Community Question Answering (CQA) services thrive as a result of a small number of highly active users, typically called experts, who provide a large number of high quality useful answers. Understanding the temporal dynamics and interactions between experts can present key insights into how community members evolve over time. In this paper, we present a temporal study of experts in CQA and analyze the changes in their behavioral patterns over time. Further, using unsupervised machine learning methods, we show the interesting evolution patterns that can help us distinguish experts from one another. Using supervised classification methods, we show that the models based on evolutionary data of users can be more effective at expert identification than the models that ignore evolution. We run our experiments on two large online CQA to show the generality of our proposed approach.

Political discourse in the United States is getting increasingly polarized. This polarization frequently causes different communities to react very differently to the same news events. Political blogs as a form of social media provide an unique insight into this phenomenon. We present a multitarget, semisupervised latent variable model, MCR-LDA to model this process by analyzing political blogs posts and their comment sections from different political communities jointly to predict the degree of polarization that news topics cause. Inspecting the model after inference reveals topics and the degree to which it triggers polarization. In this approach, community responses to news topics are observed using sentiment polarity and comment volume which serves as a proxy for the level of interest in the topic. In this context, we also present computational methods to assign sentiment polarity to the comments which serve as targets for latent variable models that predict the polarity based on the topics in the blog content. Our results show that the joint modeling of communities with different political beliefs using MCR-LDA does not sacrifice accuracy in sentiment polarity prediction when compared to approaches that are tailored to specific communities and additionally provides a view of the polarization in responses from the different communities.

In this paper, we extend existing work on latent attribute inference by leveraging the principle of homophily: we evaluate the inference accuracy gained by augmenting the user features with features derived from the Twitter profiles and postings of her friends. We consider three attributes which have varying degrees of assortativity: gender, age, and political affiliation. Our approach yields a significant and robust increase in accuracy for both age and political affiliation, indicating that our approach boosts performance for attributes with moderate to high assortativity. Furthermore, different neighborhood subsets yielded optimal performance for different attributes, suggesting that different subsamples of the user's neighborhood characterize different aspects of the user herself. Finally, inferences using only the features of a user's neighbors outperformed those based on the user's features alone. This suggests that the neighborhood context alone carries substantial information about the user.

Online discussion fora have become an important cultural and business asset in the context of many services provided by both non-profit organizations and enterprises. In order to keep and eventually increase the value these systems deliver to their users, it is often necessary to moderate or even manage their dynamics. One way to do this efficiently is to focus primarily on the most influential actors in the system. However, identifying such users becomes increasingly hard with systems where there is a continuously growing large user base. We show that analysis and explanation of influence on the cross-community level is a promising way to provide a coarse-grained picture of a potentially very large system and that it may enable its stakeholders to find groups through which the system can be efficiently influenced, or it can help them to identify and avoid activity considered as malicious. In order to achieve that, we present a novel framework for cross-community influence analysis, which is evaluated on 10 years of data from the largest Irish online discussion system Boards.ie.

Events play a prominent role in our lives, such that many social media documents describe or are related to some event. Organizing social media documents with respect to events thus seems a promising approach to better manage and organize the ever-increasing amount of user-generated content in social media applications. It would support the navigation of data by events or allow one to get notified about new postings related to the events one is interested in, just to name two applications. A challenge is to automatize this process so that incoming documents can be assigned to their corresponding event without any user intervention. We present a system that is able to classify a stream of social media data into a growing and evolving set of events. In order to scale up to the data sizes and data rates in social media applications, the use of a candidate retrieval or blocking step is crucial to reduce the number of events that are considered as potential candidates to which the incoming data point could belong to.In this paper we present and experimentally compare different blocking strategies along their cost vs. effectiveness tradeoff.We show that using a blocking strategy that selects the 60 closest events with respect to upload time, we reach F-Measures of about 85.1% while being able to process the incoming documents within 32ms on average. We thus provide a principled approach supporting to scale up classification of social media documents into events and to process the incoming stream of documents in real time.

