Social media users create immense value for various technologies such as search engines, content platforms, and most recently, generative AI models. Among social media users, those who produce content professionally, such as artists and creative workers, have expressed strong concerns about unauthorized reuse of their content; however, it remains unclear how the vast majority of casual users perceive content reuse, in particular for AI training. To gain a comprehensive understanding of users' perspectives, we surveyed 478 Instagram users from a university network about how much they value their content for commercial reuse and their acceptance of specific content reuse scenarios. We found that under reverse auction, in which the lowest bidder wins, respondents' evaluation of their content for commercial reuse is highly skewed, with a median of $62 and a long tail of more than thousands of dollars. A nontrivial percentage of respondents found content reuse for search engine display, education, and academic research acceptable. Conversely, content reuse for commercial or non-profit generative AI models was a negotiable scenario for most respondents. Most notably, compensation was likely to make almost all scenarios more acceptable to users. Our work highlights the diverse, contextually dependent perspectives that users have. We discuss how design, policy, and market practices might support contextually aware content reuse.

Online communities and their host platforms are mutually dependent yet conflict-prone. When platform policies clash with community values, communities have resisted through strikes, blackouts, and even migration to other platforms. Through such collective actions, communities have sometimes won concessions, but these have frequently proved to be temporary. Although previous research has investigated strike events and migration chains, the processes by which community-platform conflict unfolds remain obscure. How do community-platform relationships deteriorate? How do communities organize collective action? How do the participants proceed in the aftermath? We investigate a conflict between the Stack Exchange platform and community that occurred in 2023 around an emergency arising from the release of large language models (LLMs). Based on a qualitative thematic analysis of 2,070 messages from Meta Stack Exchange and 14 interviews with community members, we reveal how the 2023 conflict was preceded by a long-term deterioration in the community-platform relationship, driven in particular by the platform’s disregard for the community’s highly valued participatory role in governance. Moreover, the platform’s policy response to LLMs aggravated the community’s sense of crisis, triggering strike mobilization. We analyze how the mobilization was coordinated through a tiered leadership and communication structure, as well as how community members pivoted in the aftermath. Building on recent theoretical scholarship in social computing, we use Hirschman’s exit, voice, and loyalty framework to theorize the challenges of community-platform relations evinced in our data. Finally, we recommend ways that platforms and communities can institute participatory governance to be durable and effective.

This position paper argues that there is an urgent need to restructure markets for the information that goes into AI systems. Specifically, small-to-medium sized producers of information (such as journalists, news organizations, researchers, and creative professionals) need to be able to appoint representatives who can carry out "collective bargaining" with AI product builders in order to receive a reasonable terms and a fair return on the informational value they contribute. Obstacles to this market structure can be removed through technical work that facilitates collective bargaining in the information economy (e.g., explainable data value estimation and federated data management tools) and regulatory/policy interventions (e.g., support for trusted data intermediary organizations that represent guilds or syndicates of information producers). We argue that without collective bargaining in the information economy, AI will exacerbate a large-scale "information market failure" that will lead not only to undesirable concentration of capital, but also to a potential "ecological collapse" in the informational commons. On the other hand, collective bargaining in the information economy can create market conditions necessary for a pro-social AI future. We provide concrete actions that can be taken to support a coalition-based approach to achieve this.

Wikidata, an open structured database and a sibling project to Wikipedia, has recently become an important platform for information professionals to share structured metadata from their memory institutions-organizations that maintain public knowledge and cultural heritage materials. While studies have investigated why and how peer producers contribute to Wikidata, the institutional motivations and practices of these organizations are less understood. Given Wikidata's potential role in linking and supporting knowledge infrastructures and open data systems, we examined why and how information professionals in memory institutions use Wikidata as part of their organizational workflow. Through interviews with 15 participants, we identified the three archetypal roles of Wikidata users within memory institutions-providers, acquirers, and mutualists-and the different types of contributions that these institutions bring to Wikidata. We then explored potential collaboration opportunities between memory institutions and other volunteers in Wikidata, discussed the value of the data work conducted by these professionals, and examined how and why they track their contributions. Our work contributes to the wider discussions around collaboration and data work in CSCW by 1) studying the motivations and practices of information professionals, their differences from those doing volunteer work, and opportunities for the Wikidata community to promote more collaborative efforts within memory institutions and with other volunteers and 2) drawing attention to the important data work done by memory institutions on Wikidata and pointing out opportunities to support the contributions of information professionals.

As new machine learning methods demand larger training datasets, researchers and developers face significant challenges in dataset management. Although ethics reviews, documentation, and checklists have been established, it remains uncertain whether consistent dataset management practices exist across the community. This lack of a comprehensive overview hinders our ability to diagnose and address fundamental tensions and ethical issues related to managing large datasets. We present a systematic review of datasets published at the NeurIPS Datasets and Benchmarks track, focusing on four key aspects: provenance, distribution, ethical disclosure, and licensing. Our findings reveal that dataset provenance is often unclear due to ambiguous filtering and curation processes. Additionally, a variety of sites are used for dataset hosting, but only a few offer structured metadata and version control. These inconsistencies underscore the urgent need for standardized data infrastructures for the publication and management of datasets.

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 i¬¬n 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.

