Mining anomalies using traffic feature distributions
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Information revelation and privacy in online social networks
Proceedings of the 2005 ACM workshop on Privacy in the electronic society
Diagnosing network disruptions with network-wide analysis
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Proceedings of the first workshop on Online social networks
What's going on?: learning communication rules in edge networks
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Troubleshooting chronic conditions in large IP networks
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Towards automated performance diagnosis in a large IPTV network
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Detailed diagnosis in enterprise networks
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Anomaly extraction in backbone networks using association rules
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Understanding online social network usage from a network perspective
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Network level footprints of facebook applications
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Mining communities in networks: a solution for consistency and its evaluation
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Evolution of an online social aggregation network: an empirical study
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Measuring online service availability using twitter
WOSN'10 Proceedings of the 3rd conference on Online social networks
Large-scale app-based reporting of customer problems in cellular networks: potential and limitations
Proceedings of the first ACM SIGCOMM workshop on Measurements up the stack
Q-score: proactive service quality assessment in a large IPTV system
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Rapid detection of maintenance induced changes in service performance
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
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Social media sites such as Twitter continue to grow at a fast pace. People of all generations use social media to exchange messages and share experiences of their life in a timely fashion. Most of these sites make their data available. An intriguing question is can we exploit this real-time and massive data-flow to improve business in a measurable way. In this paper, we are particularly interested in tweets (Twitter messages) that are relevant to mobile network performance. We compare tweets with a more traditional source of user experience, i.e., customer care tickets, and correlate both of them with a list of major network incidents. From our study, we have the following observations. First, Twitter users and users who call customer service tend to report different types of performance issues. Second, we observe that tweets typically appear more rapidly in response to network problems than customer tickets. They also appear to respond to a wider range of network issues. Third, significant spikes in the number of tweets appear to indicate short term performance impairments which are not reported in our current list of major network incidents. These observations together indicate that Twitter is an attractive, complementary source for monitoring service performance and its impact on user experience.