Professor, Universität Regensburg
Udo Kruschwitz is a Professor of Information Science at the University of Regensburg. Prior to that, he had worked in the School of Computer Science and Electronic Engineering at the University of Essex for over 20 years (which is where he completed his Ph.D.).
His main research interest is the interface between information retrieval (IR) and natural language processing (NLP). He is particularly interested in projects that are aimed at transferring knowledge from academia into industrial applications. He has been involved in various forms of industry collaborations and is particularly happy about the collaboration with Signal AI which started as a Knowledge Transfer Partnership (KTP) project. The company has since become a key player in AI with more than 200 employees.
He is also actively involved in the British Computer Society’s Information Retrieval Specialist Group currently serving as its chair and co-organizes various events such as the Data Science @ Regensburg Meetup.
Keynote Speech: A Multidisciplinary Approach to Tackling Online Misinformation
Online misinformation has become a serious problem and judging by the rapid progress being made in automatically producing highly fluent and well-contextualized text by the press of a button it is fair to assume that the problem is not just not going away but going to get a lot worse. What’s more, it does not just affect individuals but has already been demonstrated to have wider implications on society, just think about some of the disinformation campaigns that had been conducted ahead of key elections.
How should the problem be addressed? There is no silver bullet, and what is needed is perhaps a wide range of approaches. The scope of the workshop is defined as exploring the use of credible information retrieval as one way forward, i.e., making sure that users get access to information that is topically relevant and genuine. I will look at a complementary, educational approach that is aimed at equipping the information consumer with the skills and knowledge to deal with misinformation.
I will report on various directions and ideas we are exploring in the multinational and multidisciplinary COURAGE project (https://www.upf.edu/web/courage) whose ultimate goal is to develop a social media companion aimed at supporting and educating users in dealing with misinformation and other social media threats, effectively teaching them social media literacy skills.
Assistant Professor, Tenure Track, University of Copenhagen
Maria Maistro studied initially Mathematics (BSc, University of Padua, 2011; MSc, University of Padua, 2014) and then Computer Science (Ph.D., University of Padua, 2018). She is a tenure track assistant professor at the Department of Computer Science, University of Copenhagen (DIKU). Prior to this, she was a Marie Curie fellow and a postdoctoral researcher at the Department of Computer Science, University of Copenhagen (DIKU), and at the University of Padua in Italy.
She conducts research in information retrieval, and particularly on evaluation, reproducibility and replicability, click log analysis, expert search, and applied machine learning. She has already co-organized several international scientific events and she has served as a member of program committees and reviewer for highly ranked conferences and journals in information retrieval.
Keynote Speech: Evaluating Misinformation – Accounting for Credible and Correct Information
In recent years, the spread of misinformation and fake news has increased more and more and has become a serious concern in different areas, e.g., politics, social media, etc. In the health domain, misinformation represents an even more alarming concern because false or not correct information can potentially threaten or harm consumers’ health. This talk will provide an overview of how to quantitatively assess and measure misinformation through the experience of four editions of the TREC health misinformation track. The track aims at helping the design of Information Retrieval systems able to promote credible and correct information over not credible and incorrect information. The first part of the talk will cover challenges in building test collections for misinformation, for example, choosing corpora that include enough low-quality and non-credible web pages, defining topics that can be fact-checked, and instructing assessors to collect multi-aspect judgments. The second part will focus on how to define evaluation measures able to penalize systems that return misinformation and to account for aspects beyond relevance, e.g., credibility and correctness. The talk will conclude with lessons learned and the outlook for future perspectives and promising directions.