Detecting Harmful Content on Online Platforms: What Platforms Need vs. Where Research Efforts Go

Author:

Arora Arnav1ORCID,Nakov Preslav1ORCID,Hardalov Momchil2ORCID,Sarwar Sheikh Muhammad1ORCID,Nayak Vibha3ORCID,Dinkov Yoan2ORCID,Zlatkova Dimitrina2ORCID,Dent Kyle3ORCID,Bhatawdekar Ameya3ORCID,Bouchard Guillaume3ORCID,Augenstein Isabelle1ORCID

Affiliation:

1. Checkstep Research, UK

2. Checkstep Research, Bulgaria

3. Checkstep, UK

Abstract

The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms, including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self-harm, and many others. Online platforms seek to moderate such content to limit societal harm, to comply with legislation, and to create a more inclusive environment for their users. Researchers have developed different methods for automatically detecting harmful content, often focusing on specific sub-problems or on narrow communities, as what is considered harmful often depends on the platform and on the context. We argue that there is currently a dichotomy between what types of harmful content online platforms seek to curb, and what research efforts there are to automatically detect such content. We thus survey existing methods as well as content moderation policies by online platforms in this light and suggest directions for future work.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

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