Big data visualization through the lens of Peirce’s visual sign theory
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Big data visualization through the lens of Peirce’s visual sign theory. / Friedman, Alon; Thellefsen, Martin.
I: Punctum International Journal of Semiotics, Bind 8, Nr. 1, 2022, s. 115-136.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Big data visualization through the lens of Peirce’s visual sign theory
AU - Friedman, Alon
AU - Thellefsen, Martin
N1 - Publisher Copyright: © 2022, Hellenic Semiotic Society. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Data from social media platforms, such as Twitter and Facebook, are generated by people who produce, spread, share, or exchange multimedia content. Such content may include text, images, sounds, or videos. To derive insight into the behavior of social media users, researchers often use open-source technologies to visualize data and generate models for data analytics. One of the most popu-lar open-source applications for managing and analyzing social media data is the open-source R programming lan-guage. Friedman and Feichtinger (2017) created an R package termed ‘Peirce’s sign theory R package’ to analyze data using Peirce’s principles of discovery. Though Peirce semi-otics have been introduced in the context of computer programming languages, so far, no previous work has applied Peirce’s sign theory to data modelling of social media data. In this paper, we use Peirce’s sign theory R package as an overall framework to gain insight into data collected from Twitter. We assembled the data using Twitter’s Analytics al-gorithm, examined the relationships between variables, and visualized the results. Subsequently, we assessed the feasi-bility of analyzing those graphics using the triadic model set out by Jappy (2013) and Peirtarinen (2012) for the interpretation of visual signs. The study results showed that Peirce’s sign theory R package effectively analyzes and visualizes Big Data from social media feeds. However, due to complexities in both the social media data feeds and Peirce’s interpretation of meaning, as outlined by Jappy (2013) and Peirtarinen (2012), we were unable to develop algorithms that generate or suggest an interpretation of visual signs.
AB - Data from social media platforms, such as Twitter and Facebook, are generated by people who produce, spread, share, or exchange multimedia content. Such content may include text, images, sounds, or videos. To derive insight into the behavior of social media users, researchers often use open-source technologies to visualize data and generate models for data analytics. One of the most popu-lar open-source applications for managing and analyzing social media data is the open-source R programming lan-guage. Friedman and Feichtinger (2017) created an R package termed ‘Peirce’s sign theory R package’ to analyze data using Peirce’s principles of discovery. Though Peirce semi-otics have been introduced in the context of computer programming languages, so far, no previous work has applied Peirce’s sign theory to data modelling of social media data. In this paper, we use Peirce’s sign theory R package as an overall framework to gain insight into data collected from Twitter. We assembled the data using Twitter’s Analytics al-gorithm, examined the relationships between variables, and visualized the results. Subsequently, we assessed the feasi-bility of analyzing those graphics using the triadic model set out by Jappy (2013) and Peirtarinen (2012) for the interpretation of visual signs. The study results showed that Peirce’s sign theory R package effectively analyzes and visualizes Big Data from social media feeds. However, due to complexities in both the social media data feeds and Peirce’s interpretation of meaning, as outlined by Jappy (2013) and Peirtarinen (2012), we were unable to develop algorithms that generate or suggest an interpretation of visual signs.
KW - Algorithm
KW - IOpen-source R
KW - Peirce’s sign theory R package
KW - Semiotics
KW - Twitter Analytics
KW - Visual Sign
U2 - 10.18680/hss.2022.0007
DO - 10.18680/hss.2022.0007
M3 - Journal article
AN - SCOPUS:85144869683
VL - 8
SP - 115
EP - 136
JO - Punctum International Journal of Semiotics
JF - Punctum International Journal of Semiotics
SN - 2459-2943
IS - 1
ER -
ID: 331851188