In this research, social media data from a public school district was utilized to help improve district communication during and after a crisis. Data was analyzed by utilizing the programming languages R and Python. The programming languages helped generate data that allowed this research to evaluate how the quality of crisis communication could be improved by using these methods. In the study, the data identified emotions in the text of comments, identifying information gaps (finding questions), and manually identified misinformation to show school districts various methods to help generate messages that can help answer the questions of parents and community members during and after a crisis. The findings from the sentiment analysis (research question one) showed that the school district's parents and community trusted the school district during their crises. Research question two (identifying information gaps) revealed seven out of the top ten crises showed that more than 20% of their comments contained questions. For research question three (identifying misinformation), it was found that out of the four categories (off topic, contradiction, rumor, and transparency) for identifying misinformation, rumors were the top category of misinformation that was found in the comments of the top ten crises. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]