For this exercise, we will focus even more on the problem and less on the amount and nature of data. On top of that, we will do cross-data and cross-platform analysis. Imagine you are working as an aide or advisor for a candidate for an upcoming election. You are preparing the candidate for an open debate or a town-hall meeting and want to make sure he/she is aware of public opinions on some of the current issues. Pick two issues out of the following list to investigate: gun control; abortion; war; immigration; inequality. For each of these topics, gather data from Twitter and YouTube using Python. How much data? Well, you decide! Perhaps 100 comments, perhaps 500 tweets, or a combination of these. What you are aiming to do is to provide a summary of what people are talking about, how much they are talking about it, and, if possible, who these people are. For instance, you could find that people with the most subjective things to say are talking a lot or they have many friends or followers. This could be interesting or important, since these people are likely spreading their understanding and opinions of the topic much more widely than regular users. So, it would serve your campaign well to know if these potentially influential people are for or against that issue. Create a report with the description of (1) your data collection method, (2) your data analysis method, and (3) your findings.