Analyzing twitter user behaviors and topic trends by exploiting dynamic rules

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Everyday online communities and social networks are accessed by millions of Web users, who produce a huge amount of user-generated content (UGC). The UGC and its publication context typically evolve over time and reflect the actual user interests and behaviors. Thus, the application of data mining techniques to discover the evolution of common user behaviors and topic trends is becoming an appealing research issue. Dynamic association rule mining is a well-established technique to discover correlations, among data collected in consecutive time periods, whose main quality indexes (e.g., support and confidence) exceed a given threshold and possibly vary from one time period to another. This Chapter presents the DyCoM (Dynamic Context Miner) data mining system. It entails the discovery of a novel and extended version of dynamic association rules, namely the dynamic generalized association rules, from both the content and the contextual features of the user-generated messages posted on Twitter. A taxonomy over contextual data features is semi-automatically built and exploited to discover dynamic correlations among data at different abstraction levels and their temporal evolution in a sequence of tweet collections. Experiments, performed on both real Twitter posts and synthetic datasets, show the effectiveness and the efficiency of the proposed DyCoM framework in supporting user behavior and topic trend analysis from Twitter.

Original languageEnglish
Title of host publicationBehavior Computing: Modeling, Analysis, Mining and Decision
PublisherSpringer-Verlag London Ltd
Pages267-287
Number of pages21
ISBN (Print)9781447129691, 9781447129684
DOIs
Publication statusPublished - Jan 1 2012

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Cag, L., & Fiori, A. (2012). Analyzing twitter user behaviors and topic trends by exploiting dynamic rules. In Behavior Computing: Modeling, Analysis, Mining and Decision (pp. 267-287). Springer-Verlag London Ltd. https://doi.org/10.1007/978-1-4471-2969-1_17