Individuals’ access to information in a social network depends on how it is distributed and where in the network individuals position themselves. In addition, individuals vary in how much effort they invest in managing their social connections. Using data from a social media site, we study how the interplay between effort and network position affects social media users’ access to diverse and novel information. Previous studies of the role of networks in information access were limited in their ability to measure the diversity of information. We address this problem by learning the topics of interest to social media users from the messages they share online with followers. We use the learned topics to measure the diversity of information users receive from the people they follow online. We confirm that users in structurally diverse network positions, which bridge otherwise disconnected regions of the follower network, tend to be exposed to more diverse and novel information. We also show that users who invest more effort in their activity on the site are not only located in more structurally diverse positions within the network than the less engaged users but also receive more novel and diverse information when in similar network positions. These findings indicate that the relationship between network structure and access to information in networks is more nuanced than previously thought.

Probabilistic models can learn users' preferences from the history of their item adoptions on a social media site, and in turn, recommend new items to users based on learned preferences. However, current models ignore psychological factors that play an important role in shaping online social behavior. One such factor is attention, the mechanism that integrates perceptual and cognitive features to select the items the user will consciously process and may eventually adopt. Recent research has shown that people have finite attention, which constrains their online interactions, and that they divide their limited attention non-uniformly over other people. We propose a collaborative topic regression model that incorporates limited, non-uniformly divided attention. We show that the proposed model is able to learn more accurate user preferences than state-of-art models, which do not take human cognitive factors into account. Specifically we analyze voting on news items on the social news aggregator and show that our model is better able to predict held out votes than alternate models. Our study demonstrates that psycho-socially motivated models are better able to describe and predict observed behavior than models which only consider latent social structure and content.

The emergence of social media creates a unique opportunity for developing a new class of crowd-powered information collection systems. Such systems actively identify potential users based on their public social media posts and solicit them directly for information. While studies have shown that users will respond to solicitations in a few domains, there is little analysis of the quality of information received. Here we explore the quality of information solicited from Twitter users in the domain of product reviews, specifically reviews for a popular tablet computer and L.A.-based food trucks. Our results show that the majority of responses to our questions (>70%) contained relevant information and often provided additional details (>37%) beyond the topic of the question. We compare the solicited Twitter reviews to other user-generated reviews from Amazon and Yelp, and found that the Twitter answers provided similar information when controlling for the questions asked. Our results also reveal limitations of this new information collection method, including its suitability in certain domains and potential technical barriers to its implementation. Our work provides strong evidence for the potential of this new class of information collection systems and design implications for their future use.

When people have questions, they often turn to their social network for answers. If the answer is obscure or time sensitive however, no members of their social networks may know the answer. For example, it may be difficult to find a friend who has experience with a particular feature or model of digital camera or who knows the current wait time for security at the local airport. In this paper, we explore the feasibility of answering questions by asking strangers. In this approach, strangers with potentially useful information are identified by mining the public status updates posted on Twitter, questions are sent to these strangers, and responses are collected. We explore feasibility in two ways: will users respond to questions sent by strangers and, if they do respond, how long must we wait for a response? Our results from asking 1159 questions across two domains suggest that 42% of users will respond to questions from strangers. 44% of these responses arrived within 30 minutes.

The increasing volume of short texts generated on social media sites, such as Twitter or Facebook, creates a great demand for effective and efficient topic modeling approaches. While latent Dirichlet allocation (LDA) can be applied, it is not optimal due to its weakness in handling short texts with fast-changing topics and scalability concerns. In this paper, we propose a transfer learning approach that utilizes abundant labeled documents from other domains (such as Yahoo! News or Wikipedia) to improve topic modeling, with better model fitting and result interpretation. Specifically, we develop Transfer Hierarchical LDA (thLDA) model, which incorporates the label information from other domains via informative priors. In addition, we develop a parallel implementation of our model for large-scale applications. We demonstrate the effectiveness of our thLDA model on both a microblogging dataset and standard text collections including AP and RCV1 datasets.