Understanding the impact of sampling and noise on detecting events using twitter
Yifang Wei, Lisa Singh · 2017
While social media sites can be used to identify events rapidly, many data streams are partial because of rate limiting while others are large, but particularly noisy. This poster investigates the impact of sample size and noise on event detection accuracy. We conduct a sensitivity analysis to understand how robust different methods for event detection on Twitter are, given a noisy, partial data stream. We find that the detection accuracy decreases as the sample fraction decreases and as the SNR decreases; however, the rate of decrease changes at different sample sizes and SNRs for different methods.