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

Research output: Chapter in Book/Report/Conference proceedingChapter

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 chapter, we describe 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 languageEnglish
Title of host publicationSocial Media Content Analysis
Subtitle of host publicationNatural Language Processing and Beyond
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages79-88
Number of pages10
ISBN (Electronic)9789813223615
ISBN (Print)9789813223608
DOIs
Publication statusPublished - 1 Jan 2017

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Wei, Z., Chen, J., Gao, W., Li, B., Zhou, L., He, Y., & Wong, K. F. (2017). An empirical study on uncertainty identification in social media context. In Social Media Content Analysis: Natural Language Processing and Beyond (pp. 79-88). World Scientific Publishing Co. Pte Ltd. https://doi.org/10.1142/9789813223615_0007