Content analysis of facebook posts in public libraries based on textual analysis

Soohyung Joo, Kun Lu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This poster presents preliminary findings from a textual analysis of social media posts created by public libraries. We collected a text corpus, consisting of 3,622 Facebook posts containing more than seven words from 151 public libraries. We conducted a term frequency analysis and latent dirichlet allocation (LDA) topic modeling to explore the content of Facebook posts. The results revealed that terms related to library programming and library event announcements appeared frequently. The LDA topic modeling identified 25 topics underlying the corpus. We observed various topics were posted on public library Facebook Pages while library programming and event related topics were most common.

Original languageEnglish
Pages (from-to)718-719
Number of pages2
JournalProceedings of the Association for Information Science and Technology
Volume54
Issue number1
DOIs
StatePublished - Jan 2017

Bibliographical note

Publisher Copyright:
Copyright © 2017 by Association for Information Science and Technology

Keywords

  • LDA topic models
  • public libraries
  • social media
  • textual analysis

ASJC Scopus subject areas

  • Computer Science (all)
  • Library and Information Sciences

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