Research and Applications of Foundation Models for Data Mining and Affective Computing (RAFDA)

7 - 10 May Taipei, Taiwan
PAKDD 2024 Workshop

Exciting News for RAFDA 2024

Exciting News: We are delighted to confirm that Springer will be the publisher for the main track of PAKDD 2024. Additionally, we are pleased to announce that the proceedings of our RAFDA workshop will also be published by Springer.

RAFDA Starts In:


The proceedings of the RAFDA workshop will be published by Springer (Lecture Notes in Artificial Intelligence (LNAI) series.


This workshop, entitled Research and Applications of Foundation Models for Data Mining and Affective Computing (RAFDA), serves as an inclusive platform exploring the intricate intersections of foundation models, including LLMs, data mining, and affective computing. RAFDA represents a converging space uniting researchers focused on the applications, advancements, and implications of foundational models within the realms of data mining and affective computing.
At its core, RAFDA encapsulates interdisciplinary dialogue, spotlighting the innovative utilisation of cutting-edge foundational models for robust data mining practices. Simultaneously, it delves into the nuanced understanding and interpretation of affective computing, nurturing an inclusive forum for collaboration and knowledge exchange among researchers, practitioners, and industry experts. RAFDA also aims to be an international workshop to facilitate dynamic discussions among researchers in foundation models, NLP and data mining, fostering a collaborative environment for sharing groundbreaking research, novel methodologies, and innovative applications. RAFDA seeks to explore the diverse applications of foundation models, including LLMs, across academic and industrial domains, paving the way for envisioning future advancements and directions within the realm of data mining and AI research.

Important Dates

Special notes: We offer two submission rounds for the RAFDA workshop, providing additional opportunities for authors. For authors from China, we strongly recommend meeting the first-round submission deadline (Jan 17 2024) due to the potential three-month period required for visa processing to enter Taipei. We are pleased to confirm that the proceedings of our RAFDA workshop will be published by Springer in the Lecture Notes in Artificial Intelligence (LNAI) series. To attract more high-quality papers, we have extended the deadline (Feb 7 2024)for the second round of submissions by one week. The new deadline is Feb 14 2024.

* 2nd round submissions(All paper deadlines are 23:59 Pacific Standard Time (PST))

Submission Deadline

February 14

Acceptance Notification

February 28

Camera Ready Paper

March 14

Scope & Topics

  • Techniques for utilizing foundation models (like LLMs) for data preprocessing and feature extraction in mining large datasets.
  • Foundation models for anomaly detection, clustering, and classification in data mining.
  • Mining unstructured data using foundation models for improved pattern recognition and data analysis.
  • Sentiment analysis using foundation models in diverse applications and industries.
  • Emotion recognition and affective computing through foundation models.
  • Understanding human emotions through large-scale textual data and deep learning techniques.
  • Investigating affective computing methods integrating linguistic and computational approaches, aiming to understand and interpret emotions, sentiments, and affective states through machine learning and foundation models, e.g., LLMs, or other data mining techniques.
  • Interpretability and explainability of foundation models in affective computing.
  • Utilizing foundation models, e.g., LLMs for large-scale data analysis and pattern recognition.
  • Ethical considerations in deploying foundation models for data mining and affective computing.
  • Addressing biases and fairness issues in foundation model-based applications.
  • Societal impact and privacy concerns related to the utilization of LLMs.
  • Adaptation and fine-tuning of foundation models, e.g., LLMs for domain-specific affective data.
  • Applications of foundation models, e.g., LLMs in sentiment-aware decision-making processes.
  • Semantic understanding and context-based affective analysis.
  • Techniques to interpret and explain the predictions of foundation models in data mining and affective computing.
  • Explaining the decision-making process of foundation models for better transparency and trust.
  • Encompassing language modeling, text generation, sentiment analysis, summarization, and various aspects of data mining.
  • Architecture and functioning of foundation models, such as LLMs (e.g., ChatGPT).
  • Integration of visual, textual, and phonetic information using foundation models for improved data understanding and analysis.
  • Multimodal sentiment analysis and affective computing with fusion of diverse data sources.
  • Conversational AI and text generation tasks.
  • Applications of foundation models, e.g., LLMs in healthcare, finance, customer service, education, and other industry-specific domains.
  • Case studies and success stories showcasing the impact of foundation models in various fields.
  • Hands-on sessions for participants to engage with foundation models, e.g., LLMs, experiment with applications, and brainstorm future directions in the field.

Paper Submission

Submitted papers must be in English. The Program Committee will undertake a double-blind review of all papers based on their technical merit, relevance to the field of data mining, originality, significance, and clarity. Every submitted paper should include an abstract of no more than 200 words and should not exceed 12 single-spaced pages using a 10pt font size, including references, appendices, etc. Authors are instructed to follow the Springer LNCS/LNAI manuscript submission guidelines for their submissions.
All papers must be electronically submitted via the CMT paper submission system (, and only in PDF format. Although authors can submit supplementary material in a separate PDF file, the reviewers are not obliged to consider it. Any papers failing to adhere to the Submission Policy will be rejected without review. Submitting a paper signifies that if the paper is accepted, at least one author will undertake regular registration and present the paper.
All submissions to RAFDA must be original work and not be under review or published in any other conference or journal. Submissions of papers must conform to the double-blind review policy, requiring the removal of any information identifying the author(s) from the main manuscript and any supplementary files. In discussing previous work, authors should reference their own studies in the third person and include all appropriate citations.

Formatting Template

All manuscripts should strictly adhere to the aforementioned format for preparation and submission. Deviation from this format may result in the disqualification of the paper from the RAFDA workshop of the conference.

Journal Publication

Extended versions of selected workshop papers will be published in either Cognitive Computation or IEEE ACSA.

Organizing Committee

Prof. Zhaoxia WANG
School of Computing and Information Systems
Singapore Management University
Prof. Erik CAMBRIA
School of Computer Science and Engineering
Nanyang Technological University
Prof. Bing LIU
School of Computer Science
University of Illinois at Chicago
Dr. Boon Kiat QUEK
Social and Cognitive Computing Department
Institute of High Performance Computing, A*STAR
Dr. Seng-Beng HO
Social and Cognitive Computing Department
Institute of High Performance Computing, A*STAR

Contact Information

Workshop Co-Chairs of RAFDA, PAKDD2024