In Jim Enote’s video about Counter Mapping, he explains that there are many different ways in which to think about maps. He differentiates the maps that are able to exude the physical locations of places, and maps that are able to effectively tell a story. He claims maps that contain cultural information or important ancestral aspects should exist in order to create a sense of community to those that read these types of maps. Although this is extremely creative, I feel as if maps have a sole purpose, and intertwining this kind of information and maps would produce something more along the lines of art. KS’s piece made me think about this idea when he says, “It felt more like a piece of art that told a story. I’m not sure that is the same purpose of satellite view on Google Maps.”

In the article, Locating Place Names At Scale: Using Natural Language Processing To Identify Geographical Information In Text, the author demonstrates how researches are able to identify the place of an area located in the text. Computer scientists use programs such as, Named Entity Recognition (NER), and Natural Language Processing (NLP) in order to identity the names of places with text documents. Researchers can then use Google API in order to create reference points in which help highlight the geographical location of the named places. This article was extremely interesting because it made me think about the levels of depth when it comes to computer programming. DA sums this up well when the student says, “I think this article was a great way to explore the fault in some programming and show how searching for specific things does not always get you the results you need.”

In the article, Lynching, Visualization, Visibility, the article talks about how data visualizations can help discover hidden trends that ultimately took place when these lynching occurred. Although the data collected was important, I genuinely believe the visualizations produced did not help with any new or essential trends that were deemed not discovered prior to the author’s research. Historically, I do not not think we need extra data visualizations in order to make it clear that lynchings were based on power, constantly promoted through white supremacy. The idea that people needed these researchers to confirm this fact through graphs and charts is evidently futile and redundant. The numerical data published by anti-lynching activists were essential to the prevention of this horrible act, but the theoretical data that these researchers are perpetuating does not find any functional use considering these conclusions were already arrived at. I agree with the statement RF provides when he says, “This same ignorance can be seen in our attitude towards police brutality.  While the federal government gathers no official records of police brutality, the records that do exist at the federal Justice Department are known to undercount these records.” I think that  data like police brutality should be numerically published because this type of information can actually provide knowledge that has the ability to culturally change this behavior.

In Johanna Drucker’s piece titled, Graphical Approaches to the Digital Humanities , the author discusses the ways in which graphical analysis can be manipulated to suit a more ‘humanistic’ approach to visualizing data. She claims that data presented graphical is objective, and is simply a collection of columns, axis, data points that are presented the way that these pieces of information are collected. Drucker states that we are able to differentiate a humanistic approach to collecting data from this standard version of graphical analysis by giving the data a point of view, or a subject. This was extremely confusing to me because I do not see how providing the graphical information with a ‘point of view’ will represent the collected data in a different way.

I thought this article was extremely intriguing in which it made me think about how ordinary individuals use metadata in order to establish ideas about others. In this article, Using Metadata to find Paul Revere, the author demonstrates that it is possible to gain an understanding of Revere’s social network simply based on recorded data that describes his membership to groups and places. This made me think about how individuals use metadata in our every day lives in order to obtain information about people that we do not personally know. Presently, social media provides a significant amount of information pertaining to the types of posts people like, and the social groups that people are apart of. This metadata allows us to gain some insight about an individual, even if you have never made personal contact with him or her. BL adds that this data is important because of the network visualization that is produced. I would be interested in collecting data from social media pages and formulating a similar social network using the same processes.

In A Report Has Come Here, the author of this piece demonstrates his/her findings when extracting data from historical documents surrounding Thomas Jefferson’s personal chef, James Hemings. This article made me think about how this data was collected and the difficulty that comes with combing through historical documents years before a digitized society. I can only wonder how researchers were able to digitize all of this information from written text considering the number of documents that probably exist. Another piece of information from the article that I found extremely interesting was the fact that researchers were able to discover nicknames that Hemings was called by during his time in France. While impressive, this piece also made me consider the process in which researchers went about going through this data. I found that BL’s comment about using the individuals’ biographical information to be insightful when thinking about this question, “Using biographical data would make sense under these circumstances, as there aren’t many clear indicators of social interactions in more historical time periods that would provide enough data points…” This information speaks to how valuable text analysis can be in terms of discovering data that is present in between the lines of these historical texts.

Over the course of the last week or so, our class has researched, read, and have been taught the principle ideas of topic modeling and text analysis. I have learned that these two types of data extraction are extremely efficient and can also lead to the discovery of valuable information that would not have been found without the analysis of the compiled text. For instance, we read about the subtle differences in racial treatment through the descriptions of white and black NFL athletes. I found NB’s example of Colin Kaepernick to be extremely relevant when reading about this, “This doesn’t come as a huge surprise considering the recent events in the NFL involving quarterback Colin Kaepernick. Kaepernick, with both the IQ and athleticism to be an elite quarterback in the league, still has yet to be signed because he publicly expressed his disgust in the current state of the league.” While the information obtained through this specific example of text analysis is important, Sharon Block’s, What, Where, When, and Sometimes Why: Data Mining Two Decades of Women’s History Abstracts raised some essential questions for me. The only limitations I have about her topic analysis research is that the scale of her topic modeling may have been too large. If a researcher is concluding that the number of words that appear in a text document ultimately dictates, in this case equality of information within gender, word count is not a reasonable causation to this claim. I am not sure how one would go about collecting more substantial data for this, but I do not think assessing the number of specific words can prove that there is inequality among sources. Inequality in regards to publishing history relates to the fundamental issue of published works, whether something written is published about gender or women’s history. The data in this case would not be found in word count, but instead the number of abstracts that were presented and published, and the number of abstracts that were presented and not published. Topic modeling cannot efficiently answer this question.

In Sinclair and Rockwell’s Text Analysis and Word Visualization: Making Meaning Count , the authors highlight the importance and efficiency of text analysis. Throughout the article, Sinclair and Rockwell provide examples of how the use of these semantic programs are effectively able to produce information on digital texts that exist. Specifically, they describe how visualizations are able to help collect important data, “Visualizations are transformations of text that tend to reduce the amount of information presented, but in service of drawing attention to some significant aspect.” Although this is extremely valuable information, it is only made significant by the individual examining the data. While text analysis is important, the data that is collected can only be used if it interpreted by the user. In Sinclair and Rockwells’s interactive NFL example, only the individual can determine the significant information that emphasizes the racial discrepancies in player description. A classmate of mine succinctly poses this question , “How is a computer to know what items are of importance and know better than a human would?” These are essential questions that are necessary for the development of semantic and text analysis. While I think creating a program that is able to differentiate this significant information is next to impossible, the extraction of analysis in extremely complicated and long texts is very important.

In her research study, Words that Have Changed History, Or Modeling the Dynamics of Linguistic Changes, Marciej Eder explores how the frequency and acceleration of language has changed over time. She does this through a form of graphical trend lines that helps to model the ways in which the researchers have measured language changing over time. Although this is fascinating, I do believe there is a fundamental error when collecting this type of data. Eder writes, “More important, however, is the fact that the scores are not even: the signal becomes stronger in some periods, clearly indicating an acceleration of the language change.” In order to identify these changes, the researchers rely on their own judgement when relating historical events to the acceleration of language. Biased data is flawed and unreliable because that allows for