Researchers and the media have become increasingly interested in protest users, or people who change (protest use) or stop (protest non-use) their use of a company's products because of the company's values and/or actions. Past work has extensively engaged with the phenomenon of technology non-use but has not focused on non-use (nor changed use) in the context of protest. With recent research highlighting the potential for protest users to exert leverage against technology companies, it is important for technology stakeholders to understand the prevalence of protest users, their motivations, and the specific tactics they currently use. In this paper, we report the results of two surveys (n = 463 and n = 398) of representative samples of American web users that examine if, how, and why people have engaged in protest use and protest non-use of the products of five major technology companies. We find that protest use and protest non-use are relatively common, with 30% of respondents in 2019 reporting they were protesting at least one major tech company. Furthermore, we identify that protest users' most common motivations were (1) concerns about business models that profit from user data and (2) privacy; and the most common tactics were (1) stopping use and (2) leveraging ad blockers. We also identify common challenges and roadblocks faced by active and potential protest users, which include (1) losing social connections and (2) the lack of alternative products. Our results highlight the growing importance of protest users in the technology ecosystem and the need for further social computing research into this phenomenon. We also provide concrete design implications for existing and future technologies to support or account for protest use and protest non-use.

Protests are important social forms of activism, but can be inaccessible to people with disabilities. Online activism, like the 2017 Disability March, has provided alternative venues for involvement in accessible protesting and social movements. In this study, we use identity theory as a lens to understand why and how disabled activists engaged in an online movement, and its impact on their self-concepts. We interviewed 18 disabled activists about their experiences with online protesting during the Disability March. Respondents' identities (as both disabled individuals and as activists) led them to organize or join the March, evolved alongside the group's actions, and were reprioritized or strained as a result of their involvement. Our findings describe the values and limitations of this activism to our respondents, highlight the tensions they perceived about their activist identities, and present opportunities to support further accessibility and identity changes by integrating technology into their activist experiences.

In the digital age, social media has become a popular venue for nonprofit organizations to advocate for causes and promote social change. The 2016 United States Presidential Election occurred amidst divisive public opinions and political uncertainties for immigrants and immigration policies were a frequently-contested debate focus. Thus, this election provided an opportunity to examine nonprofit organizations' social media usage during political conflicts. We analyzed social media posts by immigrant-focused nonprofit organizations and conducted interviews probing into how they managed their online presence and social relations. This study finds that these nonprofit organizations adopted three key strategies to support their target community: 1) disseminating content about immigration-related issues and policies; 2) calling for participation in collective endeavors to influence the political climate; 3) engaging in conversations with outside stakeholders including political actors, media, and other organizations. We use empowerment theory, which has been used widely to study marginalized populations, as a theoretical lens to discuss how NPOs' social media usage on Twitter reflects their endeavors to bring information and calls to action to immigrant communities. We, then, present design opportunities to amplify the advantages of social media to help nonprofit organizations better serve their communities in times of political upheavals.

GrabYourWallet, #boycottNRA and other online boycott campaigns have attracted substantial public interest in recent months. However, a number of significant challenges are preventing online boycotts from reaching their potential. In particular, complex webs of brands and subsidiaries can make it difficult for participants to conform to the goals of a boycott. Similarly, participants and organizers have limited visibility into a boycott's progress. This affects their ability to use sociotechnical innovations from social computing to incentivize participation. To address these challenges, this paper makes a system contribution: a new boycott tool called Out of Site . Out of Site uses lightweight automation to remove obstacles to successful online boycotts. We describe the design challenges associated with Out of Site and report results from two phases of deployment with the GrabYourWallet and Stop Animal Testing boycott communities. Our findings highlight the potential of boycott-assisting technologies and inform the design of this new class of technologies. Finally, like is the case for many systems in social computing, while we designed Out of Site for pro-social uses, there are a number of easily predictable ways in which the system can be leveraged for anti-social purposes (e.g. exacerbating filter bubble issues, empowering boycotts of businesses owned by racial, ethnic, and religious minorities). As such, we developed for this project a new, very straightforward design approach that treats preventing these anti-social uses as a top-tier design concern. This approach stands in contrast to the status quo of ignoring potential anti-social uses and/or considering them to be a secondary design priority. We discuss how our simple approach may help other research projects reduce their potential negative impacts with minimal burden.

Technology-mediated public activism has grown popular in recent years with the high uptake of social media. Facebook and Twitter have become venues for activists to participate in online activism, or organize offline activism events. However, due to accessibility barriers in physical environments and accessibility issues in social media, people with disabilities continue to face challenges when they engage with such social movements. We interviewed 22 disabled activists about how they used technology to mediate civic engagement and barriers they faced. We present preliminary findings from these interviews and describe a potential solution named ActVirtual, a mobile platform for accessible activism. Our future work will include implementing and testing ActVirtual with users to make online and offline activism more accessible.

Social media platforms are existing online spaces where users share their daily encounters, providing a large dataset of photographs of inaccessible environments. We analyzed 100 posts from Twitter and Instagram that describe accessibility problems. Our findings suggest these posts are helpful to locate, identify and communicate accessibility problems, and provide design ideas for potential assistive technologies. We suggest design implications using social media posts to improve physical accessibility.