DISA@DSAA 2023 : Computational methods for emerging problems in disinformation analysis
Call For Papers
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 https://conferences.sigappfr.org/dsaa2023/) in Thessaloniki, Greece.
The session will be technically endorsement by IEEE SMC TC on Big Data Computing as well by Lifelong Machine Learning on Data Stream and SWAROG projects.
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