Lada A. Adamic’s research while affiliated with Meta and other places

We use de-identified data from Facebook Groups to study and provide a descriptive analysis of local gift-giving communities, in particular Buy Nothing groups. These communities allow people to give items they no longer need, reduce waste, and connect to local community. Millions of people have joined Buy Nothing groups on Facebook, with an increasing pace through the COVID-19 pandemic. Buy Nothing groups are more popular in dense and urban US counties with higher educational attainment. Compared to other local groups, Buy Nothing groups have lower Facebook friendship densities, suggesting they bring together people who are not already connected. The interaction graphs in Buy Nothing groups form larger strongly connected components, indicative of norms of generalized reciprocity. The interaction patterns in Buy Nothing groups are similar to other local online gift-giving groups, with names containing terms such as `free stuff" and `pay it forward". This points to an interaction signature for local online gift-giving communities.

We present a de-identified and aggregated dataset based on geographical patterns of Facebook Groups usage and demonstrate its association with measures of social capital. The dataset is aggregated at United States county level. Established spatial measures of social capital are known to vary across US counties. Their availability and recency depends on running costly surveys. We examine to what extent a dataset based on usage patterns of Facebook Groups, which can be generated at regular intervals, could be used as a partial proxy by capturing local online associations. We identify four main latent factors that distinguish Facebook group engagement by county, obtained by exploratory factor analysis. The first captures small and private groups, dense with friendship connections. The second captures very local and small groups. The third captures non-local, large, public groups, with more age mixing. The fourth captures partially local groups of medium to large size. Only two of these factors, the first and third, correlate with offline community level social capital measures, while the second and fourth do not. Together and individually, the factors are predictive of offline social capital measures, even controlling for various demographic attributes of the counties. To our knowledge this is the first systematic test of the association between offline regional social capital and patterns of online community engagement in the same regions. By making the dataset available to the research community, we hope to contribute to the ongoing studies in social capital.

We use de-identified data from Facebook Groups to study and provide a descriptive analysis of local gift-giving communities, in particular buy nothing (BN) groups. These communities allow people to give items they no longer need, reduce waste, and connect to local community. Millions of people have joined BN groups on Facebook, with an increasing pace through the COVID-19 pandemic. BN groups are more popular in dense and urban US counties with higher educational attainment. Compared to other local groups, BN groups have lower Facebook friendship densities, suggesting they bring together people who are not already connected. The interaction graphs in BN groups form larger strongly connected components, indicative of norms of generalized reciprocity. The interaction patterns in BN groups are similar to other local online gift-giving groups, with names containing terms such as `free stuff" and `pay it forward". This points to an interaction signature for local online gift-giving communities.

We use exploratory factor analysis to investigate the online persistence of known community-level patterns of social capital variance in the U.S. context. Our analysis focuses on Facebook groups, specifically those that tend to connect users in the same local area. We investigate the relationship between established, localized measures of social capital at the county level and patterns of participation in Facebook groups in the same areas. We identify four main factors that distinguish Facebook group engagement by county. The first captures small, private groups, dense with friendship connections. The second captures very local and small groups. The third captures non-local, large, public groups, with more age mixing. The fourth captures partially local groups of medium to large size. The first and third factor correlate with community level social capital measures, while the second and fourth do not. Together and individually, the factors are predictive of offline social capital measures, even controlling for various demographic attributes of the counties. Our analysis reveals striking patterns of correlation between established measures of social capital and patterns of online interaction in local Facebook groups. To our knowledge this is the first systematic test of the association between offline regional social capital and patterns of online community engagement in the same regions.

People assume different and important roles within social networks. Some roles have received extensive study: that of influencers who are well-connected, and that of brokers who bridge unconnected parts of the network. However, very little work has explored another potentially important role, that of creating opportunities for people to interact and facilitating conversation between them. These individuals bring people together and act as social catalysts. In this paper, we test for the presence of social catalysts on the online social network Facebook. We first identify posts that have spurred conversations between the poster's friends and summarize the characteristics of such posts. We then aggregate the number of catalyzed comments at the poster level, as a measure of the individual's "catalystness." The top 1% of such individuals account for 31% of catalyzed interactions, although their network characteristics do not differ markedly from others who post as frequently and have a similar number of friends. By collecting survey data, we also validate the behavioral measure of catalystness: a person is more likely to be nominated as a social catalyst by their friends if their posts prompt discussions between other people more frequently. The measure, along with other conversation-related features, is one of the most predictive of a person being nominated as a catalyst. Although influencers and brokers may have gotten more attention for their network positions, our findings provide converging evidence that another important role exists and is recognized in online social networks.

