After organizing my tweets in the alphabetical order of the language of the tweet, I found the following languages used: Arabic, Bulgarian, Czech, Danish, German, English, British, Spanish, French, Hebrew, Croatian, Hungarian, Indonesian, Italian, Japanese, Korean, Dutch, Norwegian, Portuguese, Russian, Slovak, Swedish, Thai, Turkish, Ukranian, and Chinese, totaling 26 languages. After doing the countif calculation, I found that 2174 of the 4019 tweets I used to be in English. This totals to 54.1% of the tweets in my data set.
The languages of choice were English, German, and Spanish, as these were among the languages most frequently used in this range of tweets. It makes sense why such a high percentage of the tweets are in English, as the transgender rights issue is currently the greatest issue in the United States. Spanish also makes sense, as it is one of the most frequently spoken languages in the United States. I am curious as to why German also has such a high percentage – perhaps as German is generally a widely spoken language, many Twitter users speak German. Another possibility is that Twitter simply mis-identified the language, as English and German are relatively similar languages with regards to their letters and spelling.
Between these two days, over 4000 tweets were uploaded, so I decided against expanding my research to beyond these two days. In addition, these were the tweets I used in the previous lab. The results of the above graph do indicate that there was something substantial that happened on March 21 that caused people to use the #transgender hashtag three times as much as on March 22. After doing a bit of research, I found that on March 21, a male-to-female transgender woman won a weightlifting competition in New Zealand, and this competition brought up the point that the Olympics has specific guidelines against letting transgender individuals compete for certain events. This news could be the cause of the explosion in conversation on Twitter about transgender rights. I found this information in an article on CBN News called “Born a Man, Transgender Weightlifter Wins Women’s Competition.” On March 22, I found that there was a transgender woman who was murdered in Baltimore – she was the eighth transgender individual to be murdered in 2017. I find it interesting that there were very few tweets posted on this day in comparison to the day before; perhaps this could be due to the fact that people were tweeting about previous 2017 murders more and the eighth one did not receive as much recognition. Another possibility is that people simply did not know about the murder yet and tweeted more about it in later days.
When comparing my data results to those in the rest of the class, it is interesting to see that my mean and median are both around the middle of the extremities. There are some hashtags that have fewer than 300 tweets a day, whereas others have over 5000. My value of 2009.5 tweets per day is not a shocking number in comparison to the other results. This observation indicates that a great amount of people are using the #transgender hashtag, but it is not the most greatly discussed topic on Twitter. #transgender may not be trending at the moment, and therefore the data displays that it is not as popular as topics such as climate change.
When looking at the count, range, max, and min data, I can clearly see the hashtags that stand out with their extremely high values: #climatechange, #makeAmericagreatagain, and #blacklivesmatter. Even their minimum tweets per day is over 2000, whereas the majority of the other hashtags being studied have values less than a tenth of that. My max value does stand out a bit, with 3061 tweets whereas the majority of the maxes were less than 1000, but my minimum is pretty average in comparison to the rest. These results confirm my above statement that although #transgender is a very commonly discussed issue, it is not on the top of the radar for people’s discussions. I find this observation to be interesting because the highest amount of tweets were found with hashtags that do relate to Trump’s presidency, either in full support or full apprehension for his plans, and #transgender is certainly an issue that pertains to what laws Trump may want to enact.
When looking at my data together with the rest of the class data, I would say that my hypotheses have not changed. #transgender is certainly a hotly discussed topic, and given that I only put two days of results in my analysis, it is not a fully accurate portrayal of the great number of tweets that use #transgender. The analysis that all of my classmates did ranged between using all their tweets and using 2000 tweets, and this variance changes the class results drastically. Consistency would help make comparisons more easily.
Bibliography:
CBN News. (2017, March 21). Born a Man, Transgender Weightlifter Wins Women’s Competition. Retrieved April 12, 2017, from http://www1.cbn.com/cbnnews/us/2017/march/born-a-man-transgender-weightlifter-wins-womens-competition
Ring, T. (2017, March 22). Woman Killed in Baltimore Is Eighth Trans Murder Victim of 2017. Retrieved April 12, 2017, from T. (2017, March 22). Woman Killed in Baltimore Is Eighth Trans Murder Victim of 2017. Retrieved April 12, 2017, from http://www.advocate.com/crime/2017/3/22/woman-killed-baltimore-eighth-trans-murder-victim-2017
I was very surprised to see the amount of languages people are tweeting in about #transgender. Most of the class has a large percentage of English, but your data is heavily filled with German and Spanish. This really goes to show how global and issue transgender is.
Because of how new transgender is, there is still a lot of learning that people need to do. In the case of the man to woman transgendered woman, people do not have rules and regulations for it, so I assume that the tweets you read were both positive and negative. I think that that would a good thing to consider. The amount of people tweeting about transgender as positive vs. the amount of people who are tweeting about transgender in a negative light. When looking at your data set in comparison to mine, I think it is interesting how the murder of the transgender woman effected both of our data set, it shows how the LGBT community reaches all of the things it stands for, not just the LGB part. I wonder how many of our tweets over lap, where they are tweeting both #transgender and #LGBT. Might be cool to figure out.
Like in Olivia’s data, I am really interested in the wide range of languages included in your data. After looking at the large amount of languages included in both of your datasets despite the vastly smaller amount of tweets when compared to mine it makes me reconsider my own interpretation of my data. I now wonder how many languages I would find in my data if I were to use a smaller dataset. What also becomes noteworthy is the fact that although English seems to be the dominant language, it is only seen in 54.1% of your dataset. Even though it does make sense as to why English has the highest percentage, I think its really important to look more into why only about half of your tweets are in English.
I would also agree with your hypothesis and really like how you noted within your analysis the hashtags amongst the class that were generating a higher amount of tweets. By displaying the specific hashtags with high values such as #climatechange, #makeAmericagreatagain, and #blacklivesmatter, we are able to better understand not only the fact that your hashtag seems to be generating an average amount of tweets, but we are also given perspective as to which hashtags are more active. By adding this in I am able to draw general conclusions about how your hashtag fits into the discussion generated around the hashtags of the entire class.