CHI-ANNOUNCEMENTS Archives

ACM SIGCHI General Interest Announcements (Mailing List)

CHI-ANNOUNCEMENTS@LISTSERV.ACM.ORG

Options: Use Forum View

Use Monospaced Font
Show Text Part by Default
Condense Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Content-Type:
text/plain; charset="UTF-8"
Date:
Tue, 9 Feb 2021 14:59:47 +0200
Reply-To:
Styliani Kleanthous <[log in to unmask]>
Subject:
MIME-Version:
1.0
Message-ID:
Content-Transfer-Encoding:
quoted-printable
Sender:
"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
From:
Styliani Kleanthous <[log in to unmask]>
Parts/Attachments:
text/plain (129 lines)
Please consider contributing and/or forwarding to appropriate  colleagues
and groups.

  **** We apologize for the multiple copies of this e-mail ****

 ----------------------------------------------------------------------------------------------------

 Call for Participation
 ----------------------------------------------------------------------------------------------------


 *First Call for Participation:*

*  EXIST@IberLEF 2021:*

*  Task: EXIST-sEXism Identification in Social neTworks Website:
http://nlp.uned.es/exist2021/ <http://nlp.uned.es/exist2021/>*

 It will take place as part of IberLEF 2021, the 3rd Workshop on Iberian
Languages Evaluation Forum at the SEPLN 2021 Conference, which will be held
in September 2021 in Spain.

We are launching the first shared task on sEXism Identification in Social
neTworks (EXIST) at IberLEF 2021. The aim of EXIST is to automatically
identify sexism content on Twitter and Gab.com.
Detecting online sexism may be difficult, as it may be expressed in very
different forms. Sexism may sound “friendly”: the statement “Women must be
loved and respected, always treat them like a fragile glass” may seem
positive, but is actually considering that women are weaker than men.
Sexism may sound “funny”, as it is the case of sexist jokes or humour (“You
have to love women… just that… You will never understand them.”). Sexism
may sound “offensive” and “hateful”, as in “Humiliate, expose and degrade
yourself as the fucking bitch you are if you want a real man to give you
attention”.

Our aim is the detection of sexism in a broad sense, from explicit misogyny
to other subtle expressions that involve implicit sexist behaviours. The
automatic identification of sexisms in a broad sense may help to create,
design and determine the evolution of new equality policies, as well as
encourage better behaviors in society.

 Participants will be asked to classify “tweets” and “gab post” (in English
and Spanish) according to the following two tasks:

 *Task 1 -* The first subtask is a binary classification. The systems have
to decide whether or not a given text (tweet or gab) is sexist (i.e., it is
sexist itself, describes a sexist situation or criticizes a sexist
behaviour).

 *Task 2 -* Once a message has been classified as sexist, the second task
aims to categorize the message in different types of sexism (according to
the categorization proposed by experts and that takes into account the
different facets of women that are undermined). In particular, we propose a
five-classification task: (i) IDEOLOGICAL AND INEQUALITY,  (ii)
STEREOTYPING AND DOMINANCE, (iii) OBJECTIFICATION, (iv) SEXUAL VIOLENCE and
(v) MISOGYNY AND NON-SEXUAL VIOLENCE.

 Although we recommend to participate in both subtasks, participants are
allowed to participate just in one of them (e.g., subtask 1).

During the training phase, the task organizers will provide to the
participants the manually-annotated EXIST training dataset (tweets).
For the evaluation of the teams, the unlabelled test data will be released
(tweets and gabs). After the assessment, the labels for the test sets will
be given for both subtasks.

We encourage participation from both academic institutions and industrial
organizations. We invite the participants to fill this form
https://bit.ly/3iUXNnq. You will receive information about how to join the
Google Group about the EXIST shared task.


*  Important Dates:*

 * 1 Feb 2021 Registration open
 * 8 Mar 2021 Training set available.
 * 14 Apr 2021 Testing set available.
 * 28 Apr 2021 Systems results due to organizers.
 * 12 May 2021 Results notification to participants.
 * 26 May 2021 Submission of Working Notes by participants.
 * 16 Jun 2021 Reviews to participants (peer-reviews).
 * 30 Jun 2021 Camera-ready due to organizers.
 * Sep 2021 EXIST@IberLEF 2021

 **Note: All deadlines are 11:59PM UTC-12:00 ("anywhere on Earth").**

 *Organizers:*

Jorge Carrillo-de-Albornoz, Universidad Nacional de Educación a Distancia
(UNED)
Francisco Rodríguez-Sánchez, Universidad Nacional de Educación a Distancia
(UNED)
Laura Plaza, Universidad Nacional de Educación a Distancia (UNED)
Julio Gonzalo, Universidad Nacional de Educación a Distancia (UNED)
Paolo Rosso, Universitat Politècnica de Valencia (UPV)
Trinidad Donoso, Universidad de Barcelona (UB)
Miriam Comet, Universidad de Barcelona (UB)

 *Contact:*
 Contact the organizers by writing to:  [log in to unmask]<mailto:
[log in to unmask]

*  Website: http://nlp.uned.es/exist2021/ <http://nlp.uned.es/exist2021/>*

We invite participants to join the Google group in order to be kept up to
date with the latest news related to the task:*
https://groups.google.com/g/existiberlef2021
<https://groups.google.com/g/existiberlef2021>*

--


 -----------------------------------------------

Jorge Carrillo de Albornoz, MSc, PhD.

NLP & IR Group at UNED (http://nlp.uned.es<http://nlp.uned.es/>)
Dept. Lenguajes y Sistemas Informáticos
E.T.S. Informática UNED
C/ Juan del Rosal, 16

    ----------------------------------------------------------------------------------------
    To unsubscribe from CHI-ANNOUNCEMENTS send an email to:
     mailto:[log in to unmask]

    To manage your SIGCHI Mailing lists or read our polices see:
     https://sigchi.org/operations/listserv/
    ----------------------------------------------------------------------------------------

ATOM RSS1 RSS2