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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. 

Tech users currently have limited ability to act on concerns regarding the negative societal impacts of large tech companies. However, recent work suggests that users can exert leverage using their role in the generation of valuable data, for instance by withholding their data contributions to intelligent technologies. We propose and evaluate a new means to exert this type of leverage against tech companies: "conscious data contribution" (CDC). Users who participate in CDC exert leverage against a target tech company by contributing data to technologies operated by a competitor of that company. Using simulations, we find that CDC could be highly effective at reducing the gap in intelligent technologies performance between an incumbent and their competitors. In some cases, just 20% of users contributing data they have produced to a small competitor could help that competitor get 80% of the way towards the original company's best-case performance. We discuss the implications of CDC for policymakers, tech designers, and researchers. 

The rise of geotargeted online advertising has disrupted the business model of local journalism, but it remains ambiguous whether online advertising platforms can effectively reach local audiences. To address this ambiguity, we present a focused study auditing the positional accuracy of geotargeted display advertisements on Google. We measure the frequency and severity of geotargeting errors by targeting display ads to random ZIP codes across the United States, collecting self-reported location information from users who click on the advertisement. We find evidence that geotargeting errors are common, but minor in terms of advertising goals. While 41% of respondents lived outside the target ZIP code, only 11% lived outside the target county, and only 2% lived outside the target state. We also present details regarding a high volume of suspicious clicks in our data, which made the cost per sample extremely expensive. The paper concludes by discussing implications for advertisers, the business of local journalism, and future research. 

A growing body of work has highlighted the important role that Wikipedia's volunteer-created content plays in helping search engines achieve their core goal of addressing the information needs of hundreds of millions of people. In this paper, we report the results of an investigation into the incidence of Wikipedia links in search engine results pages (SERPs). Our results extend prior work by considering three U.S. search engines, simulating both mobile and desktop devices, and using a spatial analysis approach designed to study modern SERPs that are no longer just "ten blue links". We find that Wikipedia links are extremely common in important search contexts, appearing in 67-84% of desktop SERPs for common and trending queries, but less often for medical queries. Furthermore, we observe that Wikipedia links often appear in "Knowledge Panel" SERP elements and are in positions visible to users without scrolling, although Wikipedia appears less often and in less prominent positions on mobile devices. Our findings reinforce the complementary notions that (1) Wikipedia content and research has major impact outside of the Wikipedia domain and (2) powerful technologies like search engines are highly reliant on free content created by volunteers. 

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.