Information Processing & Management is devoted to refereed reporting of:

1. Basic and applied research in information science, computer science, cognitive science and related areas that deals with: the generation, representation, organization, storage, retrieval, and use of information; the nature, manifestations, behavior, and effects of information and knowledge; communication and distribution of information and knowledge; and human information behavior.

2. Experimental and advanced processes, related to: information retrieval (IR); digital libraries; knowledge organization and distribution; digitized contents - text, image, sound and multimedia processing; and human-computer interfaces in information systems. Implementations in information retrieval systems and a variety of information systems, networks, and contexts. Related evaluation.

3. Management of information resources, services, systems and networks, and digital libraries. Related studies of the economics of information and the principles of information management.

The aim is to provide an international forum for advanced works and critical analysis in these interdependent and interdisciplinary areas. Invited are original papers and critical reviews of trends reporting on:
• Progress in the theory, principles, and procedures in information processing, particularly involving information retrieval; search engines; knowledge and distributed intelligence; information representation, classification, extraction, filtering and summarization; question answering; information navigation, browsing and visualization; and human-computer interaction in information systems.
• Research on the formal characteristics and properties of information and knowledge and the associated processes of communication among humans and between humans and machines. Includes studies of human information needs, seeking, searching, and use; and bibliometric and infometric studies of the structural and statistical properties of information artifacts.
• Modeling and evaluation of information systems performance, particularly of information retrieval systems, knowledge systems, and digital libraries. Studies of their effectiveness, efficiency, value, or impact.
• Studies in management and economics of information and information systems. Use of information for decision making and problem solving.
• Studies in information policies. Data and issues relevant to information policies on organizational, national, and international levels. Derivation and use of information indicators.

Special Issue on Shared Experiences during the COVID-19 Pandemic and Approaches to Global Recovery
Submission Date: 2022-02-01

The COVID-19 pandemic has been exacerbated by a so-called ‘infodemic’, which includes intentional attempts to disseminate false information to undermine the global response to the health crisis. In a broad sense, the infodemic consists of major turbulences in global information processing and management as it generated a tsunami of disinformation and misinformation from various sources. The false information has posed an extreme challenge to keep social and economic life going on. Now the world is beginning to see the light at the end of the pandemic tunnel, although no one can truly predict when we will beat back the waves of COVID-19 infections. At this critical moment, it is our responsibility to review our shared experiences and search for clues that advance the well-being of global communities. To recover from the pandemic and the infodemic, it is imperative to understand how the information is processed and beliefs are formed, offering clues to comprehend not only the current COVID-19 pandemic, but also the unknown but foreseeable future pandemics.

This special issue is dedicated to analyses of how our shared experiences in the global health crisis offer approaches to the global recovery, in which the pandemic impact on information processing, emotional toll, and human interactions will be particularly scrutinized.

Information Processing & Management (IP&M) is a leading international journal in information sciences published by Elsevier with an impact factor of 6.2. It publishes cutting-edge original research at the intersection of computing and information science concerning theory, methods, or applications in a range of domains, including but not limited to advertising, business, health, information science, information technology marketing, and social computing. This special issue will follow the IP&M tradition of encouraging interdisciplinary research and recognizes that diversity in voices, ideas, and experiences in our research fields can make our scholarship more meaningful. Thus, the special issue particularly welcomes manuscripts from researchers in the areas of communication, education, psychology, and other social sciences, in addition to computer and information sciences.

Keywords (in alphabetical order)
- Artificial intelligence
- Global or public health
- Health literacy and impact
- Health communication
- Health intervention
- Human interactions
- Information and learning systems
- Information and media literacy
- Information processing
- Machine learning
- Misinformation, disinformation, and infodemic
- Media effect and impact
- Psychological support and mental health
- Risk assessment and crisis management
- Social media and mobile devices
- Vaccine hesitation or confidence 

Special Issue on Multi-modal Computing for Biomedical Diagnosis and Analysis
Submission Date: 2022-02-28

Potential topics include, but are not limited to:

- Shallow and deep learning-based multi-modal computing for medical imaging
- Novel theories and applications of multi-modal biomedical fusion for accurate clinical diagnoses
- Deep learning-based image processing and diagnostic analysis of multi-modal medical imaging data, such as early diagnosis of human diseases, accurate lesion segmentation, etc.
- Incomplete multimodal computing in disease diagnosis and health communications
- Self-supervised, semi-supervised, or unsupervised learning methods for biomedical imaging data
- Generative adversarial networks for biomedical data augmentation and processing
- Adversarial learning for security, privacy, and trust on radiological images or other health-related data
- Collection, analysis, and mining of large-scale multi-modal biomedical databases
- Visualization and understanding of multi-modal biomedical data in health communication, disaster communication, and information seeking
- Surveys/review papers on multi-modal/multi-view learning/biomedical engineering 

Special Issue on Fair and Explainable Information Access Systems for Social Good
Submission Date: 2022-03-30

We solicit different types of contributions (research papers, replicability and reproducibility studies, resource papers) on fairness and explainability. Of particular interest are case studies of successful practices in domains with large societal impact (e.g., healthcare, insurance, lending, news, educational systems), but also with large financial impact (e.g., e-commerce sites, travel booking sites, job search sites, dating sites, etc.). Studies that try to understand how algorithmic decisions fulfill some of the 17 sustainable development goals proposed by the United Nations (see https://sdgs.un.org/goals) in its 2030 agenda are also encouraged, such as goal 3 (health and well-being), goal 5 (gender equality), or goal 10 (reduced inequalities), focused but not limited to the following areas. If in doubt about the suitability, please contact the Guest Editors.

Designing fair and explainable Information Access systems:
- How to define fairness or explainability in Information Access systems
- Impact of these definitions when systems are tailored for social good
- Analysis of constraints to implement these systems, such as collecting proper data, addressing biases or inequalities in the data, using simulations or synthetic data, etc.
- Novel user models for explainable or fair information access using heterogeneous content, audio-visual content, or crowdsourcing techniques, while exploiting the impact of user emotion, personality, context, and individual cognitive differences
- Analysis of interactional and presentational aspects of explanation and fairness in Information Access systems (multi-modality, level of interactivity)
- Evaluating fair and explainable Information Access systems:
- How to evaluate fairness or explainability in Information Access systems
- Impact of these models when systems are tailored for social good
- Comparison of evaluation measurements when assessing these systems, such as defining objective metrics, user studies, understanding settings of offline experiments, influence of multiple stakeholders in evaluation, etc.
- Explore domains with large societal or financial impact: healthcare, insurance, lending, news, educational systems, e-commerce sites, travel booking sites, job search sites, dating sites, etc.
- Measuring UX and design aspects of Information Access systems for social good
- Interventions towards fair and explainable Information Access systems:
- How to modify current Information Access systems to achieve some level of fairness or explainability
- Impact of these interventions when systems are tailored for social good
- Discussion of preconditions needed or achievable post hoc analyses when modifying these systems, such as designing protocols to mitigate biases, exploratory analysis on explainable systems, etc.
- Causal and counterfactual inferences for fairness, explanation, and transparency
- Novel techniques in adversarial machine learning, graph learning, deep neural networks, etc. to improve explainability and fairness of Information Access systems