Tumblr, as one of the most popular microblogging platforms, has gained momentum recently. It is reported to have 166.4 millions of users and 73.4 billions of posts by January 2014. While many articles about Tumblr have been published in major press, there is not much scholar work so far. In this paper, we provide some pioneer analysis on Tumblr from a variety of aspects. We study the social network structure among Tumblr users, analyze its user generated content, and describe reblogging patterns to analyze its user behavior. We aim to provide a comprehensive statistical overview of Tumblr and compare it with other popular social services, including blogosphere, Twitter and Facebook, in answering a couple of key questions: What is Tumblr? How is Tumblr different from other social media networks? In short, we find Tumblr has more rich content than other microblogging platforms, and it contains hybrid characteristics of social networking, traditional blogosphere, and social media. This work serves as an early snapshot of Tumblr that later work can leverage.
There are rich literatures on both existing and emerging online social network services. Statistical patterns across different types of social networks are reported, including traditional blogosphere @cite_17 , user-generated content platforms like Flickr, Youtube and LiveJournal @cite_13 , Twitter @cite_1 @cite_18 , instant messenger network @cite_19 , Facebook @cite_7 , and Pinterest @cite_23 @cite_3 . Majority of them observe shared patterns such as long tail distribution for user degrees (power law or power law with exponential cut-off), small ($90 effect in terms of user profiles (age or location), but not with respect to gender. Indeed, people are more likely to talk to the opposite sex @cite_19 . The recent study of Pinterest observed that ladies tend to be more active and engaged than men @cite_3 , and women and men have different interests @cite_16 . We have compared Tumblr's patterns with other social networks in Table and observed that most of those trend hold in Tumblr except for some number difference.
@cite_18 @cite_7 @cite_1 @cite_3 @cite_19 @cite_23 @cite_16 @cite_13 @cite_17
2094858374 2277141067 1557969627 2118110552
This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his her social connections -- friends, friends' friends, 3-degree friends, etc? Is "Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such "informal networks", we investigated the "formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8 , and inference quality of explicit interests by up to 101 . Online social networks (OSNs) have become popular platforms for people to connect and interact with each other. Among those networks, Pinterest has recently become noteworthy for its growth and promotion of visual over textual content. The purpose of this study is to analyze this image-based network in a gender-sensitive fashion, in order to un- derstand (i) user motivation and usage pattern in the network, (ii) how communications and social interactions happen and (iii) how users describe themselves to others. This work is based on more than 220 million items generated by 683,273 users. We were able to find significant differences w.r.t. all mentioned aspects. We observed that, although the network does not encourage direct social communication, females make more use of lightweight interactions than males. Moreover, females invest more effort in reciprocating social links, are more active and generalist in content generation, and describe themselves using words of affection and positive emotions. Males, on the other hand, are more likely to be specialists and tend to describe themselves in an assertive way. We also observed that each gender has different interests in the network, females tend to make more use of the network's commercial capabilities, while males are more prone to the role of curators of items that reflect their personal taste. It is important to understand gender differences in online social networks, so one can design services and applications that leverage human social interactions and provide more targeted and relevant user experiences. Billions of user-shared images are generated by individuals in many social networks today, and this particular form of user data is widely accessible to others due to the nature of online social sharing. When user social graphs are only accessible to exclusive parties, these user-shared images are proved to be an easier and effective alternative to discover user connections. This work investigated over 360 000 user shared images from two social networks, Skyrock and 163 Weibo, in which 3 million follower followee relationships are involved. It is observed that the shared images from users with a follower followee relationship show relatively higher similarities . A multimedia big data system that utilizes this observed phenomenon is proposed as an alternative to user- generated tags and social graphs for follower followee recommendation and gender identification. To the best of our knowledge, this is the first attempt in this field to prove and formulate such a phenomenon for mass user-shared images along with more practical prediction methods. These findings are useful for information or services recommendations in any social network with intensive image sharing, as well as for other interesting personalization applications, particularly when there is no access to those exclusive user social graphs. The identification of popular and important topics discussed in social networks is crucial for a better understanding of societ al concerns. It is also useful for users to stay on top of trends without having to sift through vast amounts of shared information. Trend detection methods introduced so far have not used the network topology and has thus not been able to distinguish viral topics from topics that are diffused mostly through the news media. To address this gap, we propose two novel structural trend definitions we call coordinated and uncoordinated trends that use friendship information to identify topics that are discussed among clustered and distributed users respectively. Our analyses and experiments show that structural trends are significantly different from traditional trends and provide new insights into the way people share information online. We also propose a sampling technique for structural trend detection and prove that the solution yields in a gain in efficiency and is within an acceptable error bound. Experiments performed on a Twitter data set of 41.7 million nodes and 417 million posts show that even with a sampling rate of 0.005, the average precision is 0.93 for coordinated trends and 1 for uncoordinated trends.