An Empirical Study On Uncertainty Identification In Social Media Context

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.This chapter has been published as a conference paper in the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013) [21].