Hae-Na Lee
Stony Brook University, Stony Brook, New York, United States
Most people who are blind interact with social media content with the assistance of a screen reader, a software that converts text to speech. However, the language used in social media is well-known to contain several informal out-of-vocabulary words (e.g., abbreviations, wordplays, slang), many of which do not have corresponding standard pronunciations. The narration behavior of screen readers for such out-of-vocabulary words and the corresponding impact on the social media experience of blind screen reader users are still uncharted research territories. Therefore we seek to plug this knowledge gap by examining how current popular screen readers narrate different types of out-of-vocabulary words found on Twitter, and also, how the presence of such words in tweets influences the interaction behavior and comprehension of blind screen reader users. Our investigation showed that screen readers rarely autocorrect out-of-vocabulary words, and moreover they do not always exhibit ideal behavior for certain prolific types of out-of-vocabulary words such as acronyms and initialisms. We also observed that blind users often rely on tedious and taxing workarounds to comprehend actual meanings of out-of-vocabulary words. Informed by the observations, we finally discuss methods that can potentially reduce this interaction burden for blind users on social media.
https://dl.acm.org/doi/abs/10.1145/3491102.3501958
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)