- Theories and models of aggression and conflict in language.
- Cyberbullying, threatening, hateful, aggressive and abusive language
on the web.
- Multilingualism and aggression.
- Resource Development - Corpora, Annotation Guidelines and Best
Practices for threat and aggression detection.
- Computational Models and Methods for aggression, hate speech and
offensive language detection in text and speech.
- Detection of threats and bullying on the web.
- Automatic censorship and moderation: ethical, legal and
technological issues and challenges.

*For this iteration of the shared task, we especially encourage those who participated or have trained models on TRAC - 2018 and /or TRAC - 2020 Shared Task datasets to submit the predictions of their earlier models on our current test set. They are, of course, free to submit predictions on new models / current datasets as well.* On Mon, May 16, 2022 at 12:31 PM Ritesh Kumar <ritesh.lists@gmail.com> wrote: > *3rd Workshop on Threat, Aggression and Cyberbullying (TRAC - 2022)* > & > *Shared Tasks on Bias, Threat and Aggression Identification in Context* > Co-located with COLING 2022, October 12 - 17, 2022 > Gyeongju, the Republic of Korea > > > *Second Call for Papers and Shared Task Participation* > > *Workshop Website*: https://sites.google.com/view/trac2022/home > *Paper Submission*: https://www.softconf.com/coling2022/TRAC-2022/ > *Shared Task Website:* https://codalab.lisn.upsaclay.fr/competitions/4753 > > *Submission Deadline*: July 11, 2022 (Regular) / July 31, 2022 (ACL ARR) > > As in the earlier editions of the workshop, TRAC-2022 will focus on the > applications of NLP, ML and pragmatic studies on aggression and > impoliteness to tackle these issues. We invite *long (8 pages)* and *short > papers (4 pages)* as well as *position papers* and opinion pieces (5 - 20 > pages), *demo proposals* and *non-archival extended abstracts* (2 pages) > based on, but not limited to, any of the following themes from academic > researchers, industry and any other group / team working in the area. > > - Theories and models of aggression and conflict in language. > - Cyberbullying, threatening, hateful, aggressive and abusive language > on the web. > - Multilingualism and aggression. > - Resource Development - Corpora, Annotation Guidelines and Best > Practices for threat and aggression detection. > - Computational Models and Methods for aggression, hate speech and > offensive language detection in text and speech. > - Detection of threats and bullying on the web. > - Automatic censorship and moderation: ethical, legal and > technological issues and challenges. > > > *Shared Tasks* > TRAC-2022 will include two novel shared tasks: > > *Task 1: Bias, Threat and Aggression Identification in Context* > The first shared task will be a structured prediction task for recognising > (a) Aggression, Gender Bias, Racial Bias, Religious Intolerance and Bias > and Casteist Bias on social media and (b) the "discursive role" of a given > comment in the context of the previous comment(s). The participants will be > given a "thread" of comments with information about the presence of > different kinds of biases and threats (viz. gender bias, gendered threat > and none, etc) and its discursive relationship to the previous comment as > well as the original post (viz. attack, abet, defend, counter-speech and > gaslighting). In a series / thread of comments, participants will be > required to predict the presence of aggression and bias of each comment, > possibly making use of the context. > > *Task 2: Generalising across domains - COVID-19* > For this sub-task, the test set will be sampled from the COVID-19 related > conversation, annotated with levels of aggression, offensiveness and hate > speech. Across the globe, during the pandemic, we have seen various kinds > of novel aggressive and biased conversation on social media - in fact, in > some cases there was massive escalation of religious and other kinds of > intolerance and polarisation. The participants of TRAC-1 and TRAC-2 shared > tasks are especially encouraged to submit the predictions their their > earlier models on this test set. They may also train new models jointly on > both the datasets. Those who didn't participate in earlier tasks are also > invited to submit the predictions for this task by training models on the > two datasets and are encouraged to submit the predictions on the respective > test sets of the earlier tasks along with the predictions on the current > dataset (to enable comparison). New participants may also use TRAC-1 or > TRAC-2 dataset or a combination of the two for building the models. The aim > of the task is to evaluate the generalisability of our systems in > unexpected and novel situations. > > For participation, visit the Codalab website - > https://codalab.lisn.upsaclay.fr/competitions/4753 > > For any clarifications, contact coling.aggression@gmail.com. > > Looking forward to your participation! > >