The Digital Immune System: AI-Driven Detection and Mitigation of Online Harms
A special issue of AI (ISSN 2673-2688).
Deadline for manuscript submissions: 15 July 2026 | Viewed by 79
Special Issue Editors
Interests: abusive text detection; affect and sentiment analysis; affective computing (AC); Ainu language; artificial intelligence (AI); automatic cyberbullying detection; computational linguistics (CLs); corpus linguistics; emotional intelligence; human–computer interaction (HCI); large language models; linguistics; natural language processing (NLP); offensive text detection; philosophy of emotions; pragmatics
Special Issues, Collections and Topics in MDPI journals
Interests: knowledge acquisition; emotions; common sense; ethics; cognition
Special Issues, Collections and Topics in MDPI journals
Interests: natural language processing (NLP) and its applications; safety and transparency of large language models (LLMs)
Special Issue Information
Dear Colleagues,
Misinformation, hate speech, and other malicious content proliferate at an unprecedented scale, overwhelming manual moderation and polluting our digital commons. To combat this, the global society must move beyond reactive content removal towards creating proactive, adaptive, and resilient AI frameworks.
This Special Issue, “The Digital Immune System”, invites novel research on AI systems that can autonomously detect, analyze, and mitigate online harms. We invite papers that propose systems, like their biological counterparts, which can identify novel threats, adapt to adversarial manipulations, and contribute to the overall health of the information ecosystem.
We invite submissions on topics including, but not limited to:
- Multimodal detection of deepfakes, hate speech, and propaganda.
- Adversarial robustness and the generation of counter-narratives.
- Low-resource learning for emerging threats and languages.
- Causal inference and graph-based methods for tracking influence campaigns.
- Explainable AI (XAI) for transparent and fair moderation decisions.
- Architectures for continual learning and mitigation in dynamic environments.
We encourage interdisciplinary work that engineers the foundational AI technologies for a safer, more trustworthy, and resilient digital world.
Prof. Dr. Michal Ptaszynski
Dr. Rafal Rzepka
Prof. Dr. Hisami Suzuki
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- disinformation/misinformation/malinformation
- hate speech/toxic language/cyberbullying
- propaganda analysis
- adversarial learning/adversarial defense
- causal inference/explainable AI (XAI)/interpretability
- content moderation/counter-narrative generation
- AI trust and safety
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