Classifying Short Messages Using Collaborative Knowledge Bases: Reading Wikipedia to Understand Twitter

Classifying Short Messages Using Collaborative Knowledge Bases: Reading Wikipedia to Understand Twitter - scientific work related to Wikipedia quality published in 2013, written by Yegin Genc, Winter Mason and Jeffrey V. Nickerson.

Overview

To detect concepts from tweets, authors leverage the content of Wikipedia. This is a form of semantic transformation: ideas that emerge in short texts are mapped onto more extensive texts that contain additional structure. This additional structure is used to amplify the signal in the short text. This idea is rooted in previous research [1, 2], as well as in the work of other authors pursuing similar goals [3-5]. Authors method has two main stages. First, authors recognize candidate concepts—partsof-tweets—that may be valid entities in the tweet. These concepts are then classified into four categories: Locations, People, Organizations, and Miscellaneous. Candidate concepts are identified by mapping tweets to Wikipedia pages, and the networks of these concepts in Wikipedia are used for filtering and classification. Authors believe this technique can be applied more generally to the understanding of many forms of short messages, not just tweets, utilizing many forms of collaborative knowledge bases, not just Wikipedia.