What Do We Mean by “Interaction”? An Analysis of 35 Years of CHI
Bibliographic record
Abstract
The notion of interaction is essential to human-computer interaction, yet rarely studied. We use quantitative and qualitative methods to investigate how this notion has been used across 35 years of proceedings from the ACM Conference on Human Factors in Computing (CHI). Using natural language processing, we extract 53,568 occurrences of the word “interaction” across 4,604 papers. In these occurrences, we categorize 2,668 unique words that modify how “interaction” is used in a sentence. We show that the use of “interaction” is both increasing and diversifying, suggesting the importance of the notion, but also the difficulty in developing theory about interaction. Our findings show that styles of interaction are closely associated with changes in technology and that modalities and characteristics of interaction are becoming more of a topic than specific devices or widgets. Interaction qualities, relating to structure, feel, effectiveness, and efficiency, are consistently prominent, and the quality of novelty is increasingly frequent. From this analysis, we identify open questions about interaction, including how to build knowledge across changing technologies, how to work toward a model of quality for interaction, and what the core of a science of interaction could be.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.048 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".