Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing. We have crawled the entire Twitter site and obtained 41.7 million user profiles, 1.47 billion social relations, 4, 262 trending topics, and 106 million tweets. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks [28]. In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be similar.

Abstract. Micro-blogs are a challenging new source of information for data mining techniques. Twitter is a micro-blogging service built to discover what is happening at any moment in time, anywhere in the world. Twitter messages are short, and generated constantly, and well suited for knowledge discovery using data stream mining. We briefly discuss the challenges that Twitter data streams pose, focusing on classification problems, and then consider these streams for opinion mining and sentiment analysis. To deal with streaming unbalanced classes, we propose a sliding window Kappa statistic for evaluation in time-changing data streams. Using this statistic we perform a study on Twitter data using learning algorithms for data streams. 1

Today’s microblogging services such as Twitter have long outgrown their initial designs as SMSbased social networks. Instead, a massive and steadily-growing user population of more than 100 million is using Twitter for everything from capturing the mood of the country to detecting earthquakes and Internet service failures. It is unsurprising that the traditional centralized client-server architecture has not scaled with user demands, leading to server overload and significant impairment of availability. In this paper, we argue that the divergence in usage models of microblogging services can be best addressed using complementary mechanisms, one that provides reliable messages between friends, and another that delivers events from popular celebrities and media outlets to their thousands or even millions of followers. We present Cuckoo, a new microblogging system that offloads processing and bandwidth costs away from a small centralized server base while ensuring reliable message delivery. We use a 20day Twitter availability measurement to guide our design, and trace-driven emulation of 30,000 Twitter users to evaluate our Cuckoo prototype. Compared to the centralized approach, Cuckoo achieves 30-50% server bandwidth savings and 50-60 % CPU load reduction, while guaranteeing reliable message delivery. I.

+- corresponding author

Copyright is held by the author/owner(s). CHI’12, May 5–10, 2012, Austin, Texas, USA. ACM 978-1-4503-1016-1/12/05. In this paper we describe the preliminary results and future directions of a research in progress, which aims at assessing the hashtag effectiveness as a resource for sentiment analysis expressed on Twitter. The results so far support our hypothesis that hashtags may facilitate the detection and automatic tracking of online population sentiment about different events.