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Dr. Gianluca Demartini is a leading researcher in Human-in-the-loop AI Systems with significant contributions to Crowdsourcing , Information Retrieval , and Generative AI applications. His work bridges Machine Learning and Human-Computer Interaction , focusing on Bias Management , Fact-Checking , and Ethical AI . Major Affiliations : L3S Research Center, ScienceWISE platform, and collaborations with institutions like University of Queensland and University of Padua Over 15 years, his research has explored Crowdsourcing Quality Control (Mechanical Cheat 2012), Entity Ranking (2008-2013), and Semantic Search . Recent work (2024-2026) focuses on Generative AI Impacts in domains like Media Literacy , Data Curation , and Visual Analytics . Scientific Recognition : Best Paper Award (Top 1.4%) at ICTIR 2023 Best Short Paper Award (Top 0.6%) at ECIR 2020 Honorable Mention (Top 2%) at CSCW 2020 Best Demo Award at ISWC 2011 3rd Best Paper at LA-WEB 2008 His 15 most recent publications (2024-2026) demonstrate expertise in LLM-based Content Moderation , Immersive Data Visualization , and Trustworthy AI Systems . He has pioneered methods for Bias Detection in Wikipedia (2013), Entity Ranking (2008-2013), and Human-AI Collaboration frameworks. His work consistently addresses ethical challenges in AI for Social Good and Responsible Data Science .

• Information Retrieval
• Crowdsourcing
• Human-in-the-loop AI
• +5 more

Gias Uddin is an Associate Professor at York University's Lassonde School of Engineering and an Adjunct Professor at the University of Calgary . His research bridges Software Engineering (SE) and Artificial Intelligence (AI) , focusing on AI Trustworthiness Assessment (SE4AI) and AI-Driven Productivity Tools (AI4SE) . PhD in Software Engineering & AI, McGill University (2018) MSc in Software Engineering, Queen’s University (2008) BSc in Computer Science & Engineering, Bangladesh University of Engineering and Technology (2004) His research explores: Metamorphic Relations for LLM Hallucination Detection AI-Enhanced Software Documentation Foundational Models for Runtime System Modernization Developer-Centric AI Tooling Recent article trends show expertise in LLM Trustworthiness , Low-Code Platforms , and IoT Developer Communities . Awards include Distinguished Paper at FSE 2025 , multiple IBM Champion recognitions, and York Research Award . He leads the Data Intensive Software Analytics (DISA) Lab and mentors PhD students in SE-AI Intersections .

• Software Engineering
• Artificial Intelligence
• AI Trustworthiness Assessment (SE4AI)
• +5 more

Andrea Mattozzi is a Full Professor of Economics at the University of Bologna's Department of Economics since 2022, serving as Coordinator of the PhD Program in Economics. Previously, he held academic roles at institutions including the California Institute of Technology, Universitat Autònoma de Barcelona, and the European University Institute, where he also led the Economics Department. He earned his Ph.D. in Economics from the University of Pennsylvania and a Laurea (BA) in Economics (Summa Cum Laude) from Università Bocconi. His research focuses on Political Economy, Public Economics, and Applied Theory, with notable contributions to topics like electoral systems, policy implementation, and political competition. His work has been published in top journals such as the American Economic Review (AER) and the Journal of Economic Theory (JET), supported by grants from the NSF, Ramón y Cajal Fellowship, and EU initiatives like Horizon 2020. Mattozzi’s research demonstrates a consistent focus on institutional design, democratic accountability, and the interplay between political incentives and economic outcomes. Recent articles explore referendums, fact-checking mechanisms, and pandemic policy responses, reflecting both theoretical rigor and societal relevance. Awards: Ramón y Cajal Fellowship, NSF grants, William Polk Carey Prize (best dissertation), and CEPR Research Fellow appointment. Professional Roles: Associate Editor of the European Economic Review, RCEA Senior Fellow, and panelist for Economic Policy. His academic leadership includes directing PhD programs and coordinating interdisciplinary projects such as the European Media and Information Fund’s FacTS initiative. Mattozzi also maintains visiting professorships and collaborates internationally on policy-relevant research.

