I expect my twitter data to be found generally\u00a0from 0 degrees to 90 degrees longitude. I think this because in the news, a lot of LGBT protest, news, and stories come from America, especially with the Trump Administration. I think that my hometown is somewhere near 50 degrees, 110 degrees longitude. I believe that there could be a few tweets from that area as LGBT communities can be found anywhere. With the recent Women’s March held in Boston and the general support that all LGBT people have for one another, I would not be surprised to find tweets from my area.<\/p>\n
I have 3 mappable tweets, which means that .0014% of my total data set contain a geolocation.<\/p>\n
I had a total of 2,036 tweets containing this hashtag. 3 of them are mappable, so .0014% contain geotags. After reading Yau’s description on how data sets can shape visualization, I’ve realized that out of context, my mappable tweets collected to not represent the reality of the LGBT community. Yau writes, “when you don’t consider what your data truly represents, it’s easy to accidentally misinterpret. Always take uncertainty and variability into account. This is also when context comes into play.” If I were to just look at the 3 mappable tweets, my data set would lack both diversity, accountability, and context. Because the sample size got so much smaller, it is hard to create visualizations accurately considering it only represents .0014% of my data set. All of my tweets are in English, and from the same general area. All three tweets are in the (40 degrees, 70-80 degrees longitude) so all in America. I predict that these tweets are in the New York area as well. I think this is because of the LGBT community in big cities.<\/p>\n
Although “bigger data does not always equal better data,” I do not believe that I have collected big data. I have only collected a little over 2,000 tweets. This is not enough to say that it represents a group accurately. In order to consider this big data, it should somehow shift my knowledge, and this data set does not do that.<\/p>\n
All three of the members in my group had relatively the same percentage of mappable tweets. Naty had the highest percent, but I think that is only to do the\u00a0small sample size of 750 tweets. Julia and I had a very similar percentage. I think that this has less to do with our individual hashtags, but more so with the fact that more and more people are not allowing their locations on twitter, and maybe the members and supporters of the LGBT community are aware of location services? \u00a09\/750 Naty .012% \u00a016\/ 21508 Julia .0074%<\/p>\n
I have found that in my first map, the majority of the data I collected is found in New York. I predicted this earlier in the lab. I believe that because New York is such a large and populous city with strong opinions that is why a lot of the geo locations are there. However; I feel that this map does not represent my data set at all. Only three tweets are on this map so it is hard to be able to draw any accurate data and representations without making assumptions.<\/p>\n\n\n