@@ -41,25 +45,11 @@ $$ \text{freq}_{u} = \frac{|\text{COVID-posts by } u|}{|\text{All posts by } u|} $$ \text{pop_ratio}_{u} = \left(\frac{\sum\text{Popularity of COVID-posts by } u}{|\text{COVID-posts by } u|}\right) / \left(\frac{\sum \text{Popularity of all posts by } u}{|\text{All posts by } u|}\right) $$-In the second section, we used the following formulas to calculate frequencies and popularity ratios for each date across many users in one sample: +The frequency equation can divide by zero if the user has zero posts, and it is logical to assign the frequency to 0 when the user didn't post anything. However, it is not sensible to assign the popularity ratio to zero when the pop_ratio equation divides by zero. There are three divisions in the pop_ratio equation, so there are three possible places where it might divide by zero. To prevent division by zero, people who didn't post about COVID-19, who didn't post anything at all, and who have literally 0 popularity on any of their posts are ignored. In our data, this amount of people are ignored for each sample: -**TODO** +@include `/pop/ignored.md` -# Meta Analysis - -This section ignores date and focuses on user differences within our samples, which will answer the first part of our research question: **how frequently does people post about COVID-related issues, and how interested are people to see COVID-related posts?** - -## Method & Results - COVID-19 Posting Frequency - -**TODO: Separate method from results** - -First, we analyzed how frequently the users in these three datasets are posing about COVID-19 (ignoring retweets). Initially, we were expecting that most people will post about COVID-19 because this pandemic is very relevant to every one of us. However, we found that there are many people in our samples didn't post about COVID-19 at all. The following table shows how many people in each sample didn't post or posted less than 1% about COVID-19: - -@include `/freq/didnt-post.md` - -The `eng-news` sample has the lowest number of users who didn't have COVID-related posts, the `500-rand` sample has the highest, while `500-pop` sits in between. This large difference between `eng-news` and the rest can be explained by the news channels' obligation to report news, which includes news about new outbreaks, progress of vaccination, new cross-border policies, etc. Also, we observed that `500-pop` has much more users who posted COVID-related content than `500-rand`, while they have similar amounts of users posting less than 1%. This finding might be explained by how influential people have more incentive to express their support toward slowing the spread of the pandemic than regular users, which doesn't require frequent posting like news channels. - -We calculated frequency by dividing the total number of tweets by the number of COVID-related tweets. We might graph the frequencies on a histogram to gain more insight: (You can click on the images to enlarge them, and hold down E to view full screen). +Then, the users' results are graphed in one histogram for each sample to gain some insights about the distribution of user frequencies. However, there are many outliers and more than half who posted below 0.1% for two of our samples, making the graphs unreadable: (You can click on the images to enlarge them, and hold down E to view full screen)
-$$ pop_{user} = \left(\frac{\sum\text{Popularity of COVID-posts}}{|\text{COVID-posts}|}\right) / \left(\frac{\sum \text{Popularity of all posts}}{|\text{All posts}|}\right)$$ -- -There are three divisions in this equation, so there are three possible places where it might divide by zero. So, to prevent division by zero, we ignored people who didn't post about COVID-19 or didn't post anything at all, and we also ignored people who have literally 0 popularity on any of their posts. In our data, we ignored this amount of people for each sample: - -@include `/pop/ignored.md` - -Graphing the results, we find that the ***TODO*** - -
-$$ \text{freqs}_i = \frac{|\text{COVID-posts on date}_{i}|}{|\text{All posts on date}_{i}|} $$ +$$ \text{freq}_i = \frac{|\text{COVID-posts on date}_{i}|}{|\text{All posts on date}_{i}|} $$
-$$ \text{pops}_i = \frac{ \sum_{u \in \text{Users}} \left(\frac{\sum\text{Popularity of u's COVID-posts on date}_i}{(\text{Average popularity of all u's posts}) \cdot |\text{u's COVID-posts on date}_i|}\right)}{(\text{Number of users posted on date}_i)} $$ +$$ \text{pop_ratio}_i = \frac{ \sum_{u \in \text{Users}} \left(\frac{\sum\text{Popularity of u's COVID-posts on date}_i}{|\text{u's COVID-posts on date}_i| \cdot (\text{Average popularity of all u's posts})}\right)}{(\text{Number of users posted on date}_i)} $$-After calculation, we decided to plot line charts of `freqs` or `pops` against `dates`. Initially, we are seeing graphs with very high peaks such as the graph below. After some investigation, we found that these peaks are caused by not having enough tweets on each day to average out the random error of one single popular tweet. For example, in the graph below, we adjusted the program to print different users' popularity ratios when we found an average popularity ratio of greater than 20, which produced the output on the right. As it turns out, on 2020-07-11, the user @juniorbachchan posted that he and his father tested positive, and that single post is 163.84 times more popular than the average of all his posts. (The post is linked [here](https://twitter.com/juniorbachchan/status/1282018653215395840), it has 235k likes, 25k comments, and 32k retweets). Even though these data points are outliers, there isn't an effective way of removing them since we don't have enough tweets data from each user to calculate their range (for example, someone's COVID-related post might be the only one they've posted). So, we've decided to limit the viewing window to `y = [0, 2]` as shown in the graph on the right. +After calculation, `freqs` and `pops` are plotted in line graphs against `dates`. Initially, we are seeing graphs with very high peaks such as the graph below. After some investigation, we found that these peaks are caused by not having enough tweets on each day to average out the random error of one single popular tweet. For example, in the graph below, we adjusted the program to print different users' popularity ratios when we found an average popularity ratio of greater than 20, which produced the output on the right. As it turns out, on 2020-07-11, the user @juniorbachchan posted that he and his father tested positive, and that single post is 163.84 times more popular than the average of all his posts. (The post is linked [here](https://twitter.com/juniorbachchan/status/1282018653215395840), it has 235k likes, 25k comments, and 32k retweets). Even though these data points are outliers, there isn't an effective way of removing them since we don't have enough tweets data from each user to calculate their range (for example, someone's COVID-related post might be the only one they've posted). So, we've decided to limit the viewing window to `y = [0, 2]` as shown in the graph on the right.