• Political Economy
• Public Economics
• Applied Theory
• +3 more

Alberto Barrón-Cedeño is an Associate Professor at the Department of Interpreting and Translation, University of Bologna (Italy). He holds a PhD in Artificial Intelligence from Universitat Politècnica de València (Spain) and has served as a former Scientist at Qatar Computing Research Institute (Qatar) and an Alain Bensoussan fellow at Universitat Politècnica de Catalunya (Spain). His research focuses on computational propaganda detection, automated fact-checking systems, and natural language processing applications in combating misinformation. Education: PhD in Artificial Intelligence, Universitat Politècnica de València, Spain Research Interests: Dr. Barrón-Cedeño specializes in developing AI-driven solutions for detecting disinformation, particularly in social media and news content. His work emphasizes computational propaganda analysis, fake news detection during crises (e.g., the COVID-19 pandemic), and designing tools to assist human fact-checkers. He leads initiatives such as the CLEF CheckThat! Lab to advance interdisciplinary approaches in misinformation research. Awards: Alain Bensoussan fellowship (Universitat Politècnica de Catalunya) Advising & Grants: While no formal advisees are listed, he collaborates extensively with global teams on funded projects related to computational fact-checking and propaganda detection. Labs & Teams: Core contributor to the CLEF CheckThat! Lab, which coordinates international efforts to develop benchmarks and tools for detecting check-worthy claims and fake news.

• Artificial Intelligence
• Natural Language Processing
• Fact-Checking Systems
• +3 more

Dr. Antonela Tommasel is a Researcher at CONICET and an Adjunct Professor at National University of the Center of the Province of Buenos Aires (UNICEN) , working at the intersection of social computing, machine learning, and misinformation mitigation. Her work combines computational techniques to address challenges in social media analysis, bias detection, and recommender system design. PhD in Computer Sciences (2017), UNICEN Bachelor in Software Engineering (2012), UNICEN Her research focuses on recommender systems with applications to social media trustworthiness , including projects like Faking It! (fake news dataset creation) and OHARS (harm-aware recommendation frameworks). Key themes include misinformation spread analysis , gender bias detection , and machine learning applications for social good. She has led 11+ years of teaching in courses like Advanced Java Programming, Recommender Systems, and Natural Language Processing for AI specializations. Her technical certifications include NVIDIA Deep Learning Institute programs on GPU-accelerated data science and Coursera specializations in machine learning and visualization. Currently based at ISISTAN Research Institute in Tandil, Argentina, she advocates for responsible AI through tools like graphoW (graph-of-words library) and Sen4Smells (technical debt prioritization). Her work emphasizes bridging social media analysis with practical implementation frameworks.

• Social Computing
• Recommender Systems
• Machine Learning
• +5 more

Luis Alberto Barron Cedeno is an Associate Professor at the Department of Interpretation and Translation, University of Bologna since 2022, where he previously served as Senior Assistant Professor from 2019-2022. He holds a PhD in Computer Science from Universitat Politècnica de València (2012) and has worked at Qatar Computing Research Institute (2014-2019) and TALP Research Center (ERCIM fellowship, 2012-2014). PhD: Universitat Politècnica de València (2012) MSc: AI (Universitat Politècnica de València, 2009) MSc: Computing Science (UNAM, 2007) BEng: Computing (UNAM, 2004) His research focuses on NLP applications for analyzing text qualities like originality (plagiarism detection) and intent (hate speech, propaganda). Recent work includes persuasion techniques in spam emails , misogyny detection using argumentation theory, and Spanish language varieties analysis. He has published over 100 papers with 20+ in top-tier conferences. Key awards include SemEval 2020 Best Task for propaganda detection and ACL 2020 Best Demo . He serves as Specialty Chief Editor for Frontiers in Artificial Intelligence (NLP) since 2023. His students include PhD candidates Arianna Muti (misogyny detection), Katerina Korre (hate speech), and Paolo Gajo (inceldom analysis). 2020: SemEval Best Task 2020: ACL Best Demo 2012: PhD International Mention 2009: Best MSc Thesis 2007: MSc Cum Laude 2004: BEng Cum Laude He organizes the annual CheckThat! lab at CLEF since 2018 and chaired CLEF 2022. His teaching includes NLP courses, computational linguistics, and game localization for translation students.

