Digital technologies have brought myriad benefits for society, transforming how people connect, communicate and interact with each other. However, they have also enabled harmful and abusive behaviours, from interpersonal aggression to bullying and hate speech, to reach large audiences and for their negative effects to be amplified. As policymakers, civil society and tech companies devote more resources and time to tackle online abuse, there is a pressing need for scientific research that rigorously investigates how it is detected, moderated and countered.
Research into online abuse has expanded substantially over the past decade, and key advances have been made in many technically challenging tasks, especially in relation to detecting, categorising and measuring undesirable online content. At the same time, research remains siloed, and many disciplinary and methodological differences intersect the field. These differences limit the exchange of ideas and insights and slow advancement, making it hard for researchers to identify and address new and impactful issues. Noticeably, to-date cutting-edge research on online abuse has not been brought together in a single journal issue. In this special issue we aim to bridge the gap in online abuse research between technical disciplines, such as machine learning (ML), natural language processing (NLP) and statistics, and social scientific disciplines, such as sociology, cultural studies, the digital humanities and politics. We also invite contributions from non-academic partners, such as civil society activists and policymakers.
This special issue comes at a pivotal moment as AI is increasingly deployed to moderate and monitor abusive content online on most of the major tech platforms. Bringing together a diverse range of perspectives will help the field to mature, both by generating new insights and knowlege, and also by providing a moment to 'pause' about where the field is, helping to identify future directions of work. We invite submissions on all topics related to the study of online abuse, especially research which engages with issues relating to the ethics, explainability, fairness and use of content detection systems.
First Monday is one of the first journals dedicated to studying the internet, is fully online and open-access, and provides extensive editorial support to authors at no cost. There are no submission fees and no other associated costs. The journal’s readership is large, international, and interdisciplinary, and it has a strong track record of publishing work relating to online abuse, extremism and hate. The readership goes beyond academia, including journalists, politicians, and policy-makers. This is an exciting opportunity for research papers within individual fields to reach a wider audience, to engage with other scientific communities and to advance the study of online abuse.
Submission deadline: February, 2020
To submit a manuscript, please go to the submission portal: https://easychair.org/conferences/?conf=siwoah2021
We welcome all submissions to the special issue, including both extensions of work that has already been published in workshops and work that is currently unpublished. We are particularly interested in substantially expanded versions of papers presented at the Workshop on Online Abuse and Harms (previously: Abusive Language Online - ALW) and papers that expand on work presented at the workshop.
First Monday’s readership is interdisciplinary and even goes beyond academia, including journalists, politicians, and policy-makers. Therefore, readability is paramount: in the words of the editor, submissions should strive for a “highly accessible style, with an emphasis on avoiding academic gobbledygook and pretension.” Since First Monday is an online-only journal, there are no hard limits on the length of papers; they should be as long as necessary to thoroughly explore their topic while maintaining a clear and concise writing style. If you are keen to submit but unsure about how to tailor your work for a social scientific audience, we are more than happy to help.
When reviewing papers for the special issue we will prioritize those that have an interdisciplinary approach. We encourage you to carefully consider the implications and impacts of your work and findings, and find ways to make it more interdisciplinary and appealing to a broader audience beyond your own research community. For instance, for the NLP community, let’s say your workshop paper was about a deep neural network model that improves abuse detection by X%. Some of the ways you can consider expanding your work are: a substantive discussion around the legal or public policy aspects of your solution grounded in literature in those fields, or a well-thought-out plan for how your solution can be deployed within the digital media landscape, or a new downstream study on any undesirable biases your model may have along various demographics or an in-depth analysis of the linguistic constructs that your model picks up on and how they relate to existing linguistic literature in the area. In order to do this, we highly encourage you to seek out new collaborators and approaches from fields such as legal studies, public policy, ethics, digital media studies, sociology, psychology, and linguistics.
We ask that you follow the style guides for First Monday in drafting your submission.
Wendy Hui Kyong Chun, Simon Fraser University
Darja Fiser, Ljubljana University
Vinodkumar Prabhakaran, Google
Bertram Vidgen, The Alan Turing Institute
Rob Voigt, Northwestern University
Zeerak Waseem, University of Sheffield
Jacqueline Wernimont, Dartmouth University