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Arjen van DalenView profile

Professor

• Political Communication
• Journalism
• Algorithmic Gatekeeping
• +5 more

Arjen van Dalen is a Professor at the Department of Political Science and Public Management, Faculty of Social Sciences, University of Southern Denmark. He is affiliated with the Digital Democracy Centre and the Journalism VIP (Villum Investigator Programme). His research focuses on political communication, journalism, and quantitative/mixed methods. Education: Ph.D. in Political Science MSc in Communication Research Interests: Political communication Journalism Algorithmic gatekeeping AI in media Economic news Media trust Article Trends: His recent publications analyze algorithmic gatekeeping, AI's impact on journalism, political microtargeting, economic news consumption, and longitudinal trust in media. Key themes include automated news production, democratic implications of AI, voter behavior, and media credibility under algorithmic influence. Scientific Awards: Goldsmith Book Prize (2016) DFF-Research Project Grant (2017) Top faculty paper award from ICA Journalism Studies Section (2017) Best Book Award from The International Journal of Press/Politics (2018) Best paper in Ethics & Socially Responsible Marketing Track (2018) Teaching & Supervision: Teaches courses on Political Communication, Digital Practice, and The History, Values and Ethics of Journalism Supervised Master's Thesis by Kristiansen, M. B. Projects: Algorithmic Gatekeepers: The Democratic Consequences of News Selection Algorithms Citizens, Digitalization, and AI (CDAI) Microtargeting of Danish Voters

Sophia AnaniadouView profile

Professor

• Natural Language Processing
• Text Mining
• Artificial Intelligence
• +9 more

Sophia Ananiadou is a Professor in the School of Computer Science at the University of Manchester , where she serves as Director of The National Centre for Text Mining (NaCTeM) , the world’s first publicly funded text mining center. She leads interdisciplinary research at the intersection of Natural Language Processing (NLP) , Artificial Intelligence (AI) , and Machine Learning , with applications spanning biomedicine , public health , environmental science , law , and social sciences . Her work emphasizes computational terminology , information extraction , and neurosymbolic methods , resulting in tools like TerMine and Acromine that are widely adopted in academia and industry. Education PhD in Computational Linguistics, UMIST DEA in Linguistics, Paris VII, Jussieu DEA in Literature, Paris IV, Sorbonne BA in Language and Linguistics, University of Athens Research Interests focus on scalable text mining solutions, including automatic term recognition , event extraction , emotion detection , and resource building . Her recent publications highlight applications in large language models (EmoLLMs) , misinformation analysis , occupational exposure annotation , and argument mining , reflecting a trajectory toward integrating Affective Computing and Generative AI into biomedical and social science domains. Scientific Awards IBM UIMA Innovation Award (3x) Daiwa Award Next Big Thing Prize (2018) Collaborations include leadership roles in the Alan Turing Institute , AIST Artificial Intelligence Research Center , and the OpenMinTed H2020 project . Her tools support organizations like NICE , Cochrane , and Pfizer , and she co-founded the ACL SIGBioMed community to advance biomedical NLP.

Borja Ventura SalomView profile

Associate Professor

• Journalism
• Digital Communication
• Political Communication
• +5 more

Borja Ventura Salom is an Associate Professor in the Journalism and Narrative Department at Universidad CEU San Pablo's School of Communication. He holds a doctorate (awarded September 6, 2021) and maintains an active dual career as both an academic researcher and working journalist specializing in politics and digital media. His research focuses on contemporary challenges in journalism, particularly digital communication, political narratives, and disinformation. Ventura Salom has developed expertise in fact-checking methodologies, election coverage analysis, and the evolution of journalistic practices in the digital age. His work demonstrates particular interest in Iberian political communication contexts, examining both Spanish and Portuguese media landscapes. Analysis of his recent publications reveals a strong emphasis on practical journalism challenges in the digital era, including social media platforms as news distribution channels, electoral fact-checking mechanisms, and innovative approaches to media content analysis. His research bridges academic theory with practical journalism applications, often examining specific case studies from contemporary Spanish media. As an educator, Ventura Salom contributes to journalism and narrative studies within the university's communication programs. His professional background as an active journalist covering politics and digital issues informs his academic work and teaching approach, providing students with real-world insights into contemporary media challenges.

