A methodology for workload characterization of E-commerce sites
Proceedings of the 1st ACM conference on Electronic commerce
Characterizing Web user sessions
ACM SIGMETRICS Performance Evaluation Review
A Simple Decomposition Method for Support Vector Machines
Machine Learning
Hierarchical Workload Characterization for a Busy Web Server
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Proceedings of the first workshop on Online social networks
Proceedings of the 18th international conference on World wide web
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Eddi: interactive topic-based browsing of social status streams
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
On word-of-mouth based discovery of the web
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
The socialbot network: when bots socialize for fame and money
Proceedings of the 27th Annual Computer Security Applications Conference
Who gives a tweet?: evaluating microblog content value
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Understanding and combating link farming in the twitter social network
Proceedings of the 21st international conference on World Wide Web
Factors influencing the response rate in social question and answering behavior
Proceedings of the 2013 conference on Computer supported cooperative work
Who will retweet me?: finding retweeters in twitter
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 0.00 |
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%.