Discovering generalized association rules from Twitter

Luca Cagliero, Alessandro Fiori

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The increasing availability of user-generated content coming from online communities allows the analysis of common user behaviors and trends in social network usage. This paper presents the TweM (Tweet Miner) framework that entails the discovery of hidden and high level correlations, in the form of generalized association rules, among the content and the contextual features of posts published on Twitter (i.e., the tweets). To effectively support knowledge discovery from tweets, the TweM framework performs two main steps: (i) taxonomy generation over tweet keywords and context data and (ii) generalized association rule mining, driven by the generated taxonomy, from a sequence of tweet collections. Unlike traditional mining approaches, the generalized rule mining session performed on the current tweet collection also considers the evolution of the extracted patterns across the sequence of the previous mining sessions to prevent the discarding of rare knowledge that frequently occurs in a number of past extractions. Experiments, performed on both real Twitter posts and synthetic datasets, show the effectiveness and the efficiency of the proposed TweM framework in supporting knowledge discovery from Twitter user-generated content.

Original languageEnglish
Pages (from-to)627-648
Number of pages22
JournalIntelligent Data Analysis
Volume17
Issue number4
DOIs
Publication statusPublished - 2013

Fingerprint

Miners
Association rules
Association Rules
Mining
Knowledge Discovery
Taxonomies
Taxonomy
Data mining
Online Communities
Association Rule Mining
User Behavior
Social Networks
Availability
Experiment
Framework
Experiments

Keywords

  • generalized association rule mining
  • Social network analysis
  • taxonomy inference
  • user-generated content

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition

Cite this

Discovering generalized association rules from Twitter. / Cagliero, Luca; Fiori, Alessandro.

In: Intelligent Data Analysis, Vol. 17, No. 4, 2013, p. 627-648.

Research output: Contribution to journalArticle

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