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Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its monetary value to make apparent volunteer labor’s value. Using a novel dataset of private logs generated by moderators, we use linear mixed-effect regression and estimate that Reddit moderators worked a minimum of 466 hours per day in 2020. These hours are worth 3.4 million USD based on the median hourly wage for comparable content moderation services in the U.S. We discuss how this information may inform pathways to alleviate the one-sided relationship between technology companies and online volunteers. 

Online volunteers are an uncompensated yet valuable labor force for many social platforms. For example, volunteer content moderators perform a vast amount of labor to maintain online communities. However, as social platforms like Reddit favor revenue generation and user engagement, moderators are under-supported to manage the expansion of online communities. To preserve these online communities, developers and researchers of social platforms must account for and support as much of this labor as possible. In this paper, we quantitatively characterize the publicly visible and invisible actions taken by moderators on Reddit, using a unique dataset of private moderator logs for 126 subreddits and over 900 moderators. Our analysis of this dataset reveals the heterogeneity of moderation work across both communities and moderators. Moreover, we find that analyzing only visible work – the dominant way that moderation work has been studied thus far – drastically underestimates the amount of human moderation labor on a subreddit. We discuss the implications of our results on content moderation research and social platforms. 

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.