Abstract
We present a study analyzing the response times of users to questions on Twitter. We investigate estimating these response times using an exponential distribution-based wait time model learned from users’ previous responses. Our analysis considers several different model building approaches, including personalized models for each user, general models built for all users, and time-sensitive models specific to a day of the week or hour of the day. Our evaluation using a real world question-answer dataset shows the effectiveness of our approach.
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