IACT 2023 : The 1st International Workshop on Implicit Author Characterization from Texts for Search and Retrieval

The 1st International Workshop on Implicit Author Characterization from Texts for Search and Retrieval (IACT’23)
The workshop will be held in conjunction with the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval.

Workshop website: https://en.sce.ac.il/news/iact23
Date: July 27, 2023
Location: Taipei, Taiwan.
Paper submission deadline: Extended to May 23, 2023, AoE
Submission link: https://easychair.org/conferences/?conf=iact23

To bring the research community's attention to the limitations of current models in recognizing and characterizing AI vs. human authors, we organize the first edition of IACT workshops under the umbrella of the SIGIR conference. Research works submitted to the workshop should foster scientific advances in all aspects of author characterization.

Submission Guidelines:
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

- Full research papers: up to 8 pages. Original and high-quality unpublished contributions to the theory and practical aspects of the workshop topics.
- Short research papers: up to 5 pages. It can describe ongoing research, resources, and demos.
- Negative results papers: up to 5 pages. Highlighting tested hypotheses that did not get the expected outcome is also welcomed.
- Position papers: up to 5 pages. Discussing current and future research directions.

The length constraints do not include references.
The submissions must be anonymous and will be peer-reviewed by at least two program committee members.

Workshop Format:
The authors of accepted papers will be given 15 minutes for a short oral presentation. The workshop will run as a hybrid event to allow virtual attendance and meet the SIGIR format.

Workshop Topics:
Research works submitted to the workshop should foster the scientific advance on all aspects of implicit author information extraction from text, including but not limited to the following:

- Differentiation between AI-generated content and human-generated content and bot profiling
- Characterization of conversational agents
- Feature detection of authors for human vs. AI determination
- Prompt understanding and recognition in language models
- Personalized question-answering and conversation generation
- Troll identification on social media
- Review authenticity estimation
- Multi-modal, multi-genre, and multilingual author analysis
- Character analysis, description, and representation in narrative texts
- Detecting implicit expressions of sentiment, emotion, opinion, and bias
- Transfer learning for implicit author characterization
- Implicit author characterization annotation schema
- Evaluation of implicit author characterization
- Author characterization in low-resource languages and under-studied domains
- Accountability and regulation of AI-based information extraction, retrieval, and content generation
- Copyright issues of AI-generated content
- Ethical and privacy implications of author characterization and implicit information extraction
- Fairness and bias of AI-generated content

Organizing Committee:
Marina Litvak - [email protected]; Shamoon College of Engineering Beer Sheva; Israel
Irina Rabaev - [email protected]; Shamoon College of Engineering Beer Sheva; Israel
Alípio Mário Jorge - [email protected]; University of Porto; Porto, Portugal
Ricardo Campos - [email protected]; Polytechnic Institute of Tomar INESC TEC, Portugal; Porto, Portugal
Adam Jatowt - [email protected]; University of Innsbruck; Innsbruck, Austria

Invited Speakers:
Prof. Mark Last - Ben-Gurion University of the Negev, Israel
Prof. Dr. Valia Kordoni - Humboldt-Universität Berlin, Germany

Contacts:
Dr. Marina Litvak: [email protected]
Dr. Irina Rabaev: [email protected]