Auditing Autocomplete - Suggestion Networks and Recursive Algorithm Interrogation.
WebSci '19: 11th ACM Conference on Web Science Boston Massachusetts USA June, 2019(2019)
摘要
Autocomplete algorithms, by design, steer inquiry. When a user provides a root input, such as a search query, these algorithms dynamically retrieve, curate, and present a list of related inputs, such as search suggestions. Although ubiquitous in online platforms, a lack of research addressing the ephemerality of their outputs and the opacity of their functioning raises concerns of transparency and accountability on where inquiry is steered. Here, we introduce recursive algorithm interrogation (RAI), a breadth-first search method for auditing autocomplete by recursively submitting a root query and its child suggestions to create a network of algorithmic associations. We used RAI to conduct a longitudinal audit of autocomplete on Google and Bing using a focused set of root queries -- the names of 38 US governors who were up for reelection -- during the summer of 2018. Comparing across search engines, we found a higher turnover rate among longer and lower ranked suggestions on both search engines, a higher prevalence of social media websites in Google's suggestions, a higher prevalence of words classified as a swear or a negative emotion in Bing's suggestions, and periodic shocks that spanned across most of our root queries. We open source our code for conducting RAI and discuss how it could be applied to other platforms, topics, and settings.
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关键词
search queries, autocomplete, suggestions, algorithm auditing
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