must be clearly submitted in English and will be selected based on their originality, timeliness, significance, relevance, and clarity of presentation.
  • Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work.
  • Accepted papers will be submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements and indexed by EI. Top quality papers after presented in the conference will be selected for extension and publication in several special issues of international journals, e.g., IEEE Transactions on Computational Social Systems (IEEE), IEEE Transactions on Learning Technologies (IEEE), CCF Transactions on Pervasive Computing and Interaction (Springer), EURASIP Journal on Image and Video Processing (Springer), World Wide Web Journal (Springer), Social Network Analysis and Mining (Springer), Human-Centric Intelligent Systems (Springer), Natural Language Processing (Elsevier), Health Information Science and Systems (Springer), Web Intelligence (IOS Press), etc.
  • The use of artificial intelligence (AI)–generated text in an article shall be disclosed in the acknowledgements section of any paper submitted to an IEEE Conference or Periodical. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text.

Organizing Committee

Special Session Chairs
  • Dr. Zhangkai Wu, Macquarie University, Australia
  • Dr. Hengyu Liu, Aalborg University, Denmark