Dan Cosley

Using Social Psychology to Motivate Contributions to Online Communities

Under-contribution is a problem for many online communities. Social psychology theories of social loafing and goal-setting can lead to mid-level design goals to address this problem. We tested design principles derived from these theories in four field experiments involving members of an online movie recommender community. In each of the experiments participated were given different explanations for the value of their contributions. As predicted by theory, individuals contributed when they were reminded of their uniqueness and when they were given specific and challenging goals. However, other predictions were disconfirmed. For example, in one experiment, participants given group goals contributed more than those given individual goals. The article ends with suggestions and challenges for mining design implications from social science theories.

Feedback effects between similarity and social influence in online communities

A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties. People are similar to their neighbors in a social network for two distinct reasons: first, they grow to resemble their current friends due to social influence; and second, they tend to form new links to others who are already like them, a process often termed selection by sociologists. While both factors are present in everyday social processes, they are in tension: social influence can push systems toward uniformity of behavior, while selection can lead to fragmentation. As such, it is important to understand the relative effects of these forces, and this has been a challenge due to the difficulty of isolating and quantifying them in real settings. We develop techniques for identifying and modeling the interactions between social influence and selection, using data from online communities where both social interaction and changes in behavior over time can be measured. We find clear feedback effects between the two factors, with rising similarity between two individuals serving, in aggregate, as an indicator of future interaction -- but with similarity then continuing to increase steadily, although at a slower rate, for long periods after initial interactions. We also consider the relative value of similarity and social influence in modeling future behavior. For instance, to predict the activities that an individual is likely to do next, is it more useful to know the current activities of their friends, or of the people most similar to them?

PolyLens: a recommender system for groups of users

We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email

Getting to know you: learning new user preferences in recommender systems

Recommender systems have become valuable resources for users seeking intelligent ways to search through the enormous volume of information available to them. One crucial unsolved problem for recommender systems is how best to learn about a new user. In this paper we study six techniques that collaborative filtering recommender systems can use to learn about new users. These techniques select a sequence of items for the collaborative filtering system to present to each new user for rating. The techniques include the use of information theory to select the items that will give the most value to the recommender system, aggregate statistics to select the items the user is most likely to have an opinion about, balanced techniques that seek to maximize the expected number of bits learned per presented item, and personalized techniques that predict which items a user will have an opinion about. We study the techniques thru offline experiments with a large pre-existing user data set, and thru a live experiment with over 300 users. We show that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.

Inferring social ties from geographic coincidences

We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals’ physical locations over time.

On the recommending of citations for research papers

Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain.

Pensieve: supporting everyday reminiscence

Reminiscing is a valuable activity that people of all ages spontaneously and informally partake in as part of their everyday lives. This paper discusses the design and use of Pensieve, a system that supports everyday reminiscence by emailing memory triggers to people that contain either social media content they previously created on third-party websites or text prompts about common life experiences. We discuss how the literature on reminiscence informed Pensieves design, then analyze data from 91 users over five months. We find that people value spontaneous reminders to reminisce as well as the ability to write about their reminiscing. Shorter, more general triggers draw more responses, as do triggers containing peoples own photos-although responses to photos tended to contain more metadata elements than storytelling elements. We compare these results to data from a second, Pensieve-like system developed for Facebook, and suggest a number of important aspects to consider for both designers and researchers around technology and reminiscence.

Using intelligent task routing and contribution review to help communities build artifacts of lasting value

Many online communities are emerging that, like Wikipedia, bring people together to build community-maintained artifacts of lasting value (CALVs). Motivating people to contribute is a key problem because the quantity and quality of contributions ultimately determine a CALVs value. We pose two related research questions: 1) How does intelligent task routing---matching people with work---affect the quantity of contributions? 2) How does reviewing contributions before accepting them affect the quality of contributions? A field experiment with 197 contributors shows that simple, intelligent task routing algorithms have large effects. We also model the effect of reviewing contributions on the value of CALVs. The model predicts, and experimental data shows, that value grows more slowly with review before acceptance. It also predicts, surprisingly, that a CALV will reach the same final value whether contributions are reviewed before or after they are made available to the community.

Managing Impressions and Relationships on Facebook: Self- Presentational and Relational Concerns Revealed Through the Analysis of Language Style

The merging of audiences in social media and the variety of participation structures they present, including different audience sizes and interaction targets, pose questions about how people respond to these new communication situations. This research examined self-presentational and relational concerns through the analysis of language styles on Facebook. The authors collected a corpus of status updates, wall posts, and private messages from 79 participants. These messages varied in certain characteristics of language style, revealing differences in underlying self-presentational and relational concerns based on the publicness and directedness of the interaction. Positive emotion words correlated with self-reported self-presentational concerns in status updates, suggesting a strategic use of sharing positive emotions in public and nondirected communication via status updates. Verbal immediacy correlated with partner familiarity in wall posts but not in private messages, suggesting that verbal immediacy cues serve as markers to differentiate between more and less familiar partners in public wall posts.

REFEREE: an open framework for practical testing of recommender systems using ResearchIndex

Automated recommendation (e.g., personalized product recommendation on an ecommerce web site) is an increasingly valuable service associated with many databases--typically online retail catalogs and web logs. Currently, a major obstacle for evaluating recommendation algorithms is the lack of any standard, public, real-world testbed appropriate for the task. In an attempt to fill this gap, we have created REFEREE, a framework for building recommender systems using ResearchIndex--a huge online digital library of computer science research papers--so that anyone in the research community can develop, deploy, and evaluate recommender systems relatively easily and quickly. Research Index is in many ways ideal for evaluating recommender systems, especially so-called hybrid recommenders that combine information filtering and collaborative filtering techniques. The documents in the database are associated with a wealth of content information (author, title, abstract, full text) and collaborative information (user behaviors), as well as linkage information via the citation structure. Our framework supports more realistic evaluation metrics that assess user buy-in directly, rather than resorting to offline metrics like prediction accuracy that may have little to do with end user utility. The sheer scale of ResearchIndex (over 500,000 documents with thousands of user accesses per hour) will force algorithm designers to make real-world trade-offs that consider performance, not just accuracy. We present our own tradeoff decisions in building an example hybrid recommender called PD-Live. The algorithm uses content-based similarity information to select a set of documents from which to recommend, and collaborative information to rank the documents. PD-Live performs reasonably well compared to other recommenders in ResearchIndex.