@inproceedings{mohammad-etal-2016-dataset,
title = "A Dataset for Detecting Stance in Tweets",
author = "Mohammad, Saif and
Kiritchenko, Svetlana and
Sobhani, Parinaz and
Zhu, Xiaodan and
Cherry, Colin",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1623",
pages = "3945--3952",
abstract = "We can often detect from a person{'}s utterances whether he/she is in favor of or against a given target entity (a product, topic, another person, etc.). Here for the first time we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets of interest―their stance. The targets of interest may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. The data pertains to six targets of interest commonly known and debated in the United States. Apart from stance, the tweets are also annotated for whether the target of interest is the target of opinion in the tweet. The annotations were performed by crowdsourcing. Several techniques were employed to encourage high-quality annotations (for example, providing clear and simple instructions) and to identify and discard poor annotations (for example, using a small set of check questions annotated by the authors). This Stance Dataset, which was subsequently also annotated for sentiment, can be used to better understand the relationship between stance, sentiment, entity relationships, and textual inference.",
}
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%0 Conference Proceedings
%T A Dataset for Detecting Stance in Tweets
%A Mohammad, Saif
%A Kiritchenko, Svetlana
%A Sobhani, Parinaz
%A Zhu, Xiaodan
%A Cherry, Colin
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F mohammad-etal-2016-dataset
%X We can often detect from a person’s utterances whether he/she is in favor of or against a given target entity (a product, topic, another person, etc.). Here for the first time we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets of interest―their stance. The targets of interest may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. The data pertains to six targets of interest commonly known and debated in the United States. Apart from stance, the tweets are also annotated for whether the target of interest is the target of opinion in the tweet. The annotations were performed by crowdsourcing. Several techniques were employed to encourage high-quality annotations (for example, providing clear and simple instructions) and to identify and discard poor annotations (for example, using a small set of check questions annotated by the authors). This Stance Dataset, which was subsequently also annotated for sentiment, can be used to better understand the relationship between stance, sentiment, entity relationships, and textual inference.
%U https://aclanthology.org/L16-1623
%P 3945-3952
Markdown (Informal)
[A Dataset for Detecting Stance in Tweets](https://aclanthology.org/L16-1623) (Mohammad et al., LREC 2016)
ACL
- Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016. A Dataset for Detecting Stance in Tweets. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3945–3952, Portorož, Slovenia. European Language Resources Association (ELRA).