"Witkeys" are websites in China that form a rapidly growing web-based knowledge market. A user who posts a task also offers a small fee, and many other users submit their answers to compete. The Witkey sites fall in-between aspects of the now-defunct Google Answers (vetted experts answer questions for a fee) and Yahoo Answers (anyone can answer or ask a question). As such, these sites promise new possibilities for knowledge-sharing online communities, perhaps fostering the freelance marketplace of the future. In this paper, we investigate one of the biggest Witkey websites in China, Taskcn.com. In particular, we apply social network prestige measures to a novel construction of user and task networks based on competitive outcomes to discover the underlying properties of both users and tasks. Our results demonstrate the power of this approach: Our analysis allows us to infer relative expertise of the users and provides an understanding of the participation structure in Taskcn. The results suggest challenges and opportunities for this kind of knowledge sharing medium.

The news media play an important role in informing the public on current events. Yet it has been difficult to understand the comprehensiveness of news media coverage on an event and how the reactions that the coverage evokes may diverge, because this requires identifying the origin of an event and tracing the information all the way to individuals who consume the news. In this work, we pinpoint the information source of an event in the form of a press release and investigate how its news cycle unfolds. We follow the news through three layers of propagation: the news articles covering the press release, shares of those articles in social media, and comments on the shares. We find that a news cycle typically lasts two days. Although news media in aggregate cover the information contained in the source, a single news article will typically only provide partial coverage. Sentiment, while dampened in news coverage relative to the source, again rises in social media shares and comments. As the information propagates through the layers, it tends to diverge from the source: while some ideas emphasized in the source fade, others emerge or gain in importance. We also discover how far the news article is from the information source in terms of sentiment or language does not help predict its popularity.

Social media is playing an increasingly vital role in information dissemination. But with dissemination being more distributed, content often makes multiple hops, and consequently has opportunity to change. In this paper we focus on content that should be changing the least, namely quoted text. We find changes to be frequent, with their likelihood depending on the authority of the copied source and the type of site that is copying. We uncover patterns in the rate of appearance of new variants, their length, and popularity, and develop a simple model that is able to capture them. These patterns are distinct from ones produced when all copies are made from the same source, suggesting that information is evolving as it is being processed collectively in online social media.

In this work, we study the use of Twitter by House, Senate and gubernatorial candidates during the midterm (2010) elections in the U.S. Our data includes almost 700 candidates and over 690k documents that they produced and cited in the 3.5 years leading to the elections. We utilize graph and text mining techniques to analyze differences between Democrats, Republicans and Tea Party candidates, and suggest a novel use of language modeling for estimating content cohesiveness. Our findings show significant differences in the usage patterns of social media, and suggest conservative candidates used this medium more effectively, conveying a coherent message and maintaining a dense graph of connections. Despite the lack of party leadership, we find Tea Party members display both structural and language-based cohesiveness. Finally, we investigate the relation between network structure, content and election results by creating a proof-of-concept model that predicts candidate victory with an accuracy of 88.0%.

Online social networking tools are used around the world by people to ask questions of their friends, because friends provide direct, reliable, contextualized, and interactive responses. However, although the tools used in different cultures for question asking are often very similar, the way they are used can be very different, reflecting unique inherent cultural characteristics. We present the results of a survey designed to elicit cultural differences in people’s social question asking behaviors across the United States, the United Kingdom, China, and India. The survey received responses from 933 people distributed across the four countries who held similar job roles and were employed by a single organization. Responses included information about the questions they ask via social networking tools, and their motivations for asking and answering questions online. The results reveal culture as a consistently significant factor in predicting people’s social question and answer behavior. The prominent cultural differences we observe might be traced to people’s inherent cultural characteristics (e.g., their cognitive patterns and social orientation), and should be comprehensively considered in designing social search systems.