Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.

Subject areas include data management issues as presented in the principal international database conferences (e.g. ACM SIGMOD, ACM PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining, information retrieval, internet and cloud data management, web semantics, visual and audio information systems, scientific computing, and organisational behaviour. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome.

All papers should motivate the problems they address with compelling examples from real or potential applications. Systems papers must be serious about experimentation either on real systems or simulations based on traces from real systems. Papers from industrial organisations are welcome.

Theoretical papers should have a clear motivation from applications. They should either break significant new ground or unify and extend existing algorithms. Such papers should clearly state which ideas have potentially wide applicability.

In addition to publishing submitted articles, the Editors-in-Chief will invite retrospective articles that describe significant projects by the principal architects of those projects. Authors of such articles should write in the first person, tracing the social as well as technical history of their projects, describing the evolution of ideas, mistakes made, and reality tests.
Technical results should be explained in a uniform notation with the emphasis on clarity and on ideas that may have applications outside of the environment of that research. Particularly complex details may be summarised with references to previously published papers.

We will make every effort to allow authors the right to republish papers appearing in Information Systems in their own books and monographs. 

Special Issue on Misinformation on the Web
Submission Date: 2020-08-01

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