Method
Iconographic tracking is a digital research method designed to follow images as they circulate, enter into diverse collective activities, transform, and become consequential with time and space. While qualitative research strategies are often deployed to do field research and conduct interviews and questionnaires, iconographic tracking largely relies on digital research strategies to follow images on their travels and to trace their rhetorical transformation (see Gries 2017). For this project, we adapted iconographic tracking to learn more about how Trumpicons have been designed, where they have surfaced, and what rhetorical actions they have taken on via circulation since their inception in 2011. We especially tried to take advantage of digital visualization techniques to address these inquiries, deepen our analysis, and develop theoretical insight about this new media genre. (Note: Many of the data visualizations are interactive. We thus invite you to spend time engaging with them to learn more about the new media genre of Trumpicons.)
While iconographic tracking was invented for visual rhetoric research, it is also a viable method for digital visual studies and the humanities writ large. In the humanities (and social sciences), digital tools and research strategies are beginning to play a more prominent role in facilitating research whether text, data, image, sound based, or outcome-based (Gardiner and Musto 72–81). Digital visualization techniques are crucial in helping to generate visual representations of structured data (Gardiner and Musto 77–78) that can aid in facilitating pattern and trend identification as well as catalyzing new questions. As Derek Mueller notes, abstract visual models enhance possibilities for new questions, insights, and knowledge to come to light (4). Rather than think about data visualizations as proofs, closures, and/or conclusions, then, they ought to be understood as provocations, openings, and clearings for rethinking existing and building new theories (4).
While we do not have enough space to explain our exact tracking and data visualization strategies here, we do want to briefly discuss our tagging/coding/marking practices in the spirit of transparency. We began our research by collecting as many different Trumpicons as we could, tagging not only locations and dates, but also, of course, captions. In an effort to code for resistance, we enacted content analysis to determine if the depictions of Trump and captions were supportive of Trump, critical of Trump, or unclear. In terms of rhetorical function, we also created a coding scheme that included the following categories: education, artistic expression, political support, event promotion, protest, commercial sales, and parody, identifying in the latter three cases what kind of protest a Trumpicon took part in and when satirical parody was used for commodity activism.2 Because of our interest in the racial politics of circulation, we also loosely kept track of which Trumpicons might be associated with racism or white nationalism. For instance, one popular Trumpicon that we discuss in the following section is the “Twitler” design in which Trump is depicted as Hitler, who in our mind conjures fascism, racism, and xenophobia. We thus marked this Trumpicon as associated with white nationalism and racism.
In some Trumpicons, the design is clearly and directly related to race. For instance, in one Trumpicon, the caption reads “Whites Only,” while in another the caption simply says “Racist.” In other Trumpicons, however, their association with race surfaces less in the design than in their social action. For example, a “Nope” poster being held up in a protest against Trump’s immigration ban takes on associations with xenophobia and Islamophobia where as in other contexts a “Nope” Trumpicon may simply express one’s desires for the 2016 presidential election results. Therefore, when marking for associations with racism and/or white nationalism, we identified Trumpicons in which such associations could be determined by design and/or social action. This tagging/coding/marking system surely has its limitations and interpretative bias. As Johanna Drucker reminds us, all data sets ought to be considered a rhetorical capturing of data rather than an accurate representation of reality. However, to ensure consistency in light of the fact that we do not know the intentionality behind many Trumpicons, we made these decisions in order to better understand the frequency and creative patterns in which Trumpicons resist Trump’s white nationalist postracial self-stereotype, rhetoric, and policies.
2. We coded Trumpicons as Commercial Sales when sales was the only obvious or stated goal. We coded Trumpicons as parody when a designer identified their work as being created purely for fun in commercial or commercial contexts. We coded Trumpicons as satirical parody when critique was obvious either in design or designer’s tags, accompanying text, or labels.↩