Social media have created a new environmental context for the study of social and human behavior and services. Although social work researchers have become increasingly interested in the use of social media to address social problems, they have been slow to adapt tools that are flexible and convenient for analyzing social media data. They have also given inadequate attention to bias and representation inherent in many multimedia data sets. This article introduces the Visual and Textual Analysis of Social Media (VATAS) system, an open-source Web-based platform for labeling or annotating social media data. We use a case study approach, applying VATAS to a study of Chicago, IL, gang-involved youth communication on Twitter to highlight VATAS’ features and opportunities for interdisciplinary collaboration. VATAS is highly customizable, can be privately held on a secure server, and allows for export directly into a CSV file for qualitative, quantitative, and machine-learning analysis. Implications for research using social media sources are noted.

Vatas: An open-source web platform for visual and textual analysis of social media

Schifanella R.;
2020-01-01

Abstract

Social media have created a new environmental context for the study of social and human behavior and services. Although social work researchers have become increasingly interested in the use of social media to address social problems, they have been slow to adapt tools that are flexible and convenient for analyzing social media data. They have also given inadequate attention to bias and representation inherent in many multimedia data sets. This article introduces the Visual and Textual Analysis of Social Media (VATAS) system, an open-source Web-based platform for labeling or annotating social media data. We use a case study approach, applying VATAS to a study of Chicago, IL, gang-involved youth communication on Twitter to highlight VATAS’ features and opportunities for interdisciplinary collaboration. VATAS is highly customizable, can be privately held on a secure server, and allows for export directly into a CSV file for qualitative, quantitative, and machine-learning analysis. Implications for research using social media sources are noted.
2020
11
1
133
155
https://www.journals.uchicago.edu/doi/10.1086/707667
Data analysis; Machine learning; Multimedia; Qualitative; Social media
Patton D.U.; Blandfort P.; Frey W.R.; Schifanella R.; McGregor K.; Chang S.-F.U.
File in questo prodotto:
File Dimensione Formato  
707667.pdf

Accesso riservato

Tipo di file: PDF EDITORIALE
Dimensione 3.75 MB
Formato Adobe PDF
3.75 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1795558
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 5
social impact