{'fraction_non_alphanumeric': 0.02459016393442623, 'fraction_numerical': 0.009221311475409836, 'mean_word_length': 4.993865030674846, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Exploring Siri’s Content Diversity Using a Crowdsourced Audit





This study aims to describe the content diversity of Siri’s search results in the polarized context of US politics. To do so, a crowdsourced audit was conducted. A diverse sample of 170 US-based Siri users between the ages of 18-64 performed five identical queries about politically controversial issues. The data were analyzed using the concept of algorithmic bias. The results suggest that Siri’s search algorithm produces a long tail distribution of search results: Forty-two percent of the participants received the six most frequent answers, while 22% of the users received unique answers. These statistics indicate that Siri’s search algorithm causes moderate concentration and low fragmentation. The age and, surprisingly, the political orientation of users, do not seem to be driving either concentration or fragmentation. However, the users' gender and location appear to cause low concentration.



