We study the relationship between content and temporal dynamics of information on Twitter, focusing on the persistence of information. We compare two extreme temporal patterns in the decay rate of URLs embedded in tweets, defining a prediction task to distinguish between URLs that fade rapidly following their peak of popularity and those that fade more slowly. Our experiments show a strong association between the content and the temporal dynamics of information: given unigram features extracted from corresponding HTML webpages, a linear SVM classifier can predict the temporal pattern of URLs with high accuracy. We further explore the content of URLs in the two temporal classes using various textual analysis techniques (via LIWC and trend detection). We find that the rapidly-fading information contains significantly more words related to negative emotion, actions, and more complicated cognitive processes, whereas the persistent information contains more words related to positive emotion, leisure, and lifestyle.

Abstract—A common question in social networking research is how edges form to produce social graphs with the common characteristics, including a power-law degree distribution and a small diameter. One common model for edge formation in synthetic networks is preferential attachment. We examine the edge formation process of one Online Social Network (OSN), Buzznet, and look for evidence for preferential attachment. To our surprise, we find that a form of “anti-preferential attachment” is common, in which high-degree nodes add edges to lowdegree nodes, perhaps as a means of self-promotion. We also find that nodes are most likely to reciprocate edges from low-degree nodes, limiting the extent to which anti-preferential attachment can succeed in boosting a high-degree node’s in-degree. I.

User-contributed content is creating a surge on the Internet. A list of “buzzing topics ” can effectively monitor the surge and lead people to their topics of interest. Yet a topic phrase alone, such as “SXSW”, can rarely present the information clearly. In this paper, we propose to explore a variety of text sources for summarizing the Twitter topics, including the tweets, normalized tweets via a dedicated tweet normalization system, web contents linked from the tweets, as well as integration of different text sources. We employ the concept-based optimization framework for topic summarization, and conduct both automatic and human evaluation regarding the summary quality. Performance differences are observed for different input sources and types of topics. We also provide a comprehensive analysis regarding the task challenges. 1

The way in which social conventions emerge in communities has been of interest to social scientists for decades. Here we report on the emergence of a particular social convention on Twitter—the way to indicate a tweet is being reposted and to attribute the content to its source. Initially, different variations were invented and spread through the Twitter network. The inventors and early adopters were well-connected, active, core members of the Twitter community. The diffusion networks of these conventions were dense and highly clustered, so no single user was critical to the adoption of the conventions. Despite being invented at different times and having different adoption rates, only two variations came to be widely adopted. In this paper we describe this process in detail, highlighting insights and raising questions about how social conventions emerge.

Abstract. Existingmodels ofinformation diffusion assume thatpeer influence is the main reason for the observed propagation patterns. In this paper, we examine the role of authority pressure on the observed information cascades. We model this intuition by characterizing some nodes in the network as “authority ” nodes. These are nodes that can influence large number of peers, while themselves cannot be influenced by peers. We propose a model that associates with every item two parameters that quantify the impact of the peer and the authority pressure on the item’s propagation. Given a network and the observed diffusion patterns of the item, we learn these parameters from the data and characterize the item as peer- or authority-propagated. We also develop a randomization test that evaluates the statistical significance of our findings and makes our item characterization robust to noise. Our experiments with real datafrom onlinemediaandscientific-collaboration networksindicate that there is a strong signal of authority pressure in these networks. 1

Email usage is pervasive among people from different backgrounds, and can be an important and accurate data source to study intricate social structures. Social status and role analysis on a personal email network can help reveal hidden information. The availability of Sarah Palin’s email corpus presents a great opportunity to study the social statuses and social roles in an email network. However, the email corpus does not readily lend itself to social network analysis due to problems such as noisy email data, scale in size, and temporal constraints. In this paper, we contribute an initial investigation of social status and role analysis on Sarah Palin’s email corpus. In particular, we reconstruct a multiplex network from the unstructured email corpus, and then analyze the social statuses and roles from three different perspectives: individual, group, and temporal. Experimental result demonstrates that our proposed analytic tool provides an effective way to analyze social status and roles on email networks. To the best of our knowledge, this work is the first quantitative study of Sarah Palin’s email corpus recently released by the state of Alaska.

The focus of this paper is on demonstrating how a model of the diffusion of actionable information can be used to study information cascades on Twitter that are in response to an actual crisis event, and its concomitant alerts and warning messages from emergency managers. We will: identify the types of information requested or shared during a crisis situation; show how messages spread among the users on Twitter including what kinds of information cascades or patterns are observed; and note what these patterns tell us about information flow and the users. We conclude by noting that emergency managers can use this information to either facilitate the spreading of accurate information or impede the flow of inaccurate or improper messages.

The structure of a social network contains information useful for predicting its evolution. Nodes that are “close ” in some sense are more likely to become linked in the future than more distant nodes. We show that structural information can also help predict node activity. We use proximity to capture the degree to which two nodes are “close ” to each other in the network. In addition to standard proximity metrics used in the link prediction task, such as neighborhood overlap, we introduce new metrics that model different types of interactions that can occur between network nodes. We argue that the “closer ” nodes are in a social network, the more similar will be their activity. We study this claim using data about URL recommendation on social media sites Digg and Twitter. We show that structural proximity of two users in the follower graph is related to similarity of their activity, i.e., how many URLs they both recommend. We also show that given friends ’ activity, knowing their proximity to the user can help better predict which URLs the user will recommend. We compare the performance of different proximity metrics on the activity prediction task and find that some metrics lead to substantial performance improvements.

Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends ’ information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.

There has been a tremendous rise in the growth of online social networks all over the world in recent times. While some networks like Twitter and Facebook have been well documented, the popular Chinese microblogging social network Sina Weibo has not been studied. In this work, we examine the key topics that trend on Sina Weibo and contrast them with our observations on Twitter. We find that there is a vast difference in the content shared in China, when compared to a global social network such as Twitter. In China, the trends are created almost entirely due to retweets of media content such as jokes, images and videos, whereas on Twitter, the trends tend to have more to do with current global events and news stories. Categories and Subject Descriptors