Alon Friedman

In today’s political and rhetorical landscape, many politicians use Twitter as their communication platform of choice. President Donald Trump is one of the best-known Twitter users. The media often analyze Trump’s tweets, and several artists have attempted to interpret them through visualizations. One of the leading visual artists to do so is Erin Gallagher. During the presidency of George W. Bush, visualization was also used to interpret political messages, specifically in work by Mark Lombardi. As controversy surrounding social media’s truthfulness has escalated, it seems useful to consider how visual uncertainty has been treated in work by Lombardi and Gallagher. This study asks the following questions: What do these the two leading approaches to visual uncertainty contribute to an examination of works by Erin Gallagher and Mark Lombardi? What does a theoretical analysis reveal about common design features in the work of these artists? The comparison undertaken here shows that, regarding visual precision and credibility, it is difficult to evaluate Gallagher’s work. Future studies should examine whether visual grammar theory can add more insight into these artists’ visual arguments

Throughout Peirce’s writing, we witness his developing vision of a machine that scientists will eventually be able to create. Nadin (2010) raised the question:Why do computer scientists continue to ignore Peirce’s sign theory? A review of the literature on Peirce’s theory and the semiotics machine reveals that many authors discussed the machine;however, they donot differentiate between a physical computer machine and its software. This paper discusses the problematic issues involved in converting Peirce’s theory into a programming language, machine and software application. We demonstrate this challenge by introducing Peirce’s sign theory as a software application that runs under an open-source R environment

Purpose – The purpose of the research reported here is to improve comprehension of the socially-negotiated identity of concepts in the domain of knowledge organization. Because knowledge organization as a domain has as its focus the order of concepts, both from a theoretical perspective and from an applied perspective, it is important to understand how the domain itself understands the meaning of a concept. Design/methodology/approach – The paper provides an empirical demonstration of how the domain itself understands the meaning of a concept. The paper employs content analysis to demonstrate the ways in which concepts are portrayed in KO concept maps as signs, and they are subjected to evaluative semiotic analysis as a way to understand their meaning. The frame was the entire population of formal proceedings in knowledge organization – all proceedings of the International Society for Knowledge Organization’s international conferences (1990-2010) and those of the annual classification workshops of the Special Interest Group for Classification Research of the American Society for Information Science and Technology (SIG/CR). Findings – A total of 344 concept maps were analyzed. There was no discernible chronological pattern. Most concept maps were created by authors who were professors from the USA, Germany, France, or Canada. Roughly half were judged to contain semiotic content. Peirceian semiotics predominated, and tended to convey greater granularity and complexity in conceptual terminology. Nodes could be identified as anchors of conceptual clusters in the domain; the arcs were identifiable as verbal relationship indicators. Saussurian concept maps were more applied than theoretical; Peirceian concept maps had more theoretical content. Originality/value – The paper demonstrates important empirical evidence about the coherence of the domain of knowledge organization. Core values are conveyed across time through the concept maps in this population of conference papers