  Figures in scientific publications contain important information and results,
and alt text is needed for blind and low vision readers to engage with their
content. We conduct a study to characterize the semantic content of alt text in
HCI publications based on a framework introduced by Lundgard and Satyanarayan.
Our study focuses on alt text for graphs, charts, and plots extracted from HCI
and accessibility publications; we focus on these communities due to the lack
of alt text in papers published outside of these disciplines. We find that the
capacity of author-written alt text to fulfill blind and low vision user needs
is mixed; for example, only 50% of alt texts in our sample contain information
about extrema or outliers, and only 31% contain information about major trends
or comparisons conveyed by the graph. We release our collected dataset of
author-written alt text, and outline possible ways that it can be used to
develop tools and models to assist future authors in writing better alt text.
Based on our findings, we also discuss recommendations that can be acted upon
by publishers and authors to encourage inclusion of more types of semantic
content in alt text.
