![\"pie](\"http:\/\/commons.trincoll.edu\/amst-data-driven\/files\/2017\/04\/pie-chart-1.png\")
<\/p>\nWhen scrolling through the different languages in my tweets I found a\u00a0very wide range. \u00a0In total, I found that there were 24 languages within my dataset. \u00a0These languages included: Arabic,\u00a0Bulgarian, French-Canadian, Danish,\u00a0German, English, British English, English Australia, Spanish, French,\u00a0Indonesian, Italian, Norwegian, Japanese, Korean, Netherlands, Norwegian, Polish, Portuguese, Russian,\u00a0Swedish, TH, TR, VI, XX-IC, ZH-CN, ZH-TW.<\/p>\n
The total amount of tweets in english 80992 of the 83967 tweets. \u00a0This displays that 96.5% of these tweets are in english.<\/p>\n
<\/p>\n
When viewing this information in a pie chart it becomes clear that English is the dominant language. \u00a0Following english is Spanish, which is only 1 percent of the total language. \u00a0Although I had a wide range of languages, each language does not have many users besides the english language. \u00a0This is probably because of the amount of tweets I decided to incorporate in my dataset. \u00a0By using such a large sum of tweets it became more likely that there would be such a variety of languages. \u00a0Despite this, my data still showed that English was the main language, leading by a large amount. \u00a0This could be due to the fact that most, if not all, of the discussion is being generated in the United States. \u00a0This would also explain that Spanish would have the second largest presence.<\/p>\n
![\"Picture1\"](\"http:\/\/commons.trincoll.edu\/amst-data-driven\/files\/2017\/04\/Picture1-1.png\")
<\/p>\n
Due to the fact that my dataset was so large initially, the dates of my tweets spanned through a long period of time. \u00a0Because of this I decided to take a smaller sample of my tweets, grabbing the top 2500 tweets out of my dataset. \u00a0In this I was able to draw tweets from 9 different days. \u00a0On April 4th there were 152 tweets, on April 5th 453 tweets, April 6 380 tweets, April 7 481 tweets, April 8 225 tweets, April 9 161, April 10 224, April 11 301, and April 12 123. \u00a0This was useful in that I was able to see if there were specific days of the week that were generating more tweets than others. \u00a0By using a smaller dataset I was able to look more into specific days and what was occurring in the news on those days. \u00a0In my bar graph it becomes clear that April 7th generated the most amount of activity. \u00a0Following this was April 5th. \u00a0The least amount of tweets being generated was on April 12th. \u00a0After looking into google news I noted that the Seattle Times posted an article “Planned Parenthood deserves support, not to defund it.” \u00a0Although this is a more general article, its main objection is to stand with planned parenthood and against the defunding of it. \u00a0This type of article supports action in support, which could motivate those who support and are tweeting to do so. \u00a0When analyzing my data throughout the semester this has been a consistent trend. \u00a0Due to the fact that my hashtag is one of support and actively standing for an issue, it is positive support that typically generates the most activity from the users tweeting with my hashtag. \u00a0Also on April 11th it came out that “Republican lawmakers said Monday they want the state to foot the bill for a new family planning program that excludes funding for Planned Parenthood.” \u00a0While April 11th did generate a lot of activity I am surprised that there was not more. \u00a0Other than these articles there were not many that really stood out to me during this time span.<\/p>\n
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When evaluating my data I found my mean to be 277.78, my median per day to be 225, not to have a mode, my total data to be 2500, my max to be 481, my min to be 123 and my range to be 358. \u00a0When looking at my mean, median, and mode in comparison to everyone elses I was surprised at how large of a range there was. \u00a0My mean was on the smaller side and was a lot smaller than many of the other individials in my class. \u00a0Although Danny and I used the same sample size of 2500, he was only able to get tweets from 2 different days within this sample as opposed to my 9. \u00a0It would make sense that I did not have a mode due to the fact that there are only 9 different days and the probability of any of these nine days to generate the exact same amount of tweets is extremely slim. \u00a0I found that when looking at everyone elses mode most did not have one either. \u00a0My median was very similar to my mean. \u00a0After taking statistics I know that when considering my 5 number summary, if I were to graph this information I would find that because the mean and median are pretty similar, I would have a pretty normal distribution. \u00a0While my issue typically generates a lot of tweets, it is clear that there was not much occurring in the news during this 9 day period regarding Planned Parenthood. \u00a0There are many different issues occurring in the news displayed by the different hashtags within my class which could have been gaining more attention during this time period.<\/p>\n
When looking at the max, min, range, and count of my data in comparison to the rest of my class it seems as though my maximum is on the lower side. \u00a0The maximum seems to correlate a lot with the mean, where the individuals who had a high mean also displayed a high maximum. \u00a0Although my max was on the smaller side when compared to others, my minimum was not smaller than others. \u00a0This would explain why my range was generally smaller than most others in my class. \u00a0It seems as though because there was nothing that specifically occurred during these 9 days within my topic, I was generating more of a consistent amount of tweets each day than others in my class. \u00a0I am able to note that there are people with a very high min and high max, causing them to have a large range. \u00a0This could be explained if something major happened regarding their topic on one day, causing many people to start tweeting about it. \u00a0Google news would confirm my hypothesis that nothing really huge happened on one particular day within this 9 day span, causing the amount of tweets to remain consistent.<\/p>\n
Prior to this lab, I was seeing a lot of trends that displayed the type of news that generated activity with my tweet. \u00a0Although in many cases it is acts against specific issues that cause talk, mine tends to generate activity when displaying acts of support. \u00a0In the last lab I saw that there were more people who were following the twitter account for the funding of planned parenthood than there were the general planned parenthood account. \u00a0This shows that many people are positively reinforcing planned parenthood and react better when asked to support planned parenthood as opposed to stand against those who want to get rid of it.<\/p>\n","protected":false},"excerpt":{"rendered":"
When scrolling through the different languages in my tweets I found a\u00a0very wide range. \u00a0In total, I found that there were 24 languages within my dataset. \u00a0These languages included: Arabic,\u00a0Bulgarian, French-Canadian, Danish,\u00a0German, English, British English, English Australia, Spanish, French,\u00a0Indonesian, Italian, Norwegian, Japanese, Korean, Netherlands, Norwegian, Polish, Portuguese, Russian,\u00a0Swedish, TH, TR, VI, XX-IC, ZH-CN, ZH-TW. The…<\/p>\n","protected":false},"author":1969,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/3190"}],"collection":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/users\/1969"}],"replies":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/comments?post=3190"}],"version-history":[{"count":3,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/3190\/revisions"}],"predecessor-version":[{"id":3299,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/3190\/revisions\/3299"}],"wp:attachment":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/media?parent=3190"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/categories?post=3190"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/tags?post=3190"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}