• Natural Language Processing
• Information Retrieval
• Machine Learning
• +7 more

Kaicheng Yang is a postdoctoral researcher at Northeastern University , with an upcoming transition to Assistant Professor in the Department of Computer Science at Binghamton University in Fall 2025. His work bridges computational social science, AI ethics, and platform governance. Education: Ph.D. in Informatics from Luddy School of Informatics, Computing, and Engineering , Indiana University Bloomington His research investigates malicious information manipulation through social bots, algorithmic biases, and generative AI misuse, developing countermeasures for trustworthy online ecosystems. Recent publications explore domain-sharing patterns , news source credibility , and AI-generated social media fraud across journals like Scientific Data , PNAS , and Journal of Online Trust and Safety . Awarded Best Paper - Honorable Mention at the International AAAI Conference on Web and Social Media, Yang also contributes to open-source tools such as the LLM Domain Classification dashboard and Botometer X. His work has received media coverage from Wired , Forbes , and MIT Technology Review .

• Computational Social Science
• Social Bots
• Misinformation
• +4 more

Andrea Galassi is a Junior assistant professor (RTD-A) at the University of Bologna's Department of Computer Science and Engineering (DISI), part of the Language Technologies Lab led by Paolo Torroni. He holds a PhD in Computer Science and Engineering from the University of Bologna (2021), with a dissertation on integrating deep neural networks and symbolic knowledge. His postdoctoral research included roles at Stanford, Imperial College London, and the Humane-AI-Net European project on ethical AI. He also served as an Adjunct Professor at the University of Bologna. His research focuses on Machine Learning, Natural Language Processing (NLP), and neuro-symbolic techniques applied to argument mining, legal text analysis, and ethical AI. Notable projects include the FAIR initiative (developing scalable AI techniques), CLAUDETTE, PRIMA, and ADELE legal analytics projects, and StairwAI's horizontal matchmaking services. He is an expert in argument mining for judicial decisions and automated analysis of privacy policies. Galassi has taught over 300 hours of courses, ranging from foundational computer science to advanced NLP topics. He holds National Scientific Qualification for Associate Professor in Computer Engineering (ASN 2023-2025). His recent work emphasizes ethical AI applications, misinformation detection, and AI-driven legal systems, with publications in areas like cross-lingual legal benchmarking (LEXTREME) and subjectivity detection in news media. He leads projects on AI for social impact, including chatbots for asylum seekers and privacy-preserving dialogue systems. His lab participates in CLEF challenges on news credibility and legal argumentation analysis, demonstrating expertise in collaborative AI frameworks and real-world societal applications.

• Natural Language Processing (NLP)
• Machine Learning
• Argument Mining
• +5 more

Matthew R. DeVerna is a computational social scientist based at Indiana University Bloomington, affiliated with the Observatory on Social Media under Dr. Filippo Menczer. In September 2025, he will join Stanford's Cyber Policy Center as a postdoctoral scholar in the Social Media Lab led by Dr. Jeffrey Hancock. His research bridges artificial intelligence, social media analysis, and misinformation dynamics, with a focus on their societal and public health implications. Education: PhD in Informatics, Indiana University (2025) MA in Psychology, New York University (2020) DeVerna’s research examines how artificial intelligence shapes information access and human interaction on digital platforms. Key projects include modeling misinformation’s role in disease spread, analyzing ideological polarization on social media, and developing tools like the Top FIBers dashboard to track misinformation superspreaders. His work often employs computational methods and agent-based modeling to address systemic challenges in information integrity. His peer-reviewed publications span journals like PNAS , npj Complexity , and Scientific Reports , with recurring themes in misinformation correction, vaccine hesitancy, and platform governance. He has received multiple awards, including a $10,000 Cognizant Trust and Safety scholarship and fellowships from the Institute for Humane Studies and Michigan Institute for Data Science. DeVerna actively mentors students, notably through Indiana University’s UROC program, and develops open-source tools such as the Python packages py_misinfo_exposure and OsoMeTweet . He co-organizes academic workshops like CySoc and leads initiatives like the Awesome Speakers event series to foster interdisciplinary dialogue on social media manipulation.