• Information Integrity
• Health Communication
• Message Processing
• +2 more

Camille Saucier is an Assistant Professor in the Department of Communication at Clemson University's College of Behavioral, Social and Health Sciences. As a Faculty Affiliate of the Media Forensics Hub, she focuses on person-centered communication strategies to combat misinformation and enhance health messaging receptivity through techniques like self-affirmation, narratives, and parasocial relationships. Ph.D. in Communication from Northwestern University M.S.C. in Communication Management from University of Southern California B.A. in Psychology from University of Southern California Her research examines motivational factors influencing fact-checking behaviors and methods to promote information integrity. She teaches courses in persuasion (COMM 3050) and public communication (COMM 3080). Contact: Office 415 Strode Tower, Phone 864-656-1567, Email csaucie@clemson.edu.

Scott ReedView profile

Professor

• Pharmacogenomics
• Artificial Intelligence
• Nanoscience
• +4 more

Scott Reed is a Professor in the Department of Chemistry within the College of Liberal Arts and Sciences at the University of Colorado Denver. His office is located in Room 4131 of the Science Building, with mailing address at Department of Chemistry, Campus Box 194, P.O. Box 173364, Denver, CO 80217-3364. Dr. Reed earned his Ph.D. and M.S. from the University of Oregon in 2001 and 1996 respectively, and completed his undergraduate studies with a B.A. from Bard College, NY in 1994. His primary research interests span pharmacogenomics, artificial intelligence, nanoscience, chemical synthesis, and green chemistry. His work particularly focuses on developing computational tools to identify genetic variants that explain adverse drug reactions (ADRs). His research program has two main goals: creating an online tool that identifies specific ADRs and their causative drugs, and developing another tool that generates candidate genetic variants explaining specific ADRs. These tools facilitate rapid hypothesis testing about pharmacogenomics causes of harmful drug side effects. Dr. Reed's publication record shows a strong interdisciplinary approach with recent work (2022-2024) emphasizing AI/ML applications to chemical and pharmacological problems. His research spans from fundamental chemical synthesis and nanoscience to computational biology and AI-driven drug safety analysis. He is actively involved in educational initiatives including a course on "Vibe Coding," a Neural Network Tutorial available on YouTube, and the development of a CNN-in-Excel project (Tetris_CNN) that demonstrates convolutional neural networks within spreadsheet applications. He has also created a Chemistry Knowledgeable GPT to assist with molecular informatics. Dr. Reed maintains an active research laboratory with projects including the Advancing AI-Assisted Molecular Synthesis Challenge, focusing on applying artificial intelligence to chemical synthesis problems.

• Public Libraries
• Cultural Policy
• Media and Information Literacy
• +4 more

Dr. Hanna Carlsson is a Senior Lecturer at Linnaeus University's Faculty of Arts and Humanities , Department of Cultural Sciences. She leads the master's program in Library and Information Science and co-founded the research group 'Forskargruppen för biblioteks- och kulturpolitisk forskning'. Her work bridges public library studies , cultural policy analysis , and media literacy education . Senior Lecturer, Linnaeus University (2025) Program Director, MA in Library & Information Science (2025) PhD, Lund University (2013), thesis: 'The New City's Library: Technology, Reason, and Emotion in the Design of Knowledge and Experience Cities'. Her research examines democratic roles of public libraries in political crises, focusing on: Cultural war dynamics affecting library services Policy implementation challenges Information infrastructure in education Conflicts in cultural policy governance Digital innovation hesitancy Reading promotion strategies Recent publications (2023-2025) analyze: Far-right information activities targeting libraries Post-political shifts in Swedish cultural policy LGBTQ+ programming controversies KNIME workflows in library research Media literacy in compulsory education She collaborates with researchers from Lund University, Royal Danish Library, and Kristianstad University on projects like: InKuiS : Innovative Cultural Entrepreneurship ReSource : National Research School for Digital Culture Trolling the Library : Digital threats to democratic missions

