Editorial

The current special issue of Neurocomputing was designed to encourage researchers from interdisciplinary domains working on multilingual social media analytics to think beyond the conventional way of combating online hostile posts. The special issue was primarily based on the theme of the First Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situation (CONSTRAINT) ( https://lcs2.iiitd.edu.in/CONSTRAINT-2021/ ), held with AAAI'2021. We invited a few good quality papers accepted in CONSTRAINT to submit an extended version. We also made the call open for the general audience. The special issue broadly focused on three major points: (i) Regional language: the offensive posts under inspection may be written in low-resource regional languages (e.g., Tamil, Urdu, Bangali, Polish, Czech, Lithuanian, etc.). (ii) Emergency situation: The proposed solutions should be able to tackle misinformation during emergency situations where, due to the lack of enough historical data, learning models need to adopt additional intelligence to handle emerging and novel posts. (iii) Early detection: Since the impact of misinformation during emergency situations is highly detrimental to the society (e.g., health-related misadvice during a pandemic can cost human lives), we encourage the solutions to be able to detect such hostile posts as early as possible after their appearance on social media.