Aims and Scope
Misinformation is one of the most critical issues of recent years, which does harm to democracy, economics, and society. Despite all the attempts, traditional techniques are not powerful enough to address new challenges arising from the 4Vs (volume, variety, velocity, veracity) of Big Data. First, large volumes of data on social platforms are generated at unprecedented and ever- increasing scales. Existing misinformation detection techniques are designed for the conventional scale datasets, struggling to meet the requirements of scalability and storage. Second, social data and Web data involve a great variety of data formats in different modalities: texts, images, videos and arbitrary combinations of them. Third, data are generated in real time and continually arrives in the form of streams, facilitating the propagation of misinformation and fake news beyond control when they are detected. Fourth, the recent advances of AI-fabricated attacks like text synthesization, fake image generation and DeepFake videos create an additional layer of biases, noises, and abnormality in user behavior and content data. These challenges call for timely and robust techniques in monitoring, detecting, and mitigating misinformation by advancing topics in data management, data integration, data provenance, data quality, and stream processing.
Misinformation management techniques also need to work together with people, whose domain knowledge is on-par with the most complex AI techniques, and who must validate the automatic output for fairness and transparency. Recent human-in-the-loop platforms for such validation including Amazon Mechanical Turk and Snopes are growing in scale and expertise domains. At the same time, data management in these systems has become a new challenge with the expensive and slow-paced human labour. New data models and algorithms are needed to use human labour wisely and take into account the cognitive and physiological characteristics of the people involved.
This special issue seeks high-quality and original contributions that advance the concepts, methods, and theories of misinformation detection as well as address the mechanisms, strategies and techniques for misinformation interventions. All contributions should clearly address the knowledge gaps indicated in the literature and will be peer-reviewed by the panel of experts associated with relevant fields. We particularly welcome benchmarks, performance evaluation, testbeds for reproducibility validation.
Topics and Themes:
● Fake news, social bots, misinformation, and disinformation on social data
● Misinformation, opinion dynamics and polarization in social data
● Online misbehavior (scams, deception, and click-bait) and its relation to misinformation
● Information/Misinformation diffusion
● Credibility and reputation of news sources, social data, and crowdsourced data
● Fairness, accountability, transparency, and ethics in misinformation detection
● Fake reviewers and reviews
● Representation learning, patterns, and detection of bots, cyborgs, automated accounts
● Algorithmic bias and discrimination avoidance
● Augmented algorithmic models with bias-aware human-in-the-loop
● Misinformation monitoring, detection, and mitigation with real-time, large-scale, and streaming systems
● Initiatives against misinformation including news ecosystems, media practices, and computational moderations
● Misinformation on critical domains (politics, science, society)
● Predictive and visual analytics for decision-making on misinformation issues
Submission: 1st August 2020
First Round Notification: 1st October 2020
First Round Revisions: 1st December 2020
Second Round Notification: 1st February 2021
Final Submission: 1st March 2021
Publication: second quarter, 2021
The submission system will be open around one week before the first paper comes in. When submitting your manuscript please select the article type “VSI: Misinformation on the Web”. Please submit your manuscript before the submission deadline.
All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Please see an example here: https://www.sciencedirect.com/journal/science-of-the-total-environment/special-issue/10SWS2W7VVV
Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and the link to submit your manuscript is available on the Journal’s homepage.
Ecole Polytechnique Federale de Lausanne
Karl Aberer is a full professor for Distributed Information Systems at EPFL, Switzerland, since 2000. His research interests are on semantics in distributed information systems with applications in peer-to-peer search, data integration, semantic web, trust management and social, mobile and sensor networks. Karl Aberer received his Ph.D. in mathematics in 1991 from ETH Zurich. From 1991 to 1992 he was a postdoctoral fellow at the International Computer Science Institute (ICSI) at the University of California, Berkeley. In 1992 he joined the Integrated Publication and Information Systems institute (IPSI) of GMD in Germany, where he was leading the research division Open Adaptive Information Management Systems. From 2005 to 2012 he was the director of the Swiss National Research Center for Mobile Information and Communication Systems (NCCR- MICS). From 2012 to 2016 he was Vice-President of EPFL responsible for information systems. He is co-founder and CEO of LinkAlong, a startup established in 2017 providing analytics capabilities for open source documents. He is member of the editorial boards of VLDB Journal, ACM Transaction on Autonomous and Adaptive Systems and World Wide Web Journal and chairman of the ICDE Steering Committee.
University of Nicosia
Dr Ioannis Katakis is an Associate Professor, co-founder and co-director of the Artificial Intelligence Laboratory (ailab.unic.ac.cy) at the Computer Science Department of the University of Nicosia. He served multiple universities as a lecturer and a senior researcher. Dr Katakis was included in the J. Ioannides’ (Stanford University) list of top young Greek scientists based on the impact of their work. His research has been cited more than 6000 times by the scientific community. He published more than 50 papers in international conferences and scientific journals, organized workshops and special issues. He is an Editor at the journals `Information Systems’ and `Online Social Networks and Media’. Dr Katakis has extensive experience in European research projects where he participated as a Quality Assurance Coordinator and a Senior Researcher. His research interests include data science, machine learning, sentiment analysis, mining smart city data, social networks and computational social science.
Quoc Viet Hung (Henry) Nguyen
Dr. Quoc Viet Hung Nguyen is a senior lecturer and an ARC DECRA Fellow (Australia Discovery Early Career Researcher Award) in Griffith University. He earned his Master and PhD degrees from EPFL (Switzerland). His research focuses on Data Integration, Data Quality, Information Retrieval, Trust Management, Recommender Systems, Machine Learning and Big Data Visualization, with special emphasis on web data, social data and sensor data. He is also interested in Linked Open Data and Event-based Systems. He published several papers in top-tier venues such as SIGMOD, VLDB, SIGIR, KDD, WWW, ICDE, IJCAI, AAAI, ICDM, VLDBJ, and TKDE. He also served as a committee member of top-tier conferences such as WWW, CIKM, KDD, PAKDD, and DASFAA.
The University of Queensland
Dr. Hongzhi Yin works as a senior lecturer and an ARC DECRA Fellow (Australia Discovery Early Career Researcher Award) with The University of Queensland, Australia. He received his doctoral degree from Peking University in July 2014, and his PhD Thesis won the highly competitive Distinguished Doctor Degree Thesis Award of Peking University. His current main research interests include recommender systems, social data analytics and mining, network embedding and mining, time series data and sequence data mining and learning, chatbots, federated learning, topic models, deep learning and smart transportation. He has published 120+ papers and won 5 Best Paper Awards such as ICDE’19 Best Paper Award and ACM Annual Best Computing Award as the main author, and most of them have been published in reputed journals and top international conferences including VLDB Journal, ACM TOIS, IEEE TKDE, ACM TKDD, ACM TIST, ACM SIGMOD, ACM SIGKDD, VLDB, IEEE ICDE, AAAI, IJCAI, SIGIR, WWW, ICDM, ACM Multimedia and CIKM. He is currently directing the RSBDI (Responsible and Sustainable Big Data Intelligence) Lab. RSBDI Lab aims and strives to develop socially responsible and environmentally sustainable data mining and machine learning techniques with theoretical properties to better discover actionable patterns and intelligence from large-scale, networked, dynamic and sparse data. The research of RSBDI Lab is directly motivated by, and contributes to, applications in E-commerce and marketing, social informatics, urban traffic and information security.