He is a Lecturer (Assistant Professor) at the Queen Mary University of London, UK. He leads the Social Data Science Lab. His research revolves around Social Data Science, interdisciplinary research bridging Computational Social Science and Natural Language Processing.
He is particularly interested in linking online data with events in the real world, among others for tackling problematic issues on the Web and social media that can have a damaging effect on individuals or society at large, such as hate speech, misinformation, inequality, biases and other forms of online harm.
Towards Automated Fact-checking for Detecting and Verifying Claims
Automated fact-checking is a complex task that goes beyond the determination of the veracity of stories. An end-to-end fact-checking pipeline would involve the initial step of detecting which claims need to be fact-checked (claim check-worthiness detection), then aggregating associated evidence and knowledge, to ultimately summarise all together in a report making a verdict on the veracity of the story. In this talk, I will cover some of my research in these directions when dealing with social media data. I will first briefly discuss research assessing the capacity of untrained users in determining the potential veracity of stories. I will then discuss research in automatically detecting rumors and claims needing verification, as well as in the subsequent steps of collecting crowd stance and evidence towards determining the veracity value of stories. As part of the talk, I will be touching upon the problem of collecting suitable datasets to tackle the task.
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She is a Full Professor at the Department of Informatics, Systems, and Communication (DISCo) of the University of Milano-Bicocca. Within DISCo she leads the Information and Knowledge Representation, Retrieval, and Reasoning (IKR3) Lab.
Her main research interests are related to Information Retrieval, Recommender Systems, Text Mining, Knowledge Representation and Reasoning, and User Modeling. She is also interested in Social Media Analytics and, in particular, in the analysis of User-Generated Content for the study of information dissemination and evolution and information credibility assessment.
Credibility and Relevance in Information Retrieval
In the field of Information Retrieval, the study of how to ensure access to relevant information with respect to users’ information needs has been steadily developing over the last fifty years. It has gone from considering only the topical relevance of documents to taking into account the concept of popularity in Web search engines, to considering user contextual aspects in personalized search. Nowadays, where we are witnessing the proliferation of misinformation spread through both the Web and social media, a new challenge arises in the IR field: to provide users with information that is also credible. Depending on whether one searches Web pages or social content, depending on the task and the domain for which the search is performed, the concept of credibility understood as an aspect of relevance may change. In this talk, I will therefore present some issues related to credibility assessment in IR, and the problem of constructing environments and datasets for experimental evaluation of approaches that intend to address them.
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