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.

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.

In traditional models for word-of-mouth recommendations and viral marketing, the objective function has generally been based on reaching as many people as possible. However, a number of studies have shown that the indiscriminate spread of a product by word-of-mouth can result in overexposure, reaching people who evaluate it negatively. This can lead to an effect in which the over-promotion of a product can produce negative reputational effects, by reaching a part of the audience that is not receptive to it. How should one make use of social influence when there is a risk of overexposure? In this paper, we develop and analyze a theoretical model for this process; we show how it captures a number of the qualitative phenomena associated with overexposure, and for the main formulation of our model, we provide a polynomial-time algorithm to find the optimal marketing strategy. We also present simulations of the model on real network topologies, quantifying the extent to which our optimal strategies outperform natural baselines.

When people share updates with their friends on Facebook they have varying expectations for the feedback they will receive. In this study, we quantitatively examine the factors contributing to feedback expectations and the potential outcomes of expectation fulfillment. We conducted two sets of surveys: one asking people about their feedback expectations immediately after posting on Facebook and the other asking how the amount of feedback received on a post matched the participant's expectations. Participants were more likely to expect feedback on content they evaluated as more important, and to a lesser extent more personal. Expectations also depended on participants' age, gender, and level of activity on Facebook. When asked about feedback expectations from specific friends, participants were more likely to expect feedback from closer friends, but expectations varied considerably based on recency of communication, geographical proximity, and the type of relationship (e.g. family, co-worker). Finally, receiving more feedback relative to expectations correlated with a greater feeling of connectedness to one's Facebook friends. The findings suggest implications for the theory and the design of social network sites.

Detecting large reshare cascades is an important problem in online social networks. There are a variety of attempts to model this problem, from using time series analysis methods to stochastic processes. Most of these approaches heavily depend on the underlying network features and use network information to detect the virality of cascades. In most cases, however, getting such detailed network information can be hard or even impossible.

No abstract or record of the panel discussion was made available for publication as part of the conference proceedings.

Summary form only given. The complete presentations were not made available for publication as part of the conference proceedings.These Keynotes speeches the following: Ideas and Inventions; Scalable Collective Reasoning for Richly Structured Socio-Behavioral Data; Computational Methods for Team Formation and Network structure and information diffusion.

The asynchronous nature of communications on social network sites creates a unique opportunity for studying how posting content interacts with individuals' engagement. This study focuses on the behavioral changes occurring hours before and after contribution to better understand the changing needs and preferences of contributors. Using observational data analysis of individuals' activity on Facebook, we test hypotheses regarding the motivations for site visits, changes in the distribution of attention to content, and shifts in decisions to interact with others. We find that after posting content people are intrinsically motivated to visit the site more often, are more attentive to content from friends (but not others), and choose to interact more with friends (in large part due to reciprocity). In addition, contributors are more active on the site hours before posting and remain more active for less than a day afterwards. Our study identifies a unique pattern of engagement that accompanies contribution and can inform the design of social network sites to better support contributors.

No abstract available.

Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.

Cascades of information-sharing are a primary mechanism by which content reaches its audience on social media, and an active line of research has studied how such cascades, which form as content is reshared from person to person, develop and subside. In this paper, we perform a large-scale analysis of cascades on Facebook over significantly longer time scales, and find that a more complex picture emerges, in which many large cascades recur, exhibiting multiple bursts of popularity with periods of quiescence in between. We characterize recurrence by measuring the time elapsed between bursts, their overlap and proximity in the social network, and the diversity in the demographics of individuals participating in each peak. We discover that content virality, as revealed by its initial popularity, is a main driver of recurrence, with the availability of multiple copies of that content helping to spark new bursts. Still, beyond a certain popularity of content, the rate of recurrence drops as cascades start exhausting the population of interested individuals. We reproduce these observed patterns in a simple model of content recurrence simulated on a real social network. Using only characteristics of a cascade's initial burst, we demonstrate strong performance in predicting whether it will recur in the future.

In March of 2013, 3 million Facebook users changed their profile picture to one of an equals sign to express support of same-sex marriage. We demonstrate that this action shows complex diffusion characteristics congruent with threshold models, with most users observing several of their friends changing their profile picture before taking the action themselves. While the number of friends played a role in the adoption dynamics, so did demographic characteristics and the general propensity of the individual to change their profile picture. We show via simulation that the adoption curve is consistent with a heterogeneous-threshold model, in which the probability of adoption depends on both the number of friends and the susceptibility of the individual.

