Applying Survival Analysis and Count Models to Twitter Data

Twitter has a variety of information on it, health topic is one of the popular categories. We used a collection of almost 40,000 tweets extracted from Twitter with #blood pressure from January, 2014 to April, 2015 to investigate the potentially associated factors for popularity (measured by the number of retweet) as well as the survival of tweets (measured by the time frame from the first post to its last retweet). We have found the appearance of a few hashtags significantly decreased the survival of tweets. Furthermore, these hashtags increase( but some decrease) the odds of being retweeted. And other factors significantly associated with the odds include actor\u27s friends count, actor\u27s follower\u27s count, actor\u27s listed count and so on. We explored our results using R, the results do not highlight the potential of hashtag in the application of twitter