An empirical study on uncertainty identification in social media context

Zhongyu Wei; Junwen Chen; Wei Gao; Binyang Li; Lanjun Zhou; Yulan He; Kam-Fai Wong

An empirical study on uncertainty identification in social media context

Zhongyu Wei, Junwen Chen, Wei Gao, Binyang Li, Lanjun Zhou, Yulan He, Kam-Fai Wong

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification.

Original language	English
Title of host publication	Proceedings of the 51st annual meeting of the Association for Computational Linguistics
Publisher	Association for Computational Linguistics
Pages	58-62
Number of pages	5
Volume	2
ISBN (Print)	978-1-937284-51-0
Publication status	Published - 2013
Event	51st annual meeting of the Association for Computational Linguistics - Sofia, Bulgaria Duration: 4 Aug 2013 → 9 Aug 2013

Meeting

Meeting	51st annual meeting of the Association for Computational Linguistics
Abbreviated title	ACL 2013
Country/Territory	Bulgaria
City	Sofia
Period	4/08/13 → 9/08/13

Cite this

@inproceedings{82ec3351268b4e7b83ce29998eeae1ba,

title = "An empirical study on uncertainty identification in social media context",

abstract = "Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification.",

author = "Zhongyu Wei and Junwen Chen and Wei Gao and Binyang Li and Lanjun Zhou and Yulan He and Kam-Fai Wong",

year = "2013",

language = "English",

isbn = "978-1-937284-51-0",

volume = "2",

pages = "58--62",

booktitle = "Proceedings of the 51st annual meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics",

note = "51st annual meeting of the Association for Computational Linguistics, ACL 2013 ; Conference date: 04-08-2013 Through 09-08-2013",

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Wei, Z, Chen, J, Gao, W, Li, B, Zhou, L, He, Y & Wong, K-F 2013, An empirical study on uncertainty identification in social media context. in Proceedings of the 51st annual meeting of the Association for Computational Linguistics. vol. 2, Association for Computational Linguistics, pp. 58-62, 51st annual meeting of the Association for Computational Linguistics, Sofia, Bulgaria, 4/08/13.

TY - GEN

T1 - An empirical study on uncertainty identification in social media context

AU - Wei, Zhongyu

AU - Chen, Junwen

AU - Gao, Wei

AU - Li, Binyang

AU - Zhou, Lanjun

AU - He, Yulan

AU - Wong, Kam-Fai

PY - 2013

Y1 - 2013

N2 - Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification.

AB - Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification.

UR - http://www.scopus.com/inward/record.url?scp=84907367296&partnerID=8YFLogxK

UR - http://www.aclweb.org/anthology/P13-2011

M3 - Conference publication

AN - SCOPUS:84907367296

SN - 978-1-937284-51-0

VL - 2

SP - 58

EP - 62

BT - Proceedings of the 51st annual meeting of the Association for Computational Linguistics

PB - Association for Computational Linguistics

T2 - 51st annual meeting of the Association for Computational Linguistics

Y2 - 4 August 2013 through 9 August 2013

ER -

An empirical study on uncertainty identification in social media context

Abstract

Meeting

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