Stay #woke with Stats

After highlighting the “user_lang” column, I found there to be 27 different languages in my data set. The languages included ar, ca, cs, cy, da, de, el, en, en-GB, es, es-MX, fi, fr, hr, hu, id, it, ja, lt, nl, no, pl, pt, pt-GB, ru, sv, and tr. The number of total tweets in English is 10764. This comes out to about 93% of my total number of tweets.

After analyzing my bar graph, I get the sense that #woke is used somewhat at the same rate each day during this stretch of 15 days. The two spikes in the graph are found between 2/6/17-2/8/17 and then 2/13/17. After looking through the news for the first five days of my study, I really found no incredibly influential stories that would have affected my data. Small town mugshot photos and sports news that I found could have caused tweets with my hashtag because it can be applied to even the slightest of news, but nothing too influential. On 2/7, however, where my graph shows a small spike, was the day Carter G Woodson initiated the first Black History Week that eventually turned into Black History Month. I think it is a significant date that could create more possibilities for #woke to be used. Moving towards 2/13, the largest spike in my graph, I did encounter notable headlines. I found an article that mentions over 600 people were arrested this past week nationwide by the Immigration and Customs Enforcement. The spike, in my opinion, probably has something to do with a specific tweet that got a lot of retweets. Drawing back to my word clouds, I thought about the tweet from @AdoreDelano and the massive number of retweets he received, and this was in fact the day he wrote his tweet. It’s interesting that in both analyzations I have been able to see a massive data point and relate it to the same cause. I think circumstances like this, where one tweet gets a lot of recognition, is what will cause the most spikes in #woke data. I don’t believe there are ever drastic falls in the rate at which this term is used in tweets because I can be used so casually and commonly. It seems to me that if I were to continue analysis over multiple weeks, that there would be a steady amount of tweets written with the hashtag, and every once in a while there would be spikes that would soon fall back to the steady rate. It feel like #woke will continue to be used for a long time regarding awareness to nearly anything. That is the beauty of #woke.

Both the mean and median of my data were found to be somewhat in the middle of the list of statistics from the class, so I am not a standout. I think having a relatively average-amount of tweets in comparison to other social hashtag tweets shows the term is being applied to a lot of social issues, but also missing a lot. I think it says something about how the hashtag is something a Tweeter places on the end of a tweet regardless of the topic. It makes sense to me that I have an average-sized number of tweets for this reason because two people could easily write the same exact tweet, just one might ass #woke at the end and one might not. In terms of accessing information, clicking on #woke would bring you to a large sample of tweets that on a casual day on Twitter would be an extremely sufficient amount.

In terms of count, I had one of the larger amounts, so in that sense my data stands out. However, this probably has something to do with me taking a sample of 15 days of data, whereas others could have used a smaller time frame. Once again, my max and min are somewhat in the middle of the lists of statistics. Like in my analysis of the mean and median, it seems that #woke isn’t used in an extreme number of tweets but also not in a low number. It makes me wonder how many of these tweets actually are about national social topics, and how many are the not-so-influential tweets that I have found frequently. The max statistic says a lot also about the access to data and the way #woke can connect such a large number of people to one tweet.

I think the range of my data between the max and the min is a very important statistic to note, and in that sense my data stands out. It shows the effect that one tweet with #woke can have on sparking a conversation. One day there can be over 1,000 tweets, and another there can be less than half of that. I think through this lab I have had the realization that this hashtag is a hit or miss for what it provides. It is a term that is so loosely used that it can be slapped onto the end of the tweet regardless of the topic, so it creates some difficulty in using it to access conversations of massive social issues. It does agree with my idea that I have held consistent which is that it says a lot about how people express opinion through social networks.