ALW1: 1st Workshop on Abusive Language Online to be held at the annual meeting of the Association of Computational Linguistics (ACL) 2017 (Vancouver, Canada), August 4th, 2017.

The workshop will take place in the Mackenzie room at the Westin Bayshore, Vancouver.

Important dates

Submission due: May 2nd

Author Notification: May 18th

Camera Ready: May 26th

Workshop Date: August 4th


The last few years have seen a surge in abusive online behavior, with governments, social media platforms, and individuals struggling to cope with the consequences and to produce effective methods to combat it. In many cases, online forums, comment sections, and social media interaction have become sites of bullying, scapegoating, and hate speech. These forms of online aggression not only poison the social climate of the online communities that experience it, but can also provoke physical violence and harm.

Addressing abusive language necessitates a multi-disciplinary approach that requires knowledge from several fields, including (but not limited to): media studies, natural language processing (NLP), psychology, sociology, law, gender studies, communications, and critical race theory. NLP, as a field that directly works with computationally analyzing language, is in a unique position to develop automated methods to analyse, detect, and filter abusive language. By working across disciplinary divides, researchers in all these fields can produce a comprehensive approach to abusive language that blends together computational, social and legal methods.

In this one-day workshop, we therefore want to bring researchers of various disciplines together to discuss approaches to abusive language. The workshop will include invited speakers and panelists from fields within and outside of NLP, as well as submitted papers from researchers across all areas.  In addition, the workshop will host an “unshared task”.

Paper Topics

We invite long and short papers on any of the following general topics:

  • Assessment of all current methods of addressing abusive language

  • The social, personal and cultural effects of abusive language online

  • Legal ramifications of measures taken against abusive language use

  • NLP models and methods for abusive language detection

  • Application of NLP tools to analyze social media content and other large data sets

  • NLP models for cross-lingual abusive language detection

  • Best practices for using NLP techniques in watchdog settings

  • Development of corpora and annotation guidelines

Panel Discussion Topics

Potential panel discussion topics reflect the relevance for industry and individuals:

  • Responsibility of companies and governments in monitoring speech

  • Privacy and ethical implications of abusive language detection (false positives)

  • Follow-up: what to do when a community experiences abusive language

  • Personal experiences from individuals who have been threatened online

  • Best methods for cross-pollination of ideas between fields

Unshared Task

In order to encourage focused contributions, we direct researchers to consider the following list of data sets an unshared task, where participants can choose from a list of datasets to conduct their experiments. This list includes:

  • English Twitter Data Set [Waseem and Hovy, NAACL 2016; Waseem, NLP and CSS 2016][data]

  • German Twitter Data Set [Ross et al. NLP4CMC 2016][data]

  • Wikipedia Abusive Language Data Set [Wulczyn et al., Preprint available here][data]

Related Events

Anti-Harassment Policy

Please note that this workshop follows the anti-harassment policy set forth by the ACL. You can read the policy here.
If a situation should arise, in which you feel that the anti-harassment policy is breached, please feel free to contact the workshop organizers individually or using the workshop email, or get in touch with a member of the ACL Executive Committee.

Contact Us

Please feel free to email us at abusive.language.workshop[at]gmail[dot]com or find us on Twitter.
You can also find the event on the Sociolinguistic Events Calendar.

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