Mapping auroral activity with Twitter

Twitter is a popular, publicly-accessible, social media service that has proven useful in mapping large-scale events in real-time. In this study, for the first time, the use of Twitter as a measure of auroral activity is investigated. Peaks in the number of aurora-related tweets are found to frequently coincide with geomagnetic disturbances (detection rate of 91%). Additionally, the number of daily aurora-related tweets is found to strongly correlate with several auroral strength proxies (ravg ≈ 0.7). An examination is made of the bias for location and time of day within Twitter data, and a first order correction of these effects is presented. Overall, the results suggest that Twitter can provide both specific details about an individual aurora and accurate real-time indication of when, and even from where, an aurora is visible

Outage Detection via Real-time Social Stream Analysis: Leveraging the Power of Online Complaints

Over the past couple of years, Netflix has significantly expanded its online streaming offerings, which now encompass multiple delivery platforms and thousands of titles available for instant view. This paper documents the design and development of an outage detection system for the online services provided by Netflix. Unlike other internal quality control measures used at Netflix, this system uses only publicly available information: the tweets, or Twitter posts, that mention the word “Netflix,” and has been developed and deployed externally, on servers independent of the Netflix infrastructure. This paper discussed the system and provides assessment of the accuracy of its real-time detection and alert mechanisms