Improving object and event monitoring on twitter through lexical analysis and user profiling

Yihong Zhang*, Claudia Szabo, Quan Z. Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

5 Citations (Scopus)

Abstract

Personal users on Twitter frequently post observations about their immediate environment as part of the 500 million tweets posted everyday. These observations and their implicitly associated time and location data are a valuable source of information for monitoring objects and events, such as earthquake, hailstorm, and shooting incidents. However, given the informal and uncertain expressions used in personal Twitter messages, and the various type of accounts existing on Twitter, capturing personal observations of objects and events is challenging. In contrast to the existing supervised approaches, which require significant efforts for annotating examples, in this paper, we propose an unsupervised approach for filtering personal observations. Our approach employs lexical analysis, user profiling and classification components to significantly improve filtering precision. To identify personal accounts, we define and compute a mean user profile for a dataset and employ distance metrics to evaluate the similarity of the user profiles under analysis to the mean. Our extensive experiments with real Twitter data show that our approach consistently improves filtering precision of personal observations by around 22 %.

Original languageEnglish
Title of host publicationWeb information systems engineering – WISE 2016
Subtitle of host publication17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings. Part I
EditorsWojciech Cellary, Mohamed F. Mokbel, Jianmin Wang, Hua Wang, Rui Zhou, Yanchun Zhang
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages19-34
Number of pages16
ISBN (Electronic)9783319487403
ISBN (Print)9783319487427
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event17th International Conference on Web Information Systems Engineering, WISE 2016 - Shanghai, China, Shanghai
Duration: 8 Nov 201610 Nov 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10042
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Web Information Systems Engineering, WISE 2016
CityShanghai
Period8/11/1610/11/16

Keywords

  • Microblog content classification
  • Twitter
  • User profiling

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