Social media is an ever growing platform for people to share their thoughts and opinions, but it is also becoming the best way for some research to be done. Because posters don’t think about researchers looking at their post in the form of meta data, many different biases are avoided. This gives a newfound accuracy to the data we collect through this medium. That doesn’t mean it is a perfect source for information though. Because the research is being done online, there is no way to tell who it is that you are getting your information from. There can also be a bias, because not everyone has access to the internet, or posts their thoughts on social media.

In “Blog Post 8: What you can, can’t and shouldn’t do with Social Media Data” by CN, they also reference the positive and negative outcomes of using this method of data collection. It seems like this should just be another tool researchers use, and not replace all forms of data collection.

In “Urban electoral coalitions…” we are shown the shifting demographics of an increasingly diverse Los Angeles. Many districts have overwhelmingly different racial and ethnic make-ups, mostly due to immigration and internal migration. This leads to many different groups wanting to be represented in the cities’ politics, so various coalitions must be created to garner a majority of the democratic vote.

This phenomena isn’t unique to Los Angeles, because most Western cities have their elections take the same format, while also having many different groups wanting to be represented by one party. But the authors looked at L.A. because it is so densely populated and because of that, there is more data, which in turn makes it more accurate in its representation.

I find it very interesting that the study considered a district “Latino” if the population of the district was 50% Latino, but to be considered a “Democratic” district, a higher barrier, 52%, needed to be reached. I think that shows how liberal the city is, and that a district that is 51-49 Democratic isn’t really a “Democratic” district.

In RC’s “Counter Mapping Response” they explain how a map is more than a picture of the landscape. It is an emotional representation of the land and a people’s connection to it. I think mapping out voting blocs is similar because racial groups don’t always vote a certain way, but if there is a candidate that focuses on issues predominantly felt by one community, it makes sense that there would be overwhelming support from that demographic.

The movie industry is a multi-billion dollar industry, with some of the largest franchises and movies making over a billion dollars upon release. American movies always make most of their money domestically, but there are some “rules” as to how much they’ll make abroad. One of those rules is the 10% rule that states that a movie will make around 10% as much in Australia as it did in the states.

In “Understanding the dynamics between the United States and Australian film markets: testing the ‘10% rule'”, the authors used data from lots of different movies from across many years to prove that there is a lot more going on than the 10% rule. Some films that don’t preform as well in the U.S., like “Noah”, perform very well in Australia because of the religious influences the movie has. They proved the rule to be wrong with their findings, and as usual, data disproved a common belief that had no grounding in the facts.

In RF’s post “Data in the shadows”, they explain how public discourse affects what data is collected. When accurate data isn’t being collected, the public can be mislead to the severity of a situation. This shows the importance of data, but it also shows how dependent we are on perception and image as a  society. If public opinion states that something is important, there will be more data collected on it, which can be devastating to the people who are gone before the public changes their mind.

Even though “Using Metadata to Find Paul Revere” was written in a very distracting, old-timey style, it was still very interesting. Using metadata to find terrorists seems like a wonderful use of technology, but it also brings up many dilemmas in regard to privacy. Do we allow our government to spy on us in the hopes that it makes us a little safer?

Using this data to see what groups each person is a part of creates a giant web that interconnects all the people in the world. It becomes easy to see where and when people are talking, which then leads to knowing what they’re talking about even without being in the room or listening in.

As MLC said in “Social Networks and Paul Revere” social networks are a great way to stay connected in an ever changing world. It also helps track communication and can see what persons of interest are saying about certain things.

Topic modeling is an important field that will allow computer programs to better analyze and synthesize categorical data. While the programs we have right now aren’t perfect, they’re improving with machine learning and our understanding of their code increases over time. In “The Digital Humanities Contribution to Topic Modeling” Elijah Meeks and Scott Weingart argue that the tools we have at our disposal are too blunt at the moment for them to be taken seriously. While I agree with this sentiment, I don’t believe we should give up on the whole field just because they haven’t produced a perfect finished product yet. I also disapprove of their writing style, because the complexity of the words they used distracted me from the content of the piece.

EC said in the post “Can the Humanities Topic Model?” that we should remember that topic modeling is just a tool to help us, and shouldn’t be the only thing we use to analyze the works in the humanities. We shouldn’t allow ourselves to become too dependent on computers and programs to do our work for us, and this is a great example of where we have more knowledge about something than a computer does, at least for the moment.

Understanding what a block of text means is hard enough for humans to decipher, so how could we program a machine to do it for us without it having similar flaws? The simple answer is we can’t. Humans are still better at figuring out what a string of words means because it isn’t something that involves computational thought. To understand a piece of writing you have to not just know what each word means, but what they mean together, and in a particular order. You also have to figure out the tone of the piece, because the same sentence in one part of the document could mean something totally different in another. It’s comforting to know that computers can’t seem to quite get this figured out, because there seems to be something uniquely human about the written word, and without it we would be just like the other animals on earth. I think it’s good that the programs that read big blocks of text are still improving though, because I see the value in having condensed summaries of huge legal documents or scientific articles printed out by a machine that can summarize them better than a human could. Based off of the other responses to this article, especially “DCS Response 2 for 10.16.2018” by PE, it seems that most other people feel the same way. We are happy computers aren’t as good at us at this one task, but we understand eventually they could surpass us.

In the Theodossiou article, the author tried to answer if not having a job affects well-being more than having a low paying one. The study was done in 1992 Britain, which I would argue might skew the data towards joblessness not having as big of an affect on their life as it would in other countries. There is a running joke, which is widely accepted in Great Britain, that British people bottle up their feelings and don’t express them fully, even when given the opportunity to. In this case, I think that the jobless people when asked might under-report how much being unemployed negatively affects them. I think the study would report very different data in countries that are more accepting of sharing ones feelings. I also believe that the study was unclear in how they asked the subjects to report how they felt, because they were only given a scale of 1-4, and the wording they used for the scaling seemed very similar from one level to another. This could skew the data even further, because if a participant doesn’t fully understand what they’re responding to, how can that data be accurate? Overall, I felt like this study was important towards explaining human nature and the value we put on work, but the way in which it was conducted seems to detract from the validity of their claim that it is better for your well-being to have a low-paying job than none at all.

In MLC’s post “Unemployment on Psychological Well-Being” the most important issue to me was discussed. I think it is very important to have questions that provide accurate data, and I don’t think the way in which they gathered their data was the best way to do so. I would’ve liked if the scale was greater, like 1-10, so that you could see more variation in answers to see the relative difference in responses.

The PEW study on recidivism rates dropping from 2005-2015 paints a wonderful picture of an improving prison system, without enough data to back it up. There are plenty of states where the 3 year recidivism rate has dropped over the past 10 years, but they only showed/used data from 23… which is less than half of the country. It is hard to completely dismiss the findings though, and because of that we can see that programs aimed to help inmates upon release seem to be working. The goal of these programs is to save the taxpayer money by changing the behavior of inmates, so that once they are released, they are able to reintegrate into society. I think the article is made less trustworthy by showing how little data they used, which is unfortunate because it’s a feel good story, and you’d like to believe we’re making progress.

This is my first attempt at posting, hopefully it goes well!