Our workshop on truth discovery and fact-checking is motivated by the need for new research, tools and techniques to advance the field with the following focus areas:
- Multi-modality – Modern data are multi-modal encompassing relational data, social networks, natural language text, structured logs, images, video, etc. One family of techniques geared towards a particular data source may not work for the other. One of the major focus areas of this workshop is data heterogeneity, multi modality, novel applications and data sources related to truth discovery, fact-checking, and rumor detection.
- Interpretability – Another focus area is to explore tools and methods that can generate human-interpretable explanations as opposed to black box methods, as transparency and lack of explanations remain a concern for industry to readily deploy these techniques in practise. Robustness. The third focus of the workshop will be security and robustness of fake content detection under a known or unknown environment where adversaries can learn and commit arbitrary attack strategies for data poisoning or evading detectors.
- Resource-efficient Learning – Finally, lack of labeled training data and scarce annotation resources remain a serious challenge for truth discovery and credibility analysis – making it difficult to train state-of-the-art methods like deep neural networks. In this context, we encourage submissions focusing on techniques like few-shot learning, weak supervision from user interactions and distant supervision from auxiliary knowledge sources for resource-efficient learning.
Topics of interest include, but are not limited to:
- Truth finding and discovery
- Fact-checking, rumor, and misinformation
- Credibility analysis and spam detection
- Fake reviews and reviewers
- Leveraging knowledge bases for reasoning, validating and explaining contentious claims
- Transparency, fairness, bias, privacy and ethics of information systems
- Emerging applications, novel data sources, and case studies
- Explainable and interpretable models
- Robustness detection under adversarial and unknown data poisoning and evasion attacks.
- Heterogeneous and multi-modal information including relational data, natural language text, search logs, images, video, etc.
Submission guidelines
We invite submissions for original research papers both theory and application-oriented as well as submissions from the research track and applied data science track of the main KDD conference. We encourage the participants to submit papers on novel datasets and release them to advance the field. Papers must be submitted in PDF according to the ACM Proceedings Template https://www.acm.org/publications/proceedings-template (opens in new tab) in a single-blind format (including author names and affiliations). We welcome both long papers (maximum length of 9 pages) and short papers (maximum length of 5 pages). The accepted papers will be published on the workshop’s website, and will not be considered archival for resubmission purposes. Please submit your papers at the EasyChair submission link https://easychair.org/my/conference?conf=truefact21 (opens in new tab).
Important dates
- All deadlines are 11:59 PM Pacific Standard Time
- Workshop paper submissions due: June 1, 2021
- Workshop paper notifications: June 20, 2021
- Workshop date: August 14-18, 2021