I expect my twitter data to be found generally from 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.
I have 3 mappable tweets, which means that .0014% of my total data set contain a geolocation.
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.
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.
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 small 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? 9/750 Naty .012% 16/ 21508 Julia .0074%
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.
One tweet that I found funny was a tweet from Brooklyn, New York and is said, “#diversity is #human and #American #lgbtrights _üá__üá__üåà @ The Stonewall Inn https://t.co/ucHpDkjSco” The picture attached was a protest sign that said, “executive order to ban orange from the rainbow,” a jab at Donald Trump and his lack of support for the LGBT community. Pretty funny.
Population of Black people In Brooklyn, NY
13.533% of Brooklyn, NY’s Population was Black in 1970
16.362% of Brooklyn, NY’s Population was Black in 2010
Population of Black people In Jackson, MI
3.631% of Jackson, MI’s population was Black in 1970
1.656% of Jackson, MI’s population was Black in 1990
2.311% of Jackson, MI’s population was Black in 2010
I found that looking at the break down of race in the locations I found my tweets in was not as helpful as I thought it was going to be. This data showed me that over time, the amount of black people in Jackson, MI increased and then decreased over time, and the amount of black people in Brooklyn, NY decreased and the later increased over time. This data does not say anything about the LGBT community, as any race can be apart of the LGBT community.
The article, “The Invisibility of LGBT Individuals in Black Mega Churches,” really shed light on how race and sexuality can often overlap. I was under the impression that all races equally accepted or unaccepted rather, sexuality in the same way. I would have assumed that the black community for the most part, as a minority and not “normative” would be more apt to accept LBGT community. In this article I learned that Mega Churches, which are primarily made up of the black community, do not support LGBT people. “Black LGBT persons have felt the necessity to downplay their sexualities in order to maintain a level of comfort with the black church.” When I looked at the differences in races from where my tweets were found, I originally did not think there was much of a connection. After reading this article, I can see if there is a correlation to the amount of people tweeting about LGBT rights and the amount of the population who are black in that area. The article, “Representation and Backlash: the Positive and Negative Influence of Descriptive Representation,” talks about how the more LGBT representation seen in government positively influences the role the government can play in creating rights for the LGBT community. This article relates directly to my twitter data, as more and more people spread awareness of the LGBT community, hopefully there will be more government shifts that will aid the LGBT community. As people tweet about the potential of a new supreme court justice who is anti-LGBT, this article suggests that change is only likely to happen if a representative of the LGBT community is a part of the government. LGBT community is rightfully scared, as this selection has potential to set back the progress LGBT people and supporters have made.
Software-sorted geographies is a term to describe how individual’s lives chances are shaped by their use and treatment of computer controlled devices, and how networks are becoming more and more structured to reflect an individuals “consumerist criteria.” I think that this is seen in my data set, especially with the idea of social geographies. I believe that over time, looking at the locations of my data can develop areas where there are “safe havens” for the LGBT community and where there needs immense growth and acceptance as well. This could also create a way to target people of the LGBT community to buy specific things that their ‘group’ might be more likely to buy than members who are not LGBT. If people are tweeting about LGBT, then they could be targets for people to become “mass customized” as stores and ad’s could being to target these people.
boyd, danah, and Kate Crawford. 2012. “Critical Questions for Big Data.” Information, Communication & Society 15 (5): 662–79.
Chaney, Cassandra, and Le’Brian Patrick. “The Invisibility of LGBT Individuals in Black Mega Churches: Political and Social Implications.” Journal of African American Studies 15, no. 2 (2012): 199-217. http://www.jstor.org.ezproxy.trincoll.edu/stable/43525420.
Graham, Stephen D. N. 2005. “Software-Sorted Geographies.” In The People, Place and Space Reader, eds. Gieseking, Mangold, Katz, Low, Saegert, 133-138. New York: Routledge.
Haider-Markel, Donald P. “Representation and Backlash: The Positive and Negative Influence of Descriptive Representation.” Legislative Studies Quarterly 32, no. 1 (2007): 107-33. http://www.jstor.org.ezproxy.trincoll.edu/stable/40263412.
Yau, Nathan. 2013. “Understanding Data.” In Data Points: Visualization That Means Something, 1–42. Hoboken: Wiley.