Owen LoView profile

Assistant Professor

• Educational Technology
• Digital Media
• Journalism
• +7 more

Owen Lo is an Assistant Professor in the Department of Educational and Counselling Psychology, and Special Education at the University of British Columbia . His work bridges educational technology , digital media , and public health communication , focusing on misinformation , platform governance , and social media dynamics . His research explores information ecosystems during crises, including the impact of Meta’s news ban in Canada , anti-intellectualism in pandemic communication , and global platform governance . He examines how traditional and social media shape public understanding of health , politics , and security . Key themes in his publications include digital democracy , public health messaging , and media disruption . While no formal awards or student advising details are listed, his work intersects education , technology , and societal challenges .

Xingyi SongView profile

Lecturer

• Natural Language Processing
• Computational Social Science
• Sentiment Analysis
• +3 more

Dr. Xingyi Song serves as a Lecturer in Computational Media Analysis at the Department of Computer Science, University of Sheffield, where he is an active member of the Natural Language Processing research group and the GATE team. His academic career began with MSc and PhD studies at the same institution, followed by professional roles as a machine translation specialist at Iconic Translation Machine (2015-2016) and Research Associate for EU-funded projects including Kconnect, Knowmak, and Risis2 (2016-2021). His research spans multiple critical domains in computational linguistics and social impact applications. Primary interests include Natural Language Processing with specialized focus on sentiment analysis, computational social science methodologies, and bio-medical text processing. His work demonstrates strong interdisciplinary connections between AI techniques and real-world challenges in public health communication, misinformation detection, and social media analysis. Analysis of his recent publications reveals a clear trajectory toward multimodal and large language model applications for social good. Key trends include fake news detection using cross-modal approaches, temporal analysis of vaccine hesitancy narratives during the pandemic, and innovative applications of LLMs for topic modeling and data quality enhancement. His work consistently bridges theoretical NLP advancements with practical societal applications. Dr. Song currently leads the ASIMOV project (Innovate UK, 2024-2025, £142,691) as Principal Investigator and serves as Co-Principal Investigator for the vera.ai project (Horizon Europe, 2022-2025, £776,703), demonstrating significant research leadership in AI verification systems. His collaborative network extends across multiple EU projects and interdisciplinary teams focused on misinformation detection and social media analysis. As part of the Natural Language Processing research group and GATE team, Dr. Song contributes to Sheffield's internationally recognized expertise in language technologies. His work integrates closely with healthcare applications through collaborations with medical researchers and NHS partners, particularly in mental health text analysis and vaccine communication studies.

Xi WangView profile

Lecturer

• Natural Language Processing
• Conversational AI
• Retrieval-Augmented Generation
• +3 more

Dr Xi Wang is a Lecturer in the School of Computer Science at the University of Sheffield, specializing in Natural Language Processing. His research focuses on conversational AI systems, personalization techniques, retrieval-augmented generation, and fairness in AI applications. He actively contributes to the school's Natural Language Processing research group and maintains strong industry connections through publications in top-tier conferences. Dr Wang earned his PhD from the University of Glasgow under Prof. Iadh Ounis and Prof. Craig Macdonald, where he developed review text-based recommender systems. He subsequently conducted postdoctoral research at University College London's web intelligence group with Prof. Emine Yilmaz, focusing on knowledge-augmented task-oriented dialogue systems. His research spans critical NLP domains with emphasis on: Conversational AI: Building dialogue systems that handle complex user interactions Retrieval-Augmented Generation: Integrating external knowledge into language models Personalization: Developing adaptive systems that respect user context Fairness: Addressing bias in recommendation and dialogue systems Machine Learning: Applying advanced techniques to information retrieval challenges Analysis of Dr Wang's 15 most recent publications (2021-2025) reveals a clear trajectory toward more sophisticated conversational systems. His work increasingly integrates retrieval methods with generative models, particularly in recommendation contexts. Key trends include cold-start problem solutions, bias mitigation in conversational agents, and novel approaches to clarification question handling. The research demonstrates growing emphasis on fairness and knowledge grounding in dialogue systems. Dr Wang received a Google research workshop grant ($20,000) in 2022 for developing knowledge-enriched task-oriented dialogue systems during his postdoctoral work at UCL with Prof. Emine Yilmaz. While no current advisees are listed, Dr Wang's grant activity demonstrates research leadership. His Google-funded project focused on action-oriented dialogue systems, showing capability in securing competitive research funding. The Natural Language Processing research group at Sheffield provides his primary academic home, where he contributes to the school's strengths in speech and language technologies. His recent publications indicate active collaboration with researchers across multiple institutions including UCL, University of Glasgow, and international partners.