• Computational Social Science
• Misinformation
• Social Media
• +3 more

Folco Panizza is an Assistant Professor at the IMT School for Advanced Studies Lucca, affiliated with the MOMILAB research group. His work focuses on behavioral interventions, misinformation mitigation, and norm dynamics in digital spaces. He explores how social and cognitive mechanisms influence decision-making in contexts like public health, environmental behavior, and political communication. His research investigates topics such as vaccine hesitancy, the impact of social media on knowledge perception, and the design of effective nudges to promote pro-social behaviors. Recent studies include analyzing the role of cultural-historical perspectives in intervention design and evaluating the efficacy of medical expert endorsements in reducing vaccine skepticism. Panizza’s publications consistently address interventions against misinformation, with a focus on both theoretical frameworks (e.g., attitude entropy models) and practical applications (e.g., online experiment-driven solutions). His work bridges social psychology, behavioral economics, and computational methods, often employing large-scale experimental designs. Notable contributions include studies on the erosion of democratic norms following election outcomes and the effectiveness of crowdsourced fact-checking. His research highlights the interplay between individual attitudes and systemic societal structures, emphasizing the need for context-aware intervention strategies.

• Social Psychology
• Public Health Communication
• Behavioral Economics
• +3 more

Arkaitz Zubiaga is a Senior Lecturer (Associate Professor) at Queen Mary University of London, where he co-leads the Social Data Science lab and serves as Director of Graduate Studies. He is also part of the leadership team of the Centre for Human-Centred Computing. His research sits at the intersection of Computational Social Science and Natural Language Processing, focusing on developing NLP and LLM methods for processing social media and Web data to tackle societal harms. Zubiaga's research interests concentrate on addressing problematic issues with damaging societal effects, including hate speech, misinformation, inequality, biases, and other forms of online harm. He investigates how LLMs can be misused for malicious purposes such as spreading misinformation, generating abusive content, or exacerbating societal biases. His work emphasizes detecting and addressing irresponsible AI use where content is falsely claimed to be human-generated. His publication record shows a clear trend toward addressing bias in detection systems, particularly in cyberbullying detection where swearing bias has been identified as a critical issue. His recent work explores zero-shot and few-shot learning approaches for cross-lingual applications, stance detection, and claim verification. The research spans multiple disciplines including computational linguistics, social computing, and AI ethics, with a growing focus on multimodal approaches and longitudinal model evaluation. 2024 OSNEM best survey award for work on session-based cyberbullying detection Zubiaga actively mentors PhD students, with Peiling Yi recently passing her viva (April 2025) and welcoming new PhD students Alaa Bazaid and Ali Khairallah. He serves as senior area chair for ACL 2025 and leads the HYBRIDS MSCA Doctoral Network. His work demonstrates a strong commitment to developing responsible AI systems that can detect and mitigate online harms while addressing critical issues of bias and fairness in computational approaches.

• Natural Language Processing
• Computational Social Science
• Misinformation Detection
• +7 more

Federico Costantini is an Associate Professor at the Department of Legal Sciences , University of Udine, where he teaches Legal Informatics and Digital Security Law . He serves as an ad interim Data Protection Officer (2018–2019) and is currently a member of two ethics committees: DI4A and DMIF . Research Interests : Data Protection, AI Ethics, Legal Informatics, IoT Forensics, and Distributed Ledger Technology (DLT). Key Projects : Distributed Ledger Technology Working Group , GDHRNet COST Action , and the Truthster initiative against disinformation. Article Trends : His publications focus on intersections of law and technology, particularly GDPR compliance, IoT forensic challenges, AI ethics, and blockchain applications. Labs & Teams : Collaborates with the Artificial Intelligence Laboratory for Human-Robot Collaboration and the Research Laboratory for New Media Normativity .

• Data Protection
• AI Ethics
• Digital Security Law
• +4 more