Crowdsourced Temporal Data

Data attained through crowdsourcing have an essential role in the development of computer vision algorithms. Crowdsourced data might include reporting biases, since crowdworkers usually describe what is “worth saying" in addition to images’ content. We explore how the unprecedented events of 2020, including the unrest surrounding racial discrimination, and the COVID-19 pandemic, might be refected in responses to an open-ended annotation task on people images, originally executed in 2018 and replicated in 2020. Analyzing themes of Identity and Health conveyed in workers’ tags, we found evidence that supports the potential for temporal sensitivity in crowdsourced data. The 2020 data exhibit more race-marking of images depicting non-Whites, as well as an increase in tags describing Weight. We relate our findings to the emerging research on crowdworkers’ moods. This dataset includes all the tags, provided by crowdworkers, relevant to the topics of Health and Identity, providing aggregated counts of the occurrences of each tag in 2018 and 2020. Additionally, separate counts of the occurrences of each tag in 2018 and 2020 are provided for each depicted race (a.k.a., White, Latino, Black and Asian).