3. Requirements For our project, we need to first gather tweet data before and after data breaches. Using this data, we need to look at how each firm responded to the event, for example some firms may respond to every user or make an announcement about the breach, while others may not have any activity on Twitter related to the breach. We also need to see if the firm’s Twitter account had abnormal behavior after the data breach event and then compare it to their activity before the breach. Furthermore, we need to gather data on the firm’s tweet data, including the firm’s number of tweets, retweets, and likes. This will help us get a better idea of how much the firm used their account to handle other Twitter users, and events related to the breach. Moreover, we also need to gather Twitter data for each data breach searching for tweets published during the event time using a provided keyword list. This includes many tweets, not just the firm’s tweets. The goal is to analyze the topics of user discussion, classify different types of Twitter users, and identify influential users. After collecting the data, we need to analyze the stock market trends of the companies during the data breach event. Based on the firm’s stock during the breach, we need to analyze companies which successfully managed the data breach and those that didn’t. Ultimately, we need to come up with a proposal for how a company should handle a data breach based on our findings. 4. Design 4.1 High Level Design Figure 1 demonstrates the high level design of our project. Figure 1: High Design 4.2 Twitter Collection The process of collecting company and user Twitter data given in Figure 2. Refer to scrape_company_tweets.py and user_tweets.py in the Appendix. Figure 2: Tweet Collection Process
3. Requirements For our project, we need to first gather tweet data before and after data breaches. Using this data, we need to look at how each firm responded to the event, for example some firms may respond to every user or make an announcement about the breach, while others may not have any activity on Twitter related to the breach. We also need to see if the firm’s Twitter account had abnormal behavior after the data breach event and then compare it to their activity before the breach. Furthermore, we need to gather data on the firm’s tweet data, including the firm’s number of tweets, retweets, and likes. This will help us get a better idea of how much the firm used their account to handle other Twitter users, and events related to the breach. Moreover, we also need to gather Twitter data for each data breach searching for tweets published during the event time using a provided keyword list. This includes many tweets, not just the firm’s tweets. The goal is to analyze the topics of user discussion, classify different types of Twitter users, and identify influential users. After collecting the data, we need to analyze the stock market trends of the companies during the data breach event. Based on the firm’s stock during the breach, we need to analyze companies which successfully managed the data breach and those that didn’t. Ultimately, we need to come up with a proposal for how a company should handle a data breach based on our findings. 4. Design 4.1 High Level Design Figure 1 demonstrates the high level design of our project. Figure 1: High Design 4.2 Twitter Collection The process of collecting company and user Twitter data given in Figure 2. Refer to scrape_company_tweets.py and user_tweets.py in the Appendix. Figure 2: Tweet Collection Process