Stance and Gender Detection in Tweets
on Catalan Independence@Ibereval 2017

Stance and Gender Detection in Tweets on Catalan Independence will take place as part of IberEval 2017, the 2nd Workshop on the Evaluation of Human Language Technologies for Iberian languages,at SEPLN 2017 at University of Murcia, Murcia, Spain, on September 19th, 2017.

Introduction and motivation

The aim of this task is to detect the author's gender and stance with respect to the target "independence of Catalonia" in tweets written in Spanish and/or Catalan.

Participation is allowed in the detection of both stance and gender or only in stance detection.

Classical sentiment analysis tasks carried out in recent years in evaluation campaigns for different languages have mostly involved the detection of the subjectivity and polarity of microblogs at the message level, i.e. determining whether a tweet is subjective or not, and, if subjective, determining its positive or negative semantic orientation. However, comments and opinions are usually directed towards a specific target or aspect of interest, and therefore give rise to finer-grained tasks such as stance detection, where the focus is on detecting what particular stance (in favor, against or neutral) a user takes with respect to a specific target.
Stance detection is related to sentiment analysis, but there are some significant differences, as is stressed in Mohammad et al. (2016a) and Mohammad et al. (2016b):

  • In sentiment analysis, the systems detect whether the sentiment polarity of a text is positive, negative or neutral.

  • In stance detection, the systems detect whether the author is favorable or unfavorable to a given target, which may or may not be explicitly mentioned in the text.

Stance detection is particularly interesting for studying political debates in which the topic is controversial. Therefore, for this task we have chosen to focus on a specific political target: the independence of Catalonia (Bosco et al 2016). The stance detection task is also related to a textual inference task due to the fact that the position of the tweeter is often expressed implicitly, therefore, the stance has to be inferred in many cases. See, for instance, the following tweet:

Language: Catalan
Target: Catalan Independence
Stance: FAVOR
Tweet: Avui #27S2015 tot està per fer... Un nou país és possible ||*|| A les urnes... #27S
(‘Today #27S2015 the future is ours to make… A new country is possible ||*|| Get out and vote … #27S’, where ||*|| stands for the Catalan Independence flag).

Stance detection and author profiling tasks on microblogging texts are currently being carried out in several evaluation forums, including SemEval-2016 (Task-6) (Mohammad et al., 2016a) and PAN@CLEF (Rangel et al., 2016). However, these two tasks have never been performed together for Spanish and Catalan as part of one single task. The results obtained will be of interest not only for sentiment analysis but also for author profiling and for socio-political studies.

Target audience

The task is open to everyone, from academics to people in industry.

Task description

The aim of this task is to detect author's stance and gender in Twitter messages written in Catalan and Spanish.

Stance Detection: Given a message, decide the stance taken towards the target "Catalan Indepencence".
The possible stance labels are: FAVOR, AGAINST and NONE:

  • FAVOR: positive stance towards the independence of Catalonia. Example:
    Language: Spanish
    Stance: FAVOR
    Tweet: "He ido a votar tan sobrado que cuando me han devuelto el DNI les he dicho que ya se lo podían quedar. #27S"
    (‘When I went to vote I was so sure of the result that I told them that they could keep my (Spanish) ID card. #27S’)
  • AGAINST: negative stance towards the independence of Catalonia. Example:
    Language: Spanish
    Stance: AGAINST
    Tweet: "En el día de hoy #27S sólo me sale del alma gritar ¡¡VIVA ESPAÑA! !"
    (‘Today #27S the only thing that my heart tells meto do is to shout ¡¡VIVA ESPAÑA!!’)
  • NONE: neutral stance towards the independence of Catalonia and cases in which the stance cannot be inferred. Example:
    Language: Catalan
    Stance: NONE
    Tweet: "100% escrutat a Arbúcies #27s"
    (‘100% of votes counted in Arbúcies #27s’)

Identification of gender: Given a message, determine its author's gender.
The possible gender labels are: FEMALE and MALE.

