The past year has seen a growing public awareness of the privacy risks of social networking through personal information that people voluntarily disclose. A spotlight has accordingly been turned on the disclosure policies of social networking sites and on mechanisms for restricting access to personal information on Facebook and other sites. But this is not sufficient to address privacy concerns in a world where Web-based data mining tools can let anyone infer information about others by combining data from multiple sources. To illustrate this, we are building a demonstration data miner, GlobalInferencer, that makes inferences about an individual?s lifestyle and other behavior. GlobalInferencer uses linked data technology to perform unified searches across Facebook, Flickr, and public data sites. It demonstrates that controlling access to personal information on individual social networking sites is not an adequate framework for protecting privacy, or even for supporting valid inferencing. In addition to access restrictions, there must be mechanisms for maintaining the provenance of information combined from multiple sources, for revealing the context within which information is presented, and for respecting the accountability that determines how information should be used.

The increased popularity of feature-rich mobile devices in recent years has enabled widespread consumption and production of social media content via mobile devices. Because mobile devices and mobile applications change context within which an individual generates and consumes microblog content, we might expect microblogging behavior to differ depending on whether the user is using a mobile device. To our knowledge, little has been established about what, if any, effects such mobile interfaces have on microblogging. In this paper, we investigate this question within the context of Twitter, among the most popular microblogging platforms. This work makes three specific contributions. First, we quantify the ways in which user profiles are effected by the mobile context: (1) the extent to which users tend to be either fully non-mobile or mobile and (2) the relative activity of the mobile Twitter community. Second, we assess the differences in content between mobile and non-mobile tweets (posts to the Twitter platform). Our results show that mobile platforms produce very different patterns of Twitter usage. As part of our analysis, we propose and apply a classification system for tweets. We consider this to be the third contribution of this work. While other classification systems have been proposed, ours is the first to permit the independent encoding of a tweet’s form, content, and intended audience. In this paper we apply this system to show how tweets differ between mobile and non-mobile contexts. However, because of its flexibility and breadth, the schema may be useful to researchers studying Twitter content in other contexts as well.

In social production communities, users' individual and collective efforts lead to the creation of valuable resources — cf. Wikipedia, Open Street Map, and Reddit. Contributors to such communities often specialize in the tasks they choose to do. We found evidence for specialization by work type in Cyclopath, a geographic wiki for bicyclists -- most users edit a single type of map feature, such as points of interest or roads and trails. We also saw a user lifecycle effect: as users gain experience, they specialize in editing roads and trails. Our findings suggest more effective ways to organize social production interfaces, compose units of work, and match them to users who want to help.

In this paper we examine the network structure of the MythTV mailing list, an online technology Q&A user community, and we use time-series analysis techniques to study users’ reciprocity behavior in this community. We find that the amount of help users provide is strongly correlated to the amount of help they receive. Further, by conducting the Granger Causality test on the time series data of active users’ activity, we find that the amount of help given is actually the reason why one gets a lot of help. This finding corresponds to the concept of directed reciprocity in social networks and provides insights into social dynamics in technology-centered online communities.

Information about community structure can be useful in a variety of mobile web applications. For instance, it has been shown that community-based methods can be more effective than alternatives for routing messages in delay-tolerant networks. In this paper we present initial research that shows that information on hierarchical structures in communities can further improve the effectiveness of message routing. This is interesting because despite much previous work on the topic, there have been few concrete applications which exploit hierarchical community structure.

This paper introduces a new type of summarization task, known as microblog summarization, which aims to synthesize content from multiple microblog posts on the same topic into a human-readable prose description of fixed length. Our approach leverages (1) a generative model which induces event structures from text and (2) a user behavior model which captures how users convey relevant content.

Although sentiment analysis has attracted a lot of research, little work has been done on social media data compared to product and movie reviews. This is due to the low accuracy that results from the more informal writing seen in social media data. Currently, most of sentiment analysis tools on social media choose the lexicon-based approach instead of the machine learning approach because the latter requires the huge challenge of obtaining enough human-labeled training data for extremely large-scale and diverse social opinion data. The lexicon-based approach requires a sentiment dictionary to determine opinion polarity. This dictionary can also provide useful features for any supervised learning method of the machine learning approach. However, many benchmark sentiment dictionaries do not cover the many informal and spoken words used in social media. In addition, they are not able to update frequently to include newly generated words online. In this paper, we present an automatic sentiment dictionary generation method, called Constrained Symmetric Nonnegative Matrix Factorization (CSNMF) algorithm, to assign polarity scores to each word in the dictionary, on a large social media corpus — digg.com. Moreover, we will demonstrate our study of Amazon Mechanical Turk (AMT) on social media word polarity, using both the human-labeled dictionaries from AMT and the General Inquirer Lexicon to compare our generated dictionary with. In our experiment, we show that combining links from both WordNet and the corpus to generate sentiment dictionaries does outperform using only one of them, and the words with higher sentiment scores yield better precision. Finally, we conducted a lexicon-based sentiment analysis on human-labeled social comments using our generated sentiment dictionary to show the effectiveness of our method.

How does sentiment flow through hyperlink networks? Earlier work on hyperlink networks has focused on the structure of the network, often modeling posts as nodes in a directed graph in which edges represent hyperlinks. At the same time, sentiment analysis has largely focused on classifying texts in isolation. Here we analyze a large hyperlinked network of mass media and weblog posts to determine how sentiment features of a post affect the sentiment of connected posts and the structure of the network itself. We explore the phenomena of sentiment flow through experiments on a graph containing nearly 8 million nodes and 15 million edges. Our analysis indicates that (1) nodes are strongly influenced by their immediate neighbors, (2) deep cascades lead complex but predictable lives, (3) shallow cascades tend to be objective, and (4) sentiment becomes more polarized as depth increases.

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.

In this paper we propose a research agenda on the use of information markets as tools to collect, aggregate and analyze citizens’ opinions, expectations and preferences from social media in order to support public policy design and implementation. We argue that markets are institutional settings able to efficiently allocate scarce resources, aggregate and disseminate information into prices and accommodate hedging against various types of risks. We discuss various types of information markets, as well as address the participation of both human and computational agents in such markets.