In the article “Using Metadata to find Paul Revere”, the author simplifies how to find connections between individuals and organizations. Although a social network has the potential has the potential to be incredibly complex, Healy emphasizes that a simple piece of information such as a man’s enrollment in a school, can help find the crossroad of not only individuals that went to the given institution, but also the relationship between the multiple institutions. He describes how little information about an individual can solve complex questions. “The foundational papers in this new science of social network analysis, in fact, are almost all about what you can tell about people and their social lives based on metadata only, without much reference to the actual content of what they say.”

The study “Continuity and Disruption in European Networks of Print Production, 1550-1750” takes what is mentioned above to a whole new level. The computational analysis of art to then use as social networking for painters in the Netherlands shows how far we’ve come in data cultures. The study not only finds trends between artists but finds whether popular artists set trends in the 17th century. Its always been known that the best artists can influence style and skill shifts in the world of art, but this computational analysis program allows us to find who, when, where and what exactly is shaping the artistic world.

JM mentioned something very relevant to these texts. She stated that “the problem with history is that people need to understand that everything that is written down comes from a perspective”. The past is very complex. The majority of history is proven to not have been recorded the way it actually was so it is vital for us to consider this when analyzing answers presented by a computer. Computers are smart, yet they can’t grasp the complexity of humanity’s flaws.

An article expresses how topic modeling and text mining can make discoveries of the past. In this article using textual data and the text mining of multiple journals, essays, and letters, the researchers found the fascinating story of James Hemings.  Jefferson’s servant cook’s story is a story that would’ve been found without the help of text-mining. The lack of using his name, his outstanding skills and significance to Jefferson in literature made it impossible for historians to recognize the importance of Hemings. But thanks to text-mining, these researchers found patterns and consistencies of hundreds of letters and journals which lead them to the story of Jefferson’s cook. The article states: “Its exhilarating to think of the many ways in which digital tools might transform the archive of American slavery— pushing forward theories about the archive, arguments about its contents, and new forms of criticism that illuminate the past and inform the present.” Especially with suppressed groups throughout history, text-mining may be a way for us to dig out the ignored past of people.  A study in the “Digital Humanities”, too, focused on how social networks in texts could help us find relationships within countries — specifically Britain. But just like the Hemings article, biases in literature contribute to the meddling of data. The undermining of an individual based on their race, gender and religion, present in the 17th and 18th century, would heavily impact the results of the study. In recognizing this, the study stated: “A bias towards men is a known issue in existing historiography; this bias is neither confined to the ODNB nor particularly surprising. However, transforming textual secondary sources into visual representations allows for more purposeful “critical scrutiny of what is known, how, and by whom”. In response to last weeks readings, RF stated that “in some cases, the debate surrounding topic models is too concerned with the success of the algorithm itself opposed to the human space that the algorithm is working in”. In this, he means that the field of algorithms and the humanities are still rather polarized. As researchers in the humanities become more accustomed to the use of data analysis and text-mining,  society becomes a more connected and organized system.

As discussed in the previous reflections, data mining, text analysis, sentiment analysis and now topic modeling a contributing in unimaginable ways. These advancements in the data modeling world have expanded its impact in the world of humanities — specifically literature. Using Topic Modeling, computers are able to recognize patterns, themes, and keywords in texts. This has allowed for better research and analysis on human society in many different ways. Data’s ability to solve issues is no longer constrained to numbers. Today computers can crack codes, analyze speeches and extract key elements in literature at rates much faster and efficient as humans did previously. In the study “Where, What, When and Sometimes Why: Data Mining two decades of Women’s history” uses these skills to recognize gender inequality in literature of the past and current article publication rates. Using “word frequency”, computers run programs on finding the frequency of words such as “he”, “him”, “his”, “she”, “her” and “hers”. The results showed that male pronouns were used significantly more often than the female pronouns, which clearly alludes to some question about gender equality in all aspects (considering it’s coming from literature). NB discusses the pattern of white NFL players more frequently being called “intelligent” and black players more frequently being called “natural”. He writes: “studies like the one in the article are easy to conduct, but are they completely relevant and accurate in their findings?” He further discusses the potential of this trend to start off with because there are different quantities of black and white players in the NFL on each team across the team. The same may apply to today’s study. Since women were less represented in academia, the further we go back in time, some of the data we collect today on the literature of the past may not perfectly reflect the gender inequality in today’s society. In retrospect, it’s important to how exactly the topic modeling program is being run to know what exactly the results are saying.

A study, “Sentiment Analysis and Subjectivity” discussed the impact the web has made on individuals’ “opinions”. With the immense popularity of the Web, humans have more access to information, beliefs, and ideas previously not made accessible. Arguably, before the Web, opinions were heavily shaped by the place a person was born, their parents and developments in their lives. With the introduction of the Web, and its role of expressing news, information, and ideas,  people’s opinions are being less shaped by their immediate surroundings. “The Web has dramatically changed the way that people express their views and opinions. They can now post reviews of products at merchant sites and express their views on almost anything in Internet forums, discussion groups, and blogs, which are collectively called the user-generated content” the study states. The web and its power to influence peoples opinions on a global scale may result in a more uniformed or divided planet. The second study “Text Analysis and Visualization” discusses the unhuman speeds of text analysis. Computers are able to read, analyze and find trends in thousands of texts at rates much higher than a human could. This is a massive breakthrough in terms of identifying social and political trends through texts such as books, articles, essays, and even speeches. Even being able to identify trends below the surface can contribute to understanding social changes over time. Syntax, use of language and tone are attributes which can be identified and contribute to data sets. HR reflected on the use of data mining. “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.” The ability for computers to recognize themes in a text at the same efficiency humans presents us the opportunity to learn more about the past, analyze the present and forecast the future in ways humans have never been able to before.

