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----------------------- Overview ----------------------- Interaction amongst users on social networking platforms can enable constructive and insightful conversations and civic participation; however, on many sites that encourage user interaction, verbal abuse has become commonplace, leading to negative outcomes such as cyberbullying, hate speech, and scapegoating. In online contexts, aggressive behavior may be more frequent than in face-to-face interaction, which can poison the social climates within online communities. The last few years have seen a surge in such abusive online behavior, leaving governments, social media platforms, and individuals struggling to deal with the consequences.
For instance, in 2015, Twitter’s CEO publicly admitted that online abuse on their platform was resulting in users leaving the platform, and in some cases even having to leave their homes. More recently, Facebook, Twitter, YouTube and Microsoft pledged to remove hate speech from their platforms within 24 hours in accordance with the EU commission code of conduct and face fines of up to €50M in Germany if they systematically fail to remove abusive content within 24 hours. While governance demands the ability to respond quickly and at scale, we do not yet have effective human or technical processes that can address this need. Abusive language can often be extremely subtle and highly context dependent. Thus we are challenged to develop scalable computational methods that can reliably and efficiently detect and mitigate the use of abusive language online within variable and evolving contexts.
As a field that works directly with computational analysis of language, NLP (Natural Language Processing) is in a unique position to address this problem. Recently there have been a greater number of papers dealing with abusive language in the computational linguistics community. Abusive language is not a stable or simple target: misclassification of regular conversation as abusive can severely impact users’ freedom of expression and reputation, while misclassification of abusive conversations as unproblematic on the other hand maintains the status quo of online communities as unsafe environments. Clearly, there is still a great deal of work to be done in this area. More practically, as research into detecting abusive language is still in its infancy, the research community has yet to agree upon a suitable typology of abusive content as well as upon standards and metrics for proper evaluation, where research in media studies, rhetorical analysis, and cultural analysis can offer many insights.
In this second edition of this workshop, we continue to emphasize the computational detection of abusive language as informed by interdisciplinary scholarship and community experience. We invite paper submissions describing unpublished work from relevant fields including, but not limited to: natural language processing, law, psychology, network analysis, gender and women’s studies, and critical race theory.
----------------------- Paper Topics ----------------------- We invite long and short papers on any of the following general topics: related to developing computational models and systems:
- NLP models and methods for detecting abusive language online, including, but not limited to hate speech, cyberbullying etc. - Application of NLP tools to analyze social media content and other large data sets - NLP models for cross-lingual abusive language detection - Computational models for multi-modal abuse detection - Development of corpora and annotation guidelines - Critical algorithm studies with a focus on abusive language moderation technology - Human-Computer Interaction for abusive language detection systems - Best practices for using NLP techniques in watchdog settings
or related to legal, social, and policy considerations of abusive language online:
- The social and personal consequences of being the target of abusive language and targeting others with abusive language - Assessment of current non-NLP methods of addressing abusive language - Legal ramifications of measures taken against abusive language use - Social implications of monitoring and moderating unacceptable content - Considerations of implemented and proposed policies for dealing with abusive language online and the technological means of dealing with it.
In addition, in this one-day workshop, we will have a multidisciplinary panel discussion and a forum for plenary discussion on the issues that researchers and practitioners face in efforts to work with abusive language detection. We are also looking into the possibility of publishing a special issue journal to this iteration of the workshop. We seek to emphasise a focus on policy aspects of online abuse through invited speakers and panels.
----------------------- Unshared task ----------------------- In order to encourage focused contributions, we encourage researchers to consider using one or more of the following datasets in their experiments:
- StackOverflow Offensive Comments [Access on Workshop webpage] - Yahoo News Dataset of User Comments [Nobata et al., WWW 2016] - Twitter Data Set [Waseem and Hovy, NAACL 2016] - German Twitter Data Set [Ross et al. NLP4CMC 2016] - Greek News Data Set [Pavlopoulos et al., EMNLP 2017] - Wikimedia Toxicity Data Set [Wulczyn et al., WWW 2017] - SFU Opinion and Comment Corpus [Kolhatkar et al., In Review]
----------------------- Submission Information ----------------------- We will be using the EMNLP 2018 Submission Guidelines. Authors are invited to submit a full paper of up to 8 pages of content with up to 2 additional pages for references. We also invite short papers of up to 4 pages of content, including 2 additional pages for references. Accepted papers will be given an additional page of content to address reviewer comments. We also invite papers which describe systems. If you would like to present a demo in addition to presenting the paper, please make sure to select either "full paper + demo" or "short paper + demo" under "Submission Category" in the START submission page.
Previously published papers cannot be accepted. The submissions will be reviewed by the program committee. As reviewing will be blind, please ensure that papers are anonymous. Self-references that reveal the author's identity, e.g., "We previously showed (Smith, 1991) ...", should be avoided. Instead, use citations such as "Smith previously showed (Smith, 1991) ...".
We have also included conflict of interest in the submission form. You should mark all potential reviewers who have been authors on the paper, are from the same research group or institution, or who have seen versions of this paper or discussed it with you.
We will be using the START conference system to manage submissions.
----------------------- Important Dates ----------------------- Submission due: July 20, 2018 Author Notification: August 18, 2018 Camera Ready: August 31, 2018 Workshop Date: Oct 31st or Nov 1st, 2018 Submission link: https://www.softconf.com/emnlp2018/ALW2/ <https://www.softconf.com/emnlp2018/ALW2/>
----------------------- Organizing Committee ----------------------- Darja Fišer, University of Ljubljana & the Jožef Stefan Institute Ruihong Huang, Texas A&M University Vinodkumar Prabhakaran, Stanford University Rob Voigt, Stanford University Zeerak Waseem, University of Sheffield Jacqueline Wernimont, Arizona State University -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 16631 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20180624/3c759828/attachment.txt>