{"id":2423,"date":"2017-03-03T15:44:50","date_gmt":"2017-03-03T20:44:50","guid":{"rendered":"http:\/\/commons.trincoll.edu\/amst-data-driven\/?p=2423"},"modified":"2017-03-03T15:47:38","modified_gmt":"2017-03-03T20:47:38","slug":"words-behind-islamophobia","status":"publish","type":"post","link":"http:\/\/commons.trincoll.edu\/amst-data-driven\/2017\/03\/03\/words-behind-islamophobia\/","title":{"rendered":"Words behind Islamophobia"},"content":{"rendered":"

This week for my data, I chose to look at the last week of tweets. I hope that the tweets I see are positive and accepting of Islam rather than opposing it. I hope to see words of support.<\/p>\n

I have 173, 596 total words and 8,640 unique words.After I got rid of the initial stopwords, I still had a lot of sorting to do. Originally, when looking at the data the most frequent terms were Islamophobia, rt,\u00a0\u00fb_<\/a>, http, and city. This is clearly causing my data to be obscured because 3 of these 5 terms do not have anything to do specifically with my hashtag but more to do with Twitter in general. I do not think that these words are useful because they are words that do not add an context to my data sets. There were a lot of weird symbols that were in my text analysis picture that I needed to get rid of. I feel that there is a fair amount of data still missing from the picture. I had to enter stopwords many times in order to finally have a picture that made sense.<\/p>\n

Stopwords:\u00a0etc, http, htt, ltd, m103, \u00fb<\/span>, \u00fb_,\u00a0<\/span>\u00fb\u00ef<\/span><\/p>\n

After I had entered many stopwords in to my list, the most frequent words in my data were Islamophobia, city, muslims, mosque and ban. I am not shocked to see the words muslims and ban tied to my hashtag because of Donald Trump’s muslim ban and how these two issues are correlated. The words that appear in my data does not surprise me and make more sense the ones I originally saw. I felt as though 105 words in my word analysis cloud\u00a0helped get the best picture of what my data represents.<\/p>\n

My word analysis cloud is filled with words like hate, hatecrime, fear, racist, prosectuing and many others. This word cloud\u00a0gave me a better picture of what people are saying regarding Islamophobia. I think one thing that is interesting about looking at this cloud is that you are not entirely sure how people are using the words you are seeing.\u00a0I would assume when looking at the word hate that people are using that word to talk about how people being Islamophobic is a bad thing but in actuality the tweets could be people tweeting about how they hating Muslims. My word cloud suggests ideas that I would assume would come up when discussing Islamophobia.<\/p>\n

\"Screen<\/p>\n

I chose to look at the terms city, muslims, s_t_o_p_terror, prosecuting, and realdonaldtrump. I chose the term city because it other then Islamophobia it was the second word used most, 4,215 times. I chose\u00a0realdonaldtrump because his new policies are directly affecting Muslims not only in America but all over the world. For this reason,\u00a0I also chose the term muslim. The other 2 terms just seemed interesting to me which is why I picked those ones. Looking at my line graph, the terms\u00a0s_t_o_p_terror, muslims and city a line that follows the relative same trend. The word city peaks higher than the other lines. This term peaked on February 28. I am not sure why this term peaked on this day. After having searched Google, I found that one possible reason for this spike on February 28th could be because Michigan agreed to be a “safe haven” for refugees. The term realdonaldtrump peaked on February 25 and maintained a steady line after this day.\"Screen<\/p>\n

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http:\/\/voyant-tools.org\/?corpus=23e6bb486b18d250df518de127c0c3f8&stopList=keywords-b9c1bb932d35e006a29e23d3102a3c98&panels=cirrus,reader,trends,summary,contexts<\/p>\n\n\n