Cass Zhixue ZhaoView profile

Lecturer

• Natural Language Processing
• Model Interpretability
• Model Hallucination
• +7 more

Dr. Cass Zhixue Zhao is a Lecturer (Assistant Professor) in Natural Language Processing at the Department of Computer Science, University of Sheffield. She serves as Student Services Liaison (Library, ELTC) and leads research in model interpretability, hallucination, and compression. Her work bridges theoretical and applied NLP, with a focus on trustworthy and efficient large language models (LLMs). PhD from University of Sheffield (funded by university scholarships) Postdoc researcher (2020–2024) on model interpretability and responsible AI Co-PI for ExU project (2023–2025), funded by European Media and Information Fund (€399,926) Her research prioritizes enabling trustworthy NLP models through: Model explanation frameworks Hallucination and bias mitigation Efficient model compression techniques Knowledge editing detection and reversal Multilingual disinformation analysis Scientific text-to-image generation Recent publications (2025–2024) demonstrate expertise in: Formal logic reasoning for LLMs Security risks in knowledge editing Multilingual hallucination consistency FAITH metrics for multilingual models Pruned model hallucination analysis Visual knowledge reasoning pipelines Dr. Zhao actively supervises PhD students, with a UKRI-funded position available in 2026. She contributes to program committees (AAAI, ACL, EMNLP) and maintains active collaborations with researchers like Nikolaos Aletras, Paul Youssef, and Lucie Flek.

Jonathan MayView profile

Research Professor

• Natural Language Processing
• Machine Translation
• Computational Linguistics
• +4 more

Jonathan May is a Research Associate Professor in the Department of Computer Science at the University of Southern California and a Principal Scientist at the Information Sciences Institute. He serves as Director of the Center for Useful Techniques Enhancing Language Applications Based on Natural And Meaningful Evidence (CUTELABNAME). Dr. May teaches courses including CSCI 544: Applied Natural Language Processing and CSCI 662: Advanced Natural Language Processing through Fall 2024. Dr. May's research spans natural language processing, machine translation, and computational linguistics with significant contributions to cross-lingual processing, dialogue systems, and language model development. His work bridges theoretical advances with practical applications in journalism, negotiation systems, and multimodal understanding. Recent research focuses on large language models, with particular emphasis on efficiency, personalization, grounded generation, and exploring the limits of what language models can understand about physical phenomena and human communication. Analysis of Dr. May's recent publications (2024-2025) reveals a strong focus on advancing large language models, with particular emphasis on efficiency (e.g., 'Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length'), personalization (e.g., 'Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning'), and grounded generation (e.g., 'NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews'). A notable trend is the integration of reflection and memory mechanisms into language models, as well as exploring multimodal understanding across domains from food science to negotiation. Outstanding Paper Award at EMNLP 2024 for 'Are Large Language Models Capable of Generating Human-Level Narratives?' Outstanding Paper Award at NAACL 2018 for 'Recurrent Neural Networks as Weighted Language Recognizers' Best Demo Award at ACL Demo Sessions 2018 for 'Out-of-the-box Universal Romanization Tool uroman' Dr. May leads CUTELABNAME, which has produced numerous software tools including uroman (Universal Romanizer), BotEval, and SPOLIN. The lab maintains an active research agenda with regular publications at top NLP conferences and collaborations across academia and industry. His work demonstrates both theoretical rigor and practical applicability, with many techniques being adopted in real-world NLP systems.

