HR – Data Cultures

Posted on December 6, 2018 by HR

Response: Social Media Data

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I thought the article was a great overview of the topics of social media data gathering. There are tons of benefits including ease of collection and lack of the Hawthorne effect where people in studies change their behavior merely on the fact that they’re being watched. These benefits make social media data extremely easy to gather and smart knowing that the data is genuine. However, there is also some drawbacks including biased data because of the relative similarity between social media users, these similarities even draw closer depending on which network the data is being gathered from. Facebook users will be much older than snapchat users and there will be many more women on pinterest and men on reddit. Depending on the type of population you are trying to gather data from, this can be a good or bad thing for an analyst.

I think social media is a great way to gather data from a wide range of subjects. Obviously it depends on the type of data you are gathering and the type of people you wish to gather data from, but the ease of collection and genuine answers make social media a great place to capture data for any purpose. Classmate HC agrees that “This gives a newfound accuracy to the data we collect through this medium.” There is a massive amount of accurate data around now that we would have never had access to before social media.

Posted on November 27, 2018 by HR

Response: Epistemology of Building in the Digital Humanities

The article goes in depth about the case of humanities academics who have turned to coding in order to build their credibility but have been questioned in the idea of how scholarly their work is. Digital humanities have been working to build onto theories for years in order to explain phenomena in the humanities subject. These theories are built “as hermeneutical instruments through which we can interpret other phenomena. Digital artifacts like tools could then be considered as “telescopes for the mind” that show us something in a new light.” In essence these digital artifacts and theories are not concrete items but are floating ideas can than cut to the core of the biggest questions in the humanities. Rather than dismissing these theories as lacking credibility there needs to be an equal amount of credibility on the reader to analyze the source. The internet is fair game and people will use it to their advantage as classmate MG says “we should assume credibility with a certain amount of doubt that allows us to critically examine the work before assuming its validity.”

Posted on November 15, 2018 by HR

Urban Electoral Coalitions Response

The Urban Electoral Coalitions article analyzes data from a local election in Los Angeles, California to understand the local demographics in the age of immigration into California. They used geography to capture this data as populations of immigrants are continually changing as the influx of immigrants increases and decreases depending on the region of the world they’re coming from. The researchers utilized colorful mapping technology to represent the different urban coalitions present in the city. I found this information and methodology very interesting as they were following demographic changes through mapping. They were able to track increased enthusiasm from Latinos as the mayor candidate was also Latino and therefore resonated with that population. Immigration continually changes these coalitions in American cities but with proper mapping technology we can follow these and understand the data and dynamics much better. We can draw interesting connections to Maine and the immigration to Lewiston and Portland through elections noted by KL, “The diversity within Maine is varied and not great compared to a place like Los Angeles. The diversity within cities like Portland and Lewiston must be acknowledged as well when looking at the voters map of Maine.” I agree with them on the basis that this methodology is universal and is extremely relevant to Maine in terms of immigration affecting elections.

Posted on November 8, 2018 by HR

Response Lynching, Visualization, and Visibility

The article does a great job of describing the visualization surrounding the lynching victims in the south through different methods such as maps and graphs. They begin to describe their goals to find and expose patterns in the lynching victims and cases across the south through proper visualization. I found that the map visual was the most helpful in explaining a pattern of location based “hot-zones” where the larger circles represented greater amounts of cases in a certain region or county. This visualization method presented the data extremely well in order to expose the patterns of increased or decreased cases in different locations. They mention that “Such visualizations are possible because of the datasets that have been gathered by scholars over many decades.” This explains the usefulness of data in cases such as this, more data is always better for creating and building answers to questions we have, the data never goes away.

In NB’s post, they note that “It would be interesting to see if policy during this time had an effect on this instrumental change in executions.” I agree with this because the data was taken over a period of great change in the political scene of the country and I would be intrigued to see if the various civil rights movements over this time had an effect of the amount and severity of lynchings across the region.

Posted on November 1, 2018 by HR

Response: Using metadata to find Paul Revere

I liked this article because the author wrote it like a class lecture or tutorial. He continued along a plot line trying to understand Paul Revere’s involvement in the many organizations within Boston. Using data analysis he picked his way through the multiple people and parties to find how Revere connects to many different organizations around Boston and signifies his ability have a lot of knowledge about the city. Highlighted by his famous “midnight ride,” this shows how he had the possible connections to understand such an event. A classmate noted “that the correlations and conclusions drawn from network analysis are only helpful in relation to prior knowledge or a data point that acts as a point of contention.” I would agree with this statement because there is a relative amount of bias within network analysis and without a data point of contention, there needs to be an amount of prior knowledge in order to lack bias in the network analysis.

Posted on October 25, 2018 by HR

Response: The Digital Humanities Contribution to Topic Modeling

Reading the “The Digital Humanities Contribution to Topic Modeling” article, I found it extremely interesting and informational on topic modeling. It seemed to be a well researched and truly informational piece by guest editors, who brought the sometimes complex world of topic modeling into a much easier to understand informational piece with multiple references to academic journals and more specific insights into topic modeling. The critical engagement section at the end was especially useful in offering actual insights into the information given in the article, for example saying, “Traditional humanities scholars often equate digital humanities with technological optimism. Rather the opposite is true: digital humanists offer the jaundiced realization that computational techniques like topic modeling — long held inaccessible and unapproachable and therefore unassailable — are not an upgrade from simplistic human-driven research, but merely more tools in the ever-growing shed” (Meeks). I completely agree with the statement because I feel like the concept of topic modeling has been long held inaccessible and thought of as too complex for the average reader cannot necessarily replace human research but offer an additional tool to an ever growing audience. A classmate also noted that the article “was very clear and concise, and really got to, what I think, is the crux of the issue for topic modeling.” (MS-A) Which I can completely agree with, topic modeling is important and necessary and its expansion to a broader audience is good for all users.

Posted on October 16, 2018 by HR

Response Week 7

I found the article about the text mining and language standardization very interesting as the use of data to “read” texts for their important points can create a whole new level of efficiency for projects where only basic information is needed. An ability for a computer to read through foreign text in an effort to mine out important language is something that can be utilized greatly for professions involving great amounts of reading. I found the reasoning behind the model structure interesting as it dates back to early times of the computer where “The idea of using a computer to automatically identify “topics” is in large part a product of the desire to exploit the increasingly large amount of text that was being distributed electronically in the late twentieth century.” Back in the late 20th century, computer text was exponentially growing as the rise in technology became mainstream, I think it’s so interesting how that information is dealt with using these programs in order to extract the main topics out of texts with relative ease and maximum efficiency.

Posted on October 2, 2018 by HR

Data Over Time

I found the Fogel and Engerman article extremely interesting because of its focus on the growth of data collection over time involving labor, health, and socioeconomic conditions. The data from early revolutionary America revealing that people of poor nutrition experienced higher death rates and lack of labor ability. Obviously this is something we understand today but early data collection in the 1800’s showed this as factual and influenced ideas around labor and nutrition for the future. Data collection advanced into the late 1900’s when the DAE began to collect data on inter generational families in order to understand linkage to women in the workforce and migration statistics. This data can be used to understand labor patterns throughout the history of the United States and help prepare for the future.