The idea that data is purely used in math, science and the numbers of the world is quickly changing. Data seems to be a help to the humanities as well. Computers are able to analyze and turn stories and words into data models to help us further understand the past and our future. From the article Alien Reading: Text Mining, language standardization, and the humanities, the author explains how today computers have the capability to understand culture and language at the level humans can. He discusses the power of text-mining by saying: “Thinking of text-mining programs as objects of cultural criticism could open up an interchange between digital scholarship and the critical study of computers that is productive in both directions.” Using this, computers are able to understand biases and human tendencies to further think and act like humans. With that said, there are many complexities. For example, comprehending language and cultural changes over time. The study Words That Have Made History, Or Modeling The Dynamics Of Linguistic Changes examines the complications of stylistic shifts over time. Syntax, punctuation, and tone are constantly changing. This may be the divider between the computers and humans’  ability to be “culturally critical”. As our culture changes over time are computers able to keep up or do they have to be re-programmed? CN discussed the complexities of humans which is very difficult to record through computer data systems. CN writes: “Theodossiou decided to correlate all of the test subjects emotions to the amount of money they get paid, but there also may be some external factors that affect productivity in the workplace.” To function effectively in humanities, especially in the realm of psychology, computers need to include the complexities of culture and language to make accurate analyses on studies. 

Theodossiou’s study looked at a large variety of factors and correlations between low-paid jobs, unemployment and individuals’ psychological well-being. At first he presented a survey to unemployed workers asking a variety or questions regarding mental health. I curious however how in depth this survey was. Especially when a study is done on mental health, there can be instances that people with deep rooted mental health a) wouldn’t volunteer to take a survey and b) admit that they have a mental health issue. The same goes for unemployment. People don’t often like to talk about being unemployed. So these are examples of how the data could be affected throughout this study. Asking these questions to yourself while taking data for a study is vital for the results of the experiment. Next, as I read through the study I was wondering if we could find the causes of the two factors (mental health and unemployment) Is it the unemployment  of individuals that can cause more self doubt, or is it the possible mental instability that gets people fired from their jobs? It could very likely be both. AW mentions, that “data is dependent on the individual’s situation- should be considered when we analyze all data.” (Oct. 8, 2018) This especially pertains to the intricacies of mental health and unemployment, because it can be very difficult to find the true causes and effects of unemployment and mental health.

Data can get incredibly complex. Especially when different perspectives present countering data points, it gets difficult to identify which information is most accurate. Simpson’s Paradox is an example of how data, recorded from two ends, spits out completely different results. Studying the acceptance rates for graduate school, data shows that less women get accepted than man. However, from the perspective of the university, the acceptance rate for women is higher than that for men. Simpson’s paradox is a prime example of why data should never be considered from just one perspective, nor should it be recorded to a limited extent. From the study on mortality rates in North Dublin workhouses we see similarities in data complexity. During the height of the famine a high proportion of “immigrants” were dying in workhouses compared to “Dubliners”. Data, here too, could be vary depending on perspective. Immigrants higher mortality could be defined by the poor treatment and malnutrition of the outsiders. But what if we looked at their origin? Through that we can look at how foreign genetic pools may disadvantage immigrants’ immunity against the given virus circulating Dublin during the famine. Here, different perspectives give different results; and these are trends we see in many data models. Last week Elizabeth Cullen discusses how the development of data models is often overlooked. Mentioning the influence data recorders can have on data outcomes she writes: “they wanted to ensure that the data was from a group of extremely similar people and a cohabiting relationship is what they decided would be a factor of a similar group of people that would provide good supporting data. Considering this and Simpson’s Paradox, as individuals absorbing data, we must remember the complexities and biases of data models.

In the Pew analysis we see the importance of not only mentioning one source of data. The decrease in recidivism in the United States was the topic and main focus of the analysis made. However, the Pew article mentioned important data trends that both directly and indirectly influence the argument made. The article mentions both the decrease in crime rates on the state level and the decrease in incarceration which is vital data information to consider when analyzing the recidivism in the U.S. So how does this relate to the wage gap of gay employees? In the study hundreds of pieces of information are mentioned that standing alone would have no relevance. The study doesn’t only look at the wage gap between different-sex males and same-sex males, but it analysis how that wage gap is compared and influence by other wage gaps — such as women or black workers. Looking back at Julia Middlebrook’s post from last week, she mentions how we can answer macroeconomics questions by looking at microeconomic trends. Approaching data from different angles, as she mentions, and as we see in the studies discussed above is vital to understanding data at a deeper level.

In the Asimov excerpt the study of psychohistory highlights the fact that data is more than just numbers. The chapter starts off as using psychohistory as a “nonmathematical” (yet sorta mathematical) concept that deals with human conglomerates to fix social and economic issues. The readings reflect the fact that to arrive to fixing these social and economic issues, there isn’t only one data set or statistic that will answer those questions. The foreword by Fogel on Labor Productivity, too, emphasizes that the macroeconomic question could not be answered without looking at the microeconomic relations between laborers and their careers. Although microeconomics focuses on the small scale interpersonal economics, it heavily contributed to the macroeconomic hypothesis in the study. Both these readings show that the social issues we try to solve are never answered through one set of mathematical data, but rather through the relations and trends between multiple data sets recorded by our society.

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