Vast amounts of information are propagated in online social networks such as Facebook. This talk will describe several studies characterizing how information diffuses over social ties, from the growth of individual cascades to the predictability of their eventual size. It will also characterize the diffusion of specific kinds of information, including rumors, memes, and social movements.

The Gartner's 2014 Hype Cycle released last August moves Big Data technology from the Peak of Inflated Expectations to the beginning of the Trough of Disillusionment when interest starts to wane as reality does not live up to previous promises. As the hype is starting to dissipate it is worth asking what Big Data (however defined) means from a scientific perspective: Did the emergence of gigantic corpora exposed the limits of classical information retrieval and data mining and led to new concepts and challenges, the way say, the study of electromagnetism showed the limits of Newtonian mechanics and led to Relativity Theory, or is it all just "sound and fury, signifying nothing", simply a matter of scaling up well understood technologies? To answer this question, we have assembled a distinguished panel of eminent scientists, from both Industry and Academia: Lada Adamic (Facebook), Michael Franklin (University of California at Berkeley), Maarten de Rijke (University of Amsterdam), Eric Xing (Carnegie Mellon University), and Kai Yu (Baidu) will share their point of view and take questions from the moderator and the audience.

Apps are emerging as an important form of on-line content, and they combine aspects of Web usage in interesting ways --- they exhibit a rich temporal structure of user adoption and long-term engagement, and they exist in a broader social ecosystem that helps drive these patterns of adoption and engagement. It has been difficult, however, to study apps in their natural setting since this requires a simultaneous analysis of a large set of popular apps and the underlying social network they inhabit. In this work we address this challenge through an analysis of the collection of apps on Facebook Login, developing a novel framework for analyzing both temporal and social properties. At the temporal level, we develop a retention model that represents a user's tendency to return to an app using a very small parameter set. At the social level, we organize the space of apps along two fundamental axes --- popularity and sociality --- and we show how a user's probability of adopting an app depends both on properties of the local network structure and on the match between the user's attributes, his or her friends' attributes, and the dominant attributes within the app's user population. We also devolop models that show the importance of different feature sets with strong performance in predicting app success.

In this paper we present the first large-scale empirical study of how visually impaired people use online social networks, specifically Facebook. We identify a sample of 50K visually impaired users, and study the activities they perform, the content they produce, and the friendship networks they build on Facebook. We find that visually impaired users participate on Facebook (e.g. status updates, comments, likes) as much as the general population, and receive more feedback (i.e., comments and likes) on average on their content. By analyzing the content produced by visually impaired users, we find that they share their experience and issues related to vision impairment. We also identify distinctive patterns in their language and technology use. We also show that, compared to other users, visually impaired users have smaller social networks, but such differences have decreased over time. Our findings have implications for improving the utility and usability of online social networks for visually impaired users.

On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently unpredictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade's eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest.

Preventing contagion in networks is an important problem in public health and other domains. Targeting nodes to immunize based on their network interactions has been shown to be far more effective at stemming infection spread than immunizing random subsets of nodes. However, the assumption that selected nodes can be rendered completely immune does not hold for infections for which there is no vaccination or effective treatment. Instead, one can confer fractional immunity to some nodes by allocating variable amounts of infection-prevention resource to them. We formulate the problem to distribute a fixed amount of resource across nodes in a network such that the infection rate is minimized, prove that it is NP-complete and derive a highly effective and efficient linear-time algorithm. We demonstrate the efficiency and accuracy of our algorithm compared to several other methods using simulation on real-world network datasets including US-MEDICARE and state-level interhospital patient transfer data. We find that concentrating resources at a small subset of nodes using our algorithm is up to 6 times more effective than distributing them uniformly (as is current practice) or using network-based heuristics. To the best of our knowledge, we are the first to formulate the problem, use truly nation-scale network data and propose effective algorithms.

Sentiment is a rich and important dimension of social interaction. However, its presence in computer-mediated communication in corporate settings is not well understood. This paper provides a preliminary study of people's expression of sentiment in email conversations in an organizational context. The study reveals that sentiment levels evolve over time during the process of newcomers' socialization, that sentiment varies according to tie-strength with the recipient, and that sentiment patterns can be indicative of one's position in the corporate social network as well as job performance. These findings shed light on the complex and dynamic nature of sentiment patterns, and would inspire further explorations and applications of sentiment analysis in organizations.

Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends' information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.

