The workshop on Computational methods for emerging problems in disinformation analysis DISA@DSAA is organized during the The 10th IEEE International Conference on Data Science and Advanced Analytics (DSAA) in Thessaloniki, Greece.
Information analysis is nowadays crucial for societies, single citizens in their everyday life (e.g. while travelling, shopping, browsing, communication etc.) as well for businesses and overall economy. The right to be informed is one of fundamental requirements allowing for taking right decisions in a small and large scale (e.g. elections).
However information spreading can be also used for disinformation. The problem of the fake news publication is not new and it already has been reported in ancient ages, but it has started having a huge impact especially on social media users or people watching media news (Internet, newspapers, tv etc.). Such false information should be detected as soon as possible to avoid its negative influence on the readers and in some cases on their decisions.
Another problem and emerging challenge is coming from using automated information analysis and decision support systems (based on Artificial Intelligence (AI) and Machine Learning (ML) advances) as black-box single truth providers. If those information analysis systems are misused, attacked or fooled, their results will also lead to (dis-) information.
The main aim of this workshop is to bring together researchers and scientists computational science who are pioneering (dis-)information analysis methods to discuss problems and solutions in this area, to identify new issues, and to shape future directions for research. Moreover, we invite prospective researchers to send papers concerning (dis-)information detection methods and architectures, explainability of information processing methods and decision support systems as well as their security.
Topics of interest
- computational methods for (dis-) information analysis, especially in heterogenous types of data (images, text, tweets etc.)
- detection of fake news detection in social media
- images and video manipulation recognition
- architectural frameworks and design for (dis-)information detection
- aspects of explainability of information analysis systems and methods (including explainability of ML)
- adversarial attacks on information analysis
- explainability of deep learning
- learning how to detect the fake news in the presence of concept drift
- learning how to detect the fake news with limited ground truth access and on the basis of limited data sets, including one-shot learning
- proposing how to compare and benchmark the fake news detectors
- case studies and real-world applications
- human rights, legal and societal aspects of (dis-)information detection, including data protection and GDPR in practice
|Paper submission||new 5 June 2023|
|Notification to authors||17 July 2023|
|Camera-ready papers||7 August 2023|
|Conference sessions||9-13 October 2023|
- Prof. Michał Choraś, Bydgoszcz University of Science and Technology, Poland e-mail: firstname.lastname@example.org
- Prof. Michał Woźniak, Wroclaw University of Science and Technology, Poland e-mail: email@example.com
- Evgenia F. Adamopoulou, ICCS, NTUA, Greece
- Tomasz Andrysiak, Bydgoszcz University of Science and Technology, Poland
- Łukasz Apiecionek, Kazimierz Wielki University, Bydgoszcz, Poland
- Stan Assier, QWANT, France
- Sebastian Basterrech, VSB-Technical University of Ostrava, Czech Republic
- Robert Burduk, Wroclaw University of Science and Technology, Poland
- Krzysztof Cabaj, Warsaw University of Technology, Poland
- Konstantinos Demestichas, ICCS, NTUA, Greece
- Agata Gielczyk, Bydgoszcz University of Science and Technology, Poland
- Manuel Grana, University of the Basque Country, Spain
- Álvaro Herrero, University of Burgos, Spain
- Dagmara Jaroszewska-Choras, Kazimierz Wielki University, Bydgoszcz
- Jędrzej Kozal, Wroclaw University of Science and Technology, Poland
- Michał Koziarski, Wroclaw University of Science and Technology, Poland
- Rafał Kozik, Bydgoszcz University of Science and Technology, Poland
- Pawel Ksieniewicz, Wroclaw University of Science and Technology, Poland
- Iulia Lazar, Infocons, Romania
- Wojciech Mazurczyk, Warsaw University of Technology, Poland
- David Megias, UoC, Barcelona, Spain
- Marek Pawlicki, Bydgoszcz University of Science and Technology, Poland
- Jan Platoš, VSB-Technical University of Ostrava, Czech Republic
- Mariusz Topolski, Wroclaw University of Science and Technology, Poland
- Giulia Venturi, Z&P, Italy
- Paweł Wachel, Wroclaw University of Science and Technology, Poland
All papers should be submitted electronically via EasyChair.
The length of each paper submitted to the Research tracks should be no more than ten (10) pages and should be formatted following the standard 2-column U.S. letter style of IEEE Conference template. For further information and instructions, see the IEEE Proceedings Author Guidelines.
All submissions will be blind reviewed by the Program Committee on the basis of technical quality, relevance to the conference’s topics of interest, originality, significance, and clarity. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and authors anonymity will be rejected without reviews.
Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar cannot be considered for DSAA’2023. An exception to this rule applies to arXiv papers that were published in arXiv at least a month prior to DSAA’2023 submission deadline. Authors can submit these arXiv papers to DSAA provided that the submitted paper’s title and abstract are different from the one appearing in arXiv.