ITU

Stance Prediction for Russian: Data and Analysis

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Standard

Stance Prediction for Russian: Data and Analysis. / Lozhnikov, Nikita; Derczynski, Leon; Mazzara, Manuel.

Proceedings of 6th International Conference in Software Engineering for Defence Applications: SEDA 2018. Springer, 2018. p. 176-186 (Advances in Intelligent Systems and Computing, Vol. 925).

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Harvard

Lozhnikov, N, Derczynski, L & Mazzara, M 2018, Stance Prediction for Russian: Data and Analysis. in Proceedings of 6th International Conference in Software Engineering for Defence Applications: SEDA 2018. Springer, Advances in Intelligent Systems and Computing, vol. 925, pp. 176-186. https://doi.org/10.1007/978-3-030-14687-0_16

APA

Lozhnikov, N., Derczynski, L., & Mazzara, M. (2018). Stance Prediction for Russian: Data and Analysis. In Proceedings of 6th International Conference in Software Engineering for Defence Applications: SEDA 2018 (pp. 176-186). Springer. Advances in Intelligent Systems and Computing Vol. 925 https://doi.org/10.1007/978-3-030-14687-0_16

Vancouver

Lozhnikov N, Derczynski L, Mazzara M. Stance Prediction for Russian: Data and Analysis. In Proceedings of 6th International Conference in Software Engineering for Defence Applications: SEDA 2018. Springer. 2018. p. 176-186. (Advances in Intelligent Systems and Computing, Vol. 925). https://doi.org/10.1007/978-3-030-14687-0_16

Author

Lozhnikov, Nikita ; Derczynski, Leon ; Mazzara, Manuel. / Stance Prediction for Russian: Data and Analysis. Proceedings of 6th International Conference in Software Engineering for Defence Applications: SEDA 2018. Springer, 2018. pp. 176-186 (Advances in Intelligent Systems and Computing, Vol. 925).

Bibtex

@inproceedings{83c6230a83364060868f73c1a27add1d,
title = "Stance Prediction for Russian: Data and Analysis",
abstract = "Stance detection is a critical component of rumour and fake news identification. It involves the extraction of the stance a particular author takes related to a given claim, both expressed in text. This paper investigates stance classification for Russian. It introduces a new dataset, RuStance, of Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language. As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language.",
author = "Nikita Lozhnikov and Leon Derczynski and Manuel Mazzara",
year = "2018",
doi = "10.1007/978-3-030-14687-0_16",
language = "English",
isbn = "978-3-030-14686-3",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "176--186",
booktitle = "Proceedings of 6th International Conference in Software Engineering for Defence Applications",
address = "Germany",

}

RIS

TY - GEN

T1 - Stance Prediction for Russian: Data and Analysis

AU - Lozhnikov, Nikita

AU - Derczynski, Leon

AU - Mazzara, Manuel

PY - 2018

Y1 - 2018

N2 - Stance detection is a critical component of rumour and fake news identification. It involves the extraction of the stance a particular author takes related to a given claim, both expressed in text. This paper investigates stance classification for Russian. It introduces a new dataset, RuStance, of Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language. As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language.

AB - Stance detection is a critical component of rumour and fake news identification. It involves the extraction of the stance a particular author takes related to a given claim, both expressed in text. This paper investigates stance classification for Russian. It introduces a new dataset, RuStance, of Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language. As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language.

U2 - 10.1007/978-3-030-14687-0_16

DO - 10.1007/978-3-030-14687-0_16

M3 - Article in proceedings

SN - 978-3-030-14686-3

T3 - Advances in Intelligent Systems and Computing

SP - 176

EP - 186

BT - Proceedings of 6th International Conference in Software Engineering for Defence Applications

PB - Springer

ER -

ID: 84245912