Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/100702
Type of publication: research article
Type of publication (PDB): Straipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a)
Field of Science: Edukologija / Education (S007)
Author(s): Kasperiūnienė, Judita;Briedienė, Monika;Žydžiūnaitė, Vilma
Title: Automatic content analysis of social media short texts: scoping review of methods and tools
Is part of: Computer supported qualitative research : WCQR 2019 / editors António Pedro Costa, Luís Paulo Reis, António Moreira. Cham : Springer, 2020
Extent: p. 89-101
Date: 2020
Series/Report no.: (Advances in Intelligent Systems and Computing (AISC). 1068)
Keywords: Automatic content analysis;Machine learning;Natural language processing;Social media;Online texts;Software tools;Text classification;Text segmentation
ISBN: 9783030317867
Abstract: Content analysis is a widely applied method, applicable to qualitative and quantitative data. In content analysis, computer programs could be used not only for manual typing of codes and categories but for automatic screening of texts, software-assisted identifying and coding of words, phrases, paragraphs or events. Methods for analyzing social media textual data are usually associated with computer science, social media and communication scholar articles. These empirical sources focus on optimizing the goals of computer sciences and mostly evaluate the percentage of documents, phrases or words correctly categorized into a set of themes. Our study aimed to scope the nature and extent of empirical research articles and online materials around automatic content analysis not limited to computer science, media, and communication. This article analyzed and systematized the empirical articles of the last five years examining the categories of automatic content analysis and the related research questions focusing on social sciences and provided practical examples of methods, tools and empirical applications. Research gaps were identified and recommendations for the application of automated content analysis are provided
Internet: https://link.springer.com/chapter/10.1007/978-3-030-31787-4_7
Affiliation(s): Baltijos pažangių technologijų institutas
Edukologijos tyrimų institutas
Informatikos fakultetas
Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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