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.
Lampe @cite_4 did a set of survey studies on Facebook users, and shown that people use Facebook to maintain existing offline connections. Java @cite_1 presented one of the earliest research paper for Twitter, and found that users leverage Twitter to talk their daily activities and to seek or share information. In addition, Schwartz @cite_23 is one of the early studies on Pinterest, and from a statistical point of view that female users repin more but with fewer followers than male users. While Hochman and Raz @cite_0 published an early paper using Instagram data, and indicated differences in local color usage, cultural production rate, for the analysis of location-based visual information flows.
@cite_1 @cite_0 @cite_4 @cite_23
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User interaction in social networks, such as Twitter and Facebook, is increasingly becoming a source of useful information on daily events. The online monitoring of short messages posted in such networks often provides insight on the repercussions of events of several different natures, such as (in the recent past) the earthquake and tsunami in Japan, the royal wedding in Britain and the death of Osama bin Laden. Studying the origins and the propagation of messages regarding such topics helps social scientists in their quest for improving the current understanding of human relationships and interactions. However, the actual location associated to a tweet or to a Facebook message can be rather uncertain. Some tweets are posted with an automatically determined location (from an IP address), or with a user-informed location, both in text form, usually the name of a city. We observe that most Twitter users opt not to publish their location, and many do so in a cryptic way, mentioning non-existing places or providing less specific place names (such as “Brazil”). In this article, we focus on the problem of enriching the location of tweets using alternative data, particularly the social relationships between Twitter users. Our strategy involves recursively expanding the network of locatable users using following-follower relationships. Verification is achieved using cross-validation techniques, in which the location of a fraction of the users with known locations is used to determine the location of the others, thus allowing us to compare the actual location to the inferred one and verify the quality of the estimation. With an estimate of the precision of the method, it can then be applied to locationless tweets. Our intention is to infer the location of as many users as possible, in order to increase the number of tweets that can be used in spatial analyses of social phenomena. The article demonstrates the feasibility of our approach using a dataset comprising tweets that mention keywords related to dengue fever, increasing by 45 the number of locatable tweets. Today, it is the norm for online social (OSN) users to have accounts on multiple services. For example, a recent study showed that 34 of all Twitter users also use Pinterest. This situation leads to interesting questions such as: Are the activities that users perform on each site disjoint? Alternatively, if users perform the same actions on multiple sites, where does the information originate? Given the interlinking between social networks, failure to understand activity across multiple sites may obfuscate the true information dissemination dynamics of the social web. In this study, we take the first steps towards a more complete understanding of user behavior across multiple OSNs. We collect a sample of over 30,000 users that have accounts on both Twitter and Pinterest, crawling their profile information and activity on a daily basis for a period of almost three months. We develop a novel methodology for comparing activity across these two sites. We find that the global patterns of use across the two sites differ significantly, and that users tend to post items to Pinterest before posting them on Twitter. Our findings can inform the understanding of the behavior of users on individual sites, as well as the dynamics of sharing across the social web. Background Surveys are popular methods to measure public perceptions in emergencies but can be costly and time consuming. We suggest and evaluate a complementary “infoveillance” approach using Twitter during the 2009 H1N1 pandemic. Our study aimed to: 1) monitor the use of the terms “H1N1” versus “swine flu” over time; 2) conduct a content analysis of “tweets”; and 3) validate Twitter as a real-time content, sentiment, and public attention trend-tracking tool. Methodology Principal Findings Between May 1 and December 31, 2009, we archived over 2 million Twitter posts containing keywords “swine flu,” “swineflu,” and or “H1N1.” using Infovigil, an infoveillance system. Tweets using “H1N1” increased from 8.8 to 40.5 (R2 = .788; p<.001), indicating a gradual adoption of World Health Organization-recommended terminology. 5,395 tweets were randomly selected from 9 days, 4 weeks apart and coded using a tri-axial coding scheme. To track tweet content and to test the feasibility of automated coding, we created database queries for keywords and correlated these results with manual coding. Content analysis indicated resource-related posts were most commonly shared (52.6 ). 4.5 of cases were identified as misinformation. News websites were the most popular sources (23.2 ), while government and health agencies were linked only 1.5 of the time. 7 10 automated queries correlated with manual coding. Several Twitter activity peaks coincided with major news stories. Our results correlated well with H1N1 incidence data. Conclusions This study illustrates the potential of using social media to conduct “infodemiology” studies for public health. 2009 H1N1-related tweets were primarily used to disseminate information from credible sources, but were also a source of opinions and experiences. Tweets can be used for real-time content analysis and knowledge translation research, allowing health authorities to respond to public concerns. In information networks where users send messages to one another, the issue of information overload naturally arises: which are the most important messages? In this paper we study the problem of understanding the importance of messages in Twitter. We approach this problem in two stages. First, we perform an extensive characterization of a very large Twitter dataset which includes all users, social relations, and messages posted from the beginning of the service up to August 2009. We show evidence that information overload is present: users sometimes have to search through hundreds of messages to find those that are interesting to reply or retweet. We then identify factors that influence user response or retweet probability: previous responses to the same tweeter, the tweeter's sending rate, the age and some basic text elements of the tweet. In our second stage, we show that some of these factors can be used to improve the presentation order of tweets to the user. First, by inspecting user activity over time, we construct a simple on-off model of user behavior that allows us to infer when a user is actively using Twitter. Then, we explore two methods from machine learning for ranking tweets: a Naive Bayes predictor and a Support Vector Machine classifier. We show that it is possible to reorder tweets to increase the fraction of replied or retweeted messages appearing in the first p positions of the list by as much as 50-60 .