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Title of the Special Issue/Thematic Track

Machine and Human Factors in Misinformation Management (VSI: IPMC2022 MISINFO)

- Damiano Spina (*), Senior Lecturer and DECRA Fellow, School of Computing Technologies, RMIT University, Melbourne, Australia. email: damiano.spina@rmit.edu.au

- Kevin Roitero, Postdoctoral Research Fellow, Department of Mathematics, Computer Science, and Physics, University of Udine, Udine, Italy. email: kevin.roitero@uniud.it

- Stefano Mizzaro, Full Professor, Department of Mathematics, Computer Science, and Physics, University of Udine, Udine, Italy. email: mizzaro@uniud.it

- Gianluca Demartini, Associate Professor, School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia. email: g.demartini@uq.edu.au

- Kalina Bontcheva, Full Professor, Department of Computer Science, The University of Sheffield, United Kingdom. email: k.bontcheva@sheffield.ac.uk

(*) Managing Guest Editor.

The rise of online misinformation is posing a threat to the functioning of the overall democratic process. Nowadays, it has been observed that there is an exponential growth of false information spread across the web and social network platforms; this expansion is also connected with the development of novel tools (e.g., large language models) that are able to process and generate large amounts of data. This has enabled the increase of large-scale counter-narratives and propaganda strategies in online communities, which have a major negative impact and can influence individuals and collective decision-making processes. To contrast this worrying trend, researchers are working on the development of data-driven and hybrid algorithmic methods with the aim of detecting misinformation and to control its spread. The proposed algorithms and solutions are complex and can be classified in different categories based on the underlying approach considered: fully automatic algorithms based on artificial intelligence, machine learning, and deep learning; human powered systems, either based on panels of experts or on crowdsourcing workers; and hybrid human-in-the-loop approaches, that try to fruitfully mix the above approaches. A better understanding on how humans and machines can effectively work together in the process of managing and fighting misinformation is needed.

The aim of this special issue is to accept submissions dealing with artificial, human, and hybrid techniques aimed at fighting the spread of misinformation.

Topics of interest include, but are not limited to:

- Predictive models to model and fight misinformation spread (e.g., trust and reputation models, formal models, online misinformation diffusion models, forecasting models).

- Machine learning, deep learning, transfer learning, reinforcement learning, graph based approaches, and probabilistic methods (e.g., classification, unsupervised / semi-supervised / supervised learning, applications, architectures, loss functions, training approaches) applied to fight misinformation.

- Infrastructures and resources for misinformation management (e.g., datasets, implementations, frameworks, architectures).

- Fairness, accountability, transparency, and safety of systems and processes to fight misinformation.

- Use of social media to study and combat misinformation online.

- Human computation and crowdsourcing methodologies to fight misinformation.

- Hybrid and multi-agent approaches to fight misinformation.

- Biases in artificial, human, and hybrid systems used to address misinformation.

- Adversarial approaches to misinformation (e.g., robustness of systems, automatic generation of misinformation).

- Information provenance and traceability.

- Filtering and recommendation systems for content dealing with misinformation (e.g., content-based filtering, collaborative filtering, recommender systems).

- User-centered (e.g., user experience, effectiveness, engagement) and system-centered (e.g., metrics, experimental design, benchmark) evaluation.

- Fighting Multimedia misinformation (text, audio, image, and video; deep fakes).

- Fighting Multi- and cross-lingual misinformation.

- Generation of explanations and explainable algorithms to deal with misinformation.

- Regulation, policies, and socio-economical perspectives on misinformation and approaches to fight misinformation.

- Influence and psychological aspects of misinformation.

- Social network analysis, influencer detection of misinformation, and fake news spreader profiling.

- Corpora, annotation, and test collections (including tools and resources) to build and evaluate systems and processes to fight misinformation.

Submit your manuscript to the Special Issue category (VSI: IPMC2022 MISINFO) through the online submission system of Information Processing & Management. https://www.editorialmanager.com/ipm/

Authors will prepare the submission following the Guide for Authors on IP&M journal at (https://www.elsevier.com/journals/information-processing-and-management/0306-4573/guide-for-authors). All papers will be peer-reviewed following the IP&MC2022 reviewing procedures. Please note IP&Ms strict no pre-print policy outlined in the author guidelines.

The authors of accepted papers will be obligated to participate in IP&MC2022 and present the paper to the community to receive feedback. The accepted papers will be invited for revision after receiving feedback on the IP&MC 2022 conference. The submissions will be given premium handling at IP&M following its peer-review procedure and, (if accepted), published in IP&M as full journal articles, with also an option for a short conference version at IP&MC2022.

Please see this infographic for the manuscript flow:https://www.elsevier.com/__data/assets/pdf_file/0003/1211934/IPMC2022Timeline10Oct2022.pdf

For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference

Link:
Machine and Human Factors in Misinformation Management - Information Processing and Management Conference - Knovel