The greatest take-away from this piece is that “the visualization of cultural collections… shouldn’t aim at reproducing and consolidating the hegemonic claims of archives, but rather invite the viewer to critically question what is being viewed and the circumstances of its creation.” In our project, where we evaluate memes of resistance deployed during the Russian Invasion of Ukraine, it’s important for us, as well as our audiences, to consider the position of these memes within culture, and to question them accordingly. Who created them? What were their intentions? Were the memes targeting someone or something, and what kind of effect were they trying to have on audiences (who, like ours, will be viewing them in similarly social environments)?

As Frischknecht argues, our data visualizations may be considered generative in some respects (they are also participatory, as designed), but they are significantly limited. We supply certain conditions (e.g., data) and ask a program to generate an illustration of those data for us. Then, we might place new restrictions on it (e.g., altered color schemes) to seek new ways of visualizing the information, but the system carries these tasks out based on its own design functionalities.

The author’s examples of visualization types based on intended emphasis (of space, as a map, and time, as a timeline) are particularly relevant to our work. The latter visualization may be helpful, for us, in showing how meme content changes over time across different subreddits when selecting for particular content facets (e.g., references to violence, references to politicians). We could also show concentration of meme facets at a single time point using a map.