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.

Theoretical progress in understanding the dynamics of spreading processes on graphs suggests the existence of an epidemic threshold below which no epidemics form and above which epidemics spread to a significant fraction of the graph. We have observed information cascades on the social media site Digg that spread fast enough for one initial spreader to infect hundreds of people, yet end up affecting only 0.1% of the entire network. We find that two effects, previously studied in isolation, combine cooperatively to drastically limit the final size of cascades on Digg. First, because of the highly clustered structure of the Digg network, most people who are aware of a story have been exposed to it via multiple friends. This structure lowers the epidemic threshold while moderately slowing the overall growth of cascades. In addition, we find that the mechanism for social contagion on Digg points to a fundamental difference between information spread and other contagion processes: despite multiple opportunities for infection within a social group, people are less likely to become spreaders of information with repeated exposure. The consequences of this mechanism become more pronounced for more clustered graphs. Ultimately, this effect severely curtails the size of social epidemics on Digg.

Recommender systems are increasingly used for personalised navigation through large amounts of information, especially in the e-commerce domain for product purchase advice. Whilst much research effort is spent on developing recommenders further, there is little to no effort spent on analysing the impact of them - neither on the supply (company) nor demand (consumer) side. In this paper, we investigate the diversity impact of a movie recommender. We define diversity for different parts of the domain and measure it in different ways. The novelty of our work is the usage of real rating data (from Netflix) and a recommender system for investigating the (hypothetical) effects of various configurations of the system and users' choice models.We consider a number of different scenarios (which differ in the agent's choice model), run very extensive simulations, analyse various measurements regarding experimental validation and diversity, and report on selected findings. The choice models are an essential part of our work, since these can be influenced by the owner of the recommender once deployed.

Viral marketing mechanisms use the existing social network between customers to spread information about products and encourage product adoption. Existing viral marketing models focus on the dynamics of the diffusion process, however they typically: (a) only consider a single product campaign and (b) fail to model the evolution of the social network, as the trust between individuals changes over time, during the course of multiple campaigns. In this work, we propose an adaptive viral marketing model which captures: (1) multiple different product campaigns, (2) the diversity in customer preferences among different product categories, and (3) changing confidence in peers’ recommendations over time. By applying our model to a real-world network extracted from the Digg social news website, we provide insights into the effects of network dynamics on the different products’ adoption. Our experiments show that our proposed model outperforms earlier nonadaptive diffusion models in predicting future product adoptions. We also show how this model can be used to explore new viral marketing strategies that are more successful than classic strategies which ignore the dynamic nature of social networks.

Crowdsourced labor markets represent a powerful new paradigm for accomplishing work. Understanding the motivating factors that lead to high quality work could have significant benefits. However, researchers have so far found that motivating factors such as increased monetary reward generally increase workers’ willingness to accept a task or the speed at which a task is completed, but do not improve the quality of the work. We hypothesize that factors that increase the intrinsic motivation of a task – such as framing a task as helping others – may succeed in improving output quality where extrinsic motivators such as increased pay do not. In this paper we present an experiment testing this hypothesis along with a novel experimental design that enables controlled experimentation with intrinsic and extrinsic motivators in Amazon’s Mechanical Turk, a popular crowdsourcing task market. Results suggest that intrinsic motivation can indeed improve the quality of workers’ output, confirming our hypothesis. Furthermore, we find a synergistic interaction between intrinsic and extrinsic motivators that runs contrary to previous literature suggesting “crowding out” effects. Our results have significant practical and theoretical implications for crowd work.

We tackle the problem of grouping content available in social media applications such as Flickr, Youtube, Panoramino etc. into clusters of documents describing the same event. This task has been referred to as event identification before. We present a new formalization of the event identification task as a record linkage problem and show that this formulation leads to a principled and highly efficient solution to the problem. We present results on two datasets derived from Flickr — last.fm and upcoming — comparing the results in terms of Normalized Mutual Information and F-Measure with respect to several baselines, showing that a record linkage approach outperforms all baselines as well as a state-of-the-art system. We demonstrate that our approach can scale to large amounts of data, reducing the processing time considerably compared to a state-of-the-art approach. The scalability is achieved by applying an appropriate blocking strategy and relying on a Single Linkage clustering algorithm which avoids the exhaustive computation of pairwise similarities.

We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation.  We present promising preliminary results with better than 96% accuracy in the detection of astroturf content in the run-up to the 2010 U.S. midterm elections.

Though political theorists have emphasized the importance of political discussion in non-political spaces, past study of online political discussion has focused on primarily political websites. Using a random sample from Blogger.com, we find that 25% of all political posts are from blogs that post about politics less than 20% of the time, because the vast majority of blogs post about politics some of the time but infrequently. Far from being taboo topics in those non- political blogs, political posts got slightly more comments than non-political posts in those same blogs, and the comments overwhelmingly engage the political topics of the post, mostly agreeing but frequently disagreeing as well. We argue that non-political spaces devoted primarily to personal diaries, hobbies, and other topics represent a substantial place of online political discussion and should be a site for further study.

Information extraction systems have been recently proposed for organizing and exploring content in large online text corpora as information networks . In such networks, the nodes are named entities (e.g., people, organizations) while the edges correspond to statements indicating relations among such entities. To date, such systems extract rather primitive networks, capturing only those relations which are expressed by direct statements. In many applications, it is useful to also extract more subtle relations which are often expressed as meta statements in the text. These can, for instance provide the context for a statement (e.g., “Google acquired YouTube on October 2006”), or repercussion about a statement (e.g., “The US condemned Russia’s invasion of Georgia”). In this work, we report on a system for extracting relations expressed in both direct statements as well as in meta statements. We propose a method based on Conditional Random Fields that explores syntactic features to extract both kinds of statements seamlessly. We follow the Open Information Extraction paradigm, where a classifier is trained to recognize any type of relation instead of specific ones. Finally, our results show substantial improvements over a state-of-the-art information extraction system, both in terms of accuracy and, especially, recall.

We describe the dimensions along which Facebook users tend to express themselves via status updates using the semi-automated text analysis approach, the Meaning Extraction Method (MEM). First, we examined dimensions of self-expression in all status updates from a sample of four million Facebook users from four English-speaking countries (the United States, Canada, the United Kingdom, and Australia) in order to examine how these countries vary in their self-expressions. All four countries showed a basic three-component structure, indicating that the medium is a stronger influence than country characteristics or demographics on how people use Facebook status updates. In each country, people vary in terms of the extent to which they use Informal Speech, share Positive Events, and discuss School in their Facebook status updates. Together, these factors tell us how users differ in their self-expression, and thus illustrate meaningful use cases for the product: Talking about what’s going on tends to be positive, and people vary in terms of the extent to which their status updates are short, slangy emotional expressions and topics regarding school. The specific words that define these factors showed subtle differences across countries: The use of profanity indicates fewer school words (but only in Australia), whereas the UK shows greater use of slang terms (rather than profanity) when speaking informally. The MEM also identified English-language dialects as a meaningful dimension along which the countries varied. In sum, beyond simply indicating topicality of posts, this study provides insight into how status updates are used for self-expression. We discuss several theoretical frameworks that could produce these results, and more broadly discuss the generation of theoretical frameworks from wholly empirical data (such as naturalistic Internet speech) using the MEM.