Hate and Fake: Tackling the Evil in Online Social Media

Dear Colleagues,

Online platforms have become a big part of our everyday life. Expressions of thoughts and facts through social media (Facebook, Twitter, Instagram), discussion websites (Reddit), messaging services (WhatsApp, Snapchat), blogs, forums and online chats have been widespread. While new opportunities have been opened up to give a voice to people on the web, on the other hand, hate speech and fake news promote the evil related to social exclusion and misinformation. The interplay between hate speech and fake news is still growing; one fosters the other. Potential virality and presumed anonymity make the two phenomena dangerous and hurtful. This Special Issue aims to explore novel emerging technologies related to machine learning and natural language processing for countering the spread of hate and fake news on social media.

Two main questions underly this Special Issue:

• What are the differences and similarities related to hate speech and fake news between different languages, targets, topics and platforms?
• What are the main causes, intentional or unintentional, related to the spread of fake news and hate on social media?

The contributions submitted to the current Special Issue will provide substantial advancements to hate speech and fake news detection, both from a machine learning and linguistic perspective. We encourage authors to submit original research articles, case studies, reviews, comparative analyses, and theoretical and critical perspectives that are related, but not limited to, the following topics:

• Machine learning and natural language processing models for detecting hate speech and fake news
• Hate speech and fake news corpora: compilation, annotation and evaluation
• Modelling the impact of fake news in the dissemination of hate messages
• Cross-language and cross-cultural comparisons of hate speech and fake news
• Explainability of hate speech and fake news detection models
• Multi-lingual and multimodal detection of fake news and hate speech
• Fairness, accountability, transparency, and ethics in misinformation and hate speech detection
• Data and algorithmic bias in fake and hate speech detection
• Fake news and hate speech spreader identification
• Profiling of fake news and hate speech users
• Modelling the diversity of several types of hate speech: racism, sexism, etc…
• Modelling relationships and differences between hate speech and offensive/aggressive/vulgar language
• Fact-checking and trustworthiness of online sources
• Detection of different types of fake news: hoax, propaganda, trolling, and satire

Prof. Dr. Elisabetta Fersini
Dr. Manuel Montes-y-Gómez
Prof. Dr. Paolo Rosso
Guest Editors

Manuscript Submission Information

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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. Multimodal Technologies and Interaction 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 1800 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.