Hye Min KimView profile

Assistant Professor

• Social Media for Public Health Promotion
• Communication Technologies in Health and Wellbeing
• Health Behavior and Persuasion

Hye Min Kim is an Assistant Professor of Communication at the University of Massachusetts Boston. Her research focuses on the intersection of communication technologies, public health promotion, and health behavior persuasion. PhD in Communication from University of Southern California (2022) MA in Communication from Yonsei University (2016) BA in Mass Communication from Yonsei University (2014) Her research examines how digital communication tools address public health challenges through computational analysis of social media and experimental methods. Key areas include health stigma reduction, vaccination promotion, and misinformation management. Recent work analyzes anti-stigma campaigns using m-health apps, partisan framing of pandemic policies, and network strategies for health communication. Articles span topics from HPV vaccine messaging to e-cigarette regulation debates. Kim employs mixed-method approaches combining behavioral experiments, meta-analysis, and large-scale social media data analysis to understand technology's role in health decision-making among underserved populations.

• Political Participation
• Political Socialization
• News Consumption
• +5 more

Cato Waeterloos is a postdoctoral researcher at the Institute for Media Studies (IMS) of KU Leuven , focusing on the intersection of new media and democratic participation. Her work examines how social media reshapes political engagement, news consumption, and citizenship literacy. Education: Master's in Communication Sciences (2018, Ghent University) PhD in Communication Sciences (2022, Ghent University) Research Interests: Waeterloos investigates political socialization in digital environments, analyzing how social media platforms influence civic literacy, news consumption patterns, and non-traditional participation forms. Her work bridges theoretical frameworks with empirical studies on youth political engagement. Project Involvement: She led a 4-year BOF-funded project on social media's role in political participation and contributed to Ghent University's 'Municipality Without a Town Hall' initiative. Currently, she explores influencer-driven political knowledge development in the iBOF POLKNOW project. Key Trends in Publications: Her recent work spans political knowledge acquisition, fact-checking dynamics, and platform-specific participation mechanisms. Collaborative studies with Ghent University teams emphasize cross-generational and cross-platform analysis, particularly focusing on digital-native youth. Labs & Collaborations: Based at the Lab for Media, Information & Influence , she collaborates with Prof. Dr. Michaël Opgenhaffen and Prof. Dr. Desiree Schmuck on EU-funded research, integrating digital tools for enhanced civic participation.

• Distributed Optimization
• Stochastics
• Ergodicity
• +6 more

Behrouz Touri is an Associate Professor in the Industrial and Enterprise Systems Engineering department at the University of Illinois at Urbana-Champaign. His research focuses on distributed optimization, stochastic systems, and information sharing mechanisms in networked environments. Academic Rank: Associate Professor Affiliation: College of Engineering, University of Illinois at Urbana-Champaign Research Interests include distributed optimization algorithms, ergodicity in stochastic chains, and modeling of information/opinion dynamics with applications in decentralized systems. His recent work addresses gradient descent with delayed updates, imperfect information sharing, and algorithmic fact-checking frameworks. Scientific Outputs demonstrate expertise in convex optimization (93% relevance), distributed computing (100% coverage), and stochastic modeling (79% emphasis). The 2024-2025 publications highlight convergence analysis and unreliability learning in distributed settings. Awards : Donald P Eckman Award (2018)

Jevin D. WestView profile

Professor

• Misinformation Studies
• Generative AI
• Sociology of Science
• +2 more

Jevin D. West is a Professor and Associate Dean for Research at the University of Washington Information School , where he co-founded the Center for an Informed Public . His computational research examines the spread of misinformation , sociology of science , and generative AI's impact on discourse . He advocates for open educational initiatives like his Calling Bullshit course and data reasoning games . Recent research trends span: Misinformation psychology (PNAS 2025) Network science applications (arXiv 2025) Antivaccine communities (PNAS Nexus 2024) AI ethics (Science Advances 2021) Grants include: $5M Knight Foundation for disinformation summer institute $1.1M NSF for rural library misinformation programs $293K NSF for academic recommendation systems He serves on editorial boards for JASIST and Science Advances , directs the Center for an Informed Public , and collaborates with institutions like PNAS and Nature Human Behavior .