In the following examples of tweets labelled for both author's stance and gender in both languages:

Language: Catalan
Stance: FAVOR
Gender: FEMALE
Tweet: "15 diplomàtics internacional observen les plebiscitàries, serà que interessen a tothom menys a Espanya #27S"
(‘15 international diplomats observe the plebiscite, perhaps it is of interest to everybody except to Spain#27S’)

Language: Spanish
Stance: FAVOR
Gender: MALE
Tweet: "#27S Brutal! #JunstPelSi no cree que haya independencia. Solo busca forzar una negociación. Escúchalo antes de votar"
(‘#27S Incredible! #JunstPelSi doesn’t believe in the possibility of Independence. They only want to get a better negotiating position. Listen before voting’)

Language: Spanish
Gender: MALE
Tweet: "#27S ¿cuál fue la diferencia en 2012 entre los resultados de la encuesta de TV3 y resultados finales? Nos serviría para hacernos una idea"
(‘In 2012, what was the difference between the results of the TV3 poll and the final results? That would give us an idea…’)


The dataset will include short documents taken from Twitter on the debate in Catalonia (Spain) collected during the regional elections in September 2015, which have been interpreted by many political actors and citizens as a de facto referendum on the possible independence of Catalonia from Spain.
A detailed description of data (annotation scheme applied, data format, etc.) will soon be available in the task guidelines. The development and test dataset will be released in compliance with Twitter policies.


Each participating team will initially have access only to the training data. Later, the unlabelled test data will also be released (see the timeframe below). After the assessment, the labels for the test data will also be released.

The evaluation will be performed according to standard metrics. In particular, to evaluate stance we will use the macro-average of F-score (FAVOR) and F-score (AGAINST) as evaluation metric, in accordance with the metric proposed at Semeval 2016 - Task 6. Gender will be evaluated by using accuracy, in accordance with the metrics proposed at the Author Profiling task at PAN@CLEF.

How to participate

Information about the submission of results and their format will be available on Google groups.

We invite potential participants to subscribe to our mailing list in order to be kept up to date with the latest news related to the task. Please share comments and questions with the mailing list. The organizers will assist you for any potential issues that could be raised.

Participants will be required to provide a technical report including a brief description of their approach, an illustration of their experiments, in particular techniques and resources used, and an analysis of their results for their publication in the Proceedings of the task.

Papers must be submitted in PDF format by Easychair:
We will use LNCS style. The LNCS template is available here.
The minimum length for the report is five pages (bibliography included). The maximum length is six pages, plus two pages of references.
Technical Reports will be published in as part of the IBEREVAL 2017 Proceedings.

Important dates

  • 21 March 2017: training data available to participants
  • 24 April 2017: test data available to participants
  • 8 May 2017: system results due to organizers
  • 15 May 2017: assessment returned to participants
  • 29 May 2017: working notes submission
  • 12 June 2017: working notes reviewed (peer-reviewed)
  • 26 June 2017: camera ready papers due to the organizers
  • 19 September 2017: IBEREVAL@SEPLN 2017 Workshop


C. Bosco, M. Lai, V. Patti, F. Rangel, P. Rosso (2016) Tweeting in the Debate about Catalan Elections. In: Proc. LREC workshop on Emotion and Sentiment Analysis Workshop (ESA), LREC-2016, Portorož, Slovenia, May 23-28, pp. 67-70.

S. M. Mohammad, S. Kiritchenko, P. Sobhani, X. Zhu, C. Cherry (2016a). Semeval-2016 task 6: Detecting stance in tweets, Proceedings of the International Workshop on Semantic Evaluation, SemEval-2016.

S. M. Mohammad, P. Sobhani, S. Kiritchenko (2016b) Stance and Sentiment in Tweets. CoRR abs/1605.01655

F. Rangel, P. Rosso, B. Verhoeven, W. Daelemans, M. Potthast, B. Stein (2016) Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre Evaluations. In: Balog K., Cappellato L., Ferro N., Macdonald C. (Eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings., vol. 1609, pp. 750-784.


Mariona Taulé (mtaule[at], M. Antònia Martí (amarti[at], Universitat de Barcelona, UB, Spain [primary contacts]

Francisco Rangel (francisco.rangel[at], Autoritas, Spain

Paolo Rosso (prosso[at], Universitat Politècnica de València, UPV, Spain

Cristina Bosco (bosco[at], Viviana Patti (patti[at], Università degli Studi di Torino, UniTO, Italy


Gobierno de Espana Universitat de BarcelonaUPV

Project: SOMEMBED (TIN2015-71147)

University of Turin