Unlike traditional firms, open collaborative systems rely on volunteers to operate, and many communities struggle to maintain enough contributors to ensure the quality and quantity of content. However, Wikipedia has historically faced the exact opposite problem: too much participation, particularly from users who, knowingly or not, do not share the same norms as veteran Wikipedians. During its period of exponential growth, the Wikipedian community developed specialized socio-technical defense mechanisms to protect itself from the negatives of massive participation: spam, vandalism, falsehoods, and other damage. Yet recently, Wikipedia has faced a number of high-profile issues with recruiting and retaining new contributors. In this paper, we first illustrate and describe the various defense mechanisms at work in Wikipedia, which we hypothesize are inhibiting newcomer retention. Next, we present results from an experiment aimed at increasing both the quantity and quality of editors by altering various elements of these defense mechanisms, specifically pre-scripted warnings and notifications that are sent to new editors upon reverting or rejecting contributions. Using logistic regressions to model new user activity, we show which tactics work best for different populations of users based on their motivations when joining Wikipedia. In particular, we found that personalized messages in which Wikipedians identified themselves in active voice and took direct responsibility for rejecting an editor’s contributions were much more successful across a variety of outcome metrics than the current messages, which typically use an institutional and passive voice.

In this paper we cluster and analyse temporal user behaviour in online communities. We adapt a simple unsupervised clustering algorithm to an evolutionary setting where we cluster users into prototypical behavioural roles based on features derived from their ego-centric reply-graphs. We then analyse changes in the role membership of the users over time, the change in role composition of forums over time and examine the differences between forums in terms of role composition. We perform this analysis on 200 forums from a popular national bulletin board and 14 enterprise technical support forums.

We use data extracted from many weblogs to identify the underlying relations of a set of companies in the Standard and Poor (S\&P) 500 index. We define a pairwise similarity measure for the companies based on the weblog articles and then apply a graph clustering procedure. We show that it is possible to capture some interesting relations between companies using this method. As an application of this clustering procedure we propose a cluster-based portfolio selection method which combines information from the weblog data and historical stock prices. Through simulation experiments, we show that our method performs better (in terms of risk measures) than cluster-based portfolio strategies based on company sectors or historical stock prices. This suggests that the methodology has the potential to identify groups of companies whose stock prices are more likely to be correlated in the future.

Online community managers work towards building and managing communities around a given brand or topic. A risk imposed on such managers is that their community may die out and its utility diminish to users. Understanding what drives attention to content and the dynamics of discussions in a given community informs the community manager and/or host with the factors that are associated with attention. In this paper we gain insights into the idiosyncrasies that individual community forums exhibit in their attention patterns and how the factors that impact activity differ. We glean such insights by using logistic regression models for identifying seed posts and explore the effectiveness of a range of features. Our findings show that the discussion behaviour of different communities is clearly impacted by different factors.

As the Web has become integrated into daily life, understanding how individuals spend their time online impacts domains ranging from public policy to marketing. It is difficult, however, to measure even simple aspects of browsing behavior via conventional methods---including surveys and site-level analytics---due to limitations of scale and scope. In part addressing these limitations, large-scale Web panel data are a relatively novel means for investigating patterns of Internet usage. In one of the largest studies of browsing behavior to date, we pair Web histories for 250,000 anonymized individuals with user-level demographics---including age, sex, race, education, and income---to investigate three topics. First, we examine how behavior changes as individuals spend more time online, showing that the heaviest users devote nearly twice as much of their time to social media relative to typical individuals. Second, we revisit the digital divide, finding that the frequency with which individuals turn to the Web for research, news, and healthcare is strongly related to educational background, but not as closely tied to gender and ethnicity. Finally, we demonstrate that browsing histories are a strong signal for inferring user attributes, including ethnicity and household income, a result that may be leveraged to improve ad targeting.