What do we study when studying politics and democracy? A semantic analysis of how politics and democracy are treated in SIGCHI conference papers

TL;DR

The paper tackles semantic ambiguity in how SIGCHI papers treat politics and democracy by conducting a dual-method analysis: manual thematic coding of a large corpus (378 politics and 152 democracy papers) and computational triangulation via a structural topic model. It uncovers 13 themes and shows distinct orientation differences, including macro vs mezzo levels and observational vs activist positions, with a notable rise of terms over four decades and a dominance of academic community contexts. The study provides a nuanced map of how politics and democracy are invoked in HCI, highlighting semantic drift and the need for explicit framing to maintain cross-disciplinary progress. Its findings offer a foundation for a more pluralistic and societally relevant research agenda within the HCI community and SIGCHI venues.

Abstract

Human-computer interaction scholars are increasingly touching on topics related to politics or democracy. As these concepts are ambiguous, an examination of concepts' invoked meanings aids in the self-reflection of our research efforts. We conduct a thematic analysis of all papers with the word `politics' in abstract, title or keywords ($n$=378) and likewise 152 papers with the word `democracy.' We observe that these words are increasingly being used in human-computer interaction, both in absolute and relative terms. At the same time, we show that researchers invoke these words with diverse levels of analysis in mind: the early research focused on mezzo-level (i.e., small groups), but more recently the work has begun to include macro-level analysis (i.e., society and politics as played in the public sphere). After the increasing focus on the macro-level, we see a transition towards more normative and activist research, in some areas it replaces observational and empirical research. These differences indicate semantic differences, which -- in the worst case -- may limit scientific progress. We bring these differences visible to help in further exchanges of ideas and human-computer interaction community to explore how it orients itself to politics and democracy.

Figures (5)

• Figure 1: Semantic literature review and its relation to substantive domains.
• Figure 2: Literature selection was based on searching of keywords and conducting a minimal exclusion by removing duplicates and material which could not be classified.
• Figure 3: The four stages of the literature classification process -- development and refinement of the codebook (the first two stages), automated classification (the third stage), and creation of the final classification framework (the final stage).
• Figure 4: Temporal analysis of publications. Colours same as in Table \ref{['tab:rq2counts']}: user-generated content, media consumption, public participation, social movements, social issues, public services, organization and workplace, politics of research, technology democratization, design methods, data analysis methods, motivating context or background, academic community activities.
• Figure 5: Topics and the estimated proportions for their representation in the abstracts. Enlarge view for details.