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Even though a restaurant may receive different ratings across review platforms, people often see only one rating during a local search (e.g. 'best burgers near me'). In this paper, we examine the differences in ratings between two commonly used review platforms-Google Maps and Yelp. We found that restaurant ratings on Google Maps are, on average, 0.7 stars higher than those on Yelp, with the increase being driven in large part by higher ratings for chain restaurants on Google Maps. We also found extensive diversity in top-ranked restaurants by geographic region across platforms. For example, for a given metropolitan area, there exists little overlap in its top ten lists of restaurants on Google Maps and Yelp. Our results problematize the use of a single review platform in local search and have implications for end users of ratings and local search technologies. We outline concrete design recommendations to improve communication of restaurant evaluation and discuss the potential causes for the divergence we observed. 

The public is increasingly concerned about the practices of large technology companies with regards to privacy and many other issues. To force changes in these practices, there have been growing calls for “data strikes.” These new types of collective action would seek to create leverage for the public by starving business-critical models (e.g. recommender systems, ranking algorithms) of much-needed training data. However, little is known about how data strikes would work, let alone how effective they would be. Focusing on the important commercial domain of recommender systems, we simulate data strikes under a wide variety of conditions and explore how they can augment traditional boycotts. Our results suggest that data strikes can be effective and that users have more power in their relationship with technology companies than they do with other companies. However, our results also highlight important trade-offs and challenges that must be considered by potential organizers. 

Millions of people worldwide contribute content to peer production repositories that serve human information needs and provide vital world knowledge to prominent artificial intelligence systems. Yet, extreme gender participation disparities exist in which men significantly outnumber women. A central concern has been that due to self-focus bias, these disparities can lead to corresponding gender content disparities, in which content of interest to men is better represented than content of interest to women. This paper investigates the relationship between participation and content disparities in OpenStreetMap. We replicate findings that women are dramatically under-represented as OSM contributors, and observe that men and women contribute different types of content and do so about different places. However, the character of these differences confound simple narratives about self-focus bias: we find that on a proportional basis, men produced a higher proportion of contributions in feminized spaces compared to women, while women produced a higher proportion of contributions in masculinized spaces compared to men. 

Search engines are some of the most popular and profitable intelligent technologies in existence. Recent research, however, has suggested that search engines may be surprisingly dependent on user-created content like Wikipedia articles to address user information needs. In this paper, we perform a rigorous audit of the extent to which Google leverages Wikipedia and other user-generated content to respond to queries. Analyzing results for six types of important queries (e.g. most popular, trending, expensive advertising), we observe that Wikipedia appears in over 80% of results pages for some query types and is by far the most prevalent individual content source across all query types. More generally, our results provide empirical information to inform a nascent but rapidly-growing debate surrounding a highly consequential question: Do users provide enough value to intelligent technologies that they should receive more of the economic benefits from intelligent technologies?