Significant cultural differences persist between East and West. Software systems that have been proven to operate efficiently within one culture can fail in the context of the other, especially if they are intended to support rich social interactions. In this paper we demonstrate how a virtual currency system, not unlike ones employed by many US-based websites, evolved within a thriving Chinese online forum into an essential medium for extremely diverse and culturally specific social exchange activities. The social interactions reflect the traditional Chinese idea of guanxi, or interpersonal influence and connectedness, while at the same time incorporating the norms of a new generation of Internet users.

Large general-purposed community question-answering sites are becoming popular as a new venue for generating knowledge and helping users in their information needs. In this paper we analyze the characteristics of knowledge generation and user participation behavior in the largest question-answering online community in South Korea, Naver Knowledge-iN. We collected and analyzed over 2.6 million question/answer pairs from fifteen categories between 2002 and 2007, and have interviewed twenty six users to gain insights into their motivations,roles, usage and expertise. We find altruism, learning, and competency are frequent motivations for top answerers to participate, but that participation is often highly intermittent. Using a simple measure of user performance, we find that higher levels of participation correlate with better performance. We also observe that users are motivated in part through a point system to build a comprehensive knowledge database. These and other insights have significant implications for future knowledge generating online communities.

This paper describes our approach to assured information sharing. The research is being carried out under a MURI 9Multiuniversiyt Research Initiative) project funded by the Air Force Office of Scientific Research (AFOSR). The main objective of our project is: define, design and develop an Assured Information Sharing Lifecycle (AISL) that realizes the DoD's information sharing value chain. In this paper we describe the problem faced by the Department of Defense and our solution to developing an AISL System.

Social influence determines to a large extent what we adopt and when we adopt it. This is just as true in the digital domain as it is in real life, and has become of increasing importance due to the deluge of user-created content on the Internet. In this paper, we present an empirical study of user-to-user content transfer occurring in the context of a time-evolving social network in Second Life, a massively multiplayer virtual world.

Witkeys are a thriving type of web-based knowledge sharing market in China, supporting a form of crowdsourcing. In a Witkey site, users offer a small award for a solution to a task, and other users compete to have their solution selected.

Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one's curiosity about a countless number of things. In this paper, we seek to understand YA's knowledge sharing and activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users' interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics to predict, within a given category, whether a particular answer will be chosen as the best answer by the asker.

Blogs, wikis, tagging, podcasts, and social networking websites such as MySpace, Facebook, Flickr and YouTube have radically changed user interactions on the World Wide Web from a static, one-way, consumption model to a dynamic, multi-way, participation model. Broad user power and flexibility have changed how people engage in and experience their interconnections, interests, and collaborations. Online social interactions will evolve in the next decade to address the growing needs of its user community and make entries into many aspects of our lives. This evolution may very well be among the most exciting ones of our times where the individual and collective power of people to contribute and share content, experiences, ideas, expertise etc. may be even more enhanced than it is today. The enhancement may be shaped through a better understanding of user needs and behavior and it may be enabled through the seamless convergence of multi-modal technologies, new applications, new domains, data mining and better navigational and search capabilities. Some of these changes will also permeate into the workplace and change the way we work. This panel will discuss how online social interactions may evolve in the next decade and what impact it may have on diverse dimensions in our world.

Help-seeking communities have been playing an increasingly critical role in the way people seek and share information. However, traditional help-seeking mechanisms of these online communities have some limitations. In this paper, we describe an expertise-finding mechanism that attempts to alleviate the limitations caused by not knowing users' expertise levels. As a result of using social network data from the online community, this mechanism can automatically infer expertise level. This allows, for example, a question list to be personalized to the user's expertise level as well as to keyword similarity. We believe this expertise location mechanism will facilitate the development of next generation help-seeking communities.

Web-based communities have become important places for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small number of simple simulation rules governing the question-answer dynamic in the network. These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum, but also allow us to evaluate how other algorithms may perform in communities with different characteristics. We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities.

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective.

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We then establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies product and pricing categories for which viral marketing seems to be very effective.

Beyond serving as online diaries, Weblogs have evolved into a complex social structure, one which is in many ways ideal for the study of the propagation of information. As Weblog authors discover and republish information, we are able to use the existing link structure of blogspace to track its flow. Where the path by which it spreads is ambiguous, we utilize a novel inference scheme that takes advantage of data describing historical, repeating patterns of "infection." Our paper describes this technique as well as a visualization system that allows for the graphical tracking of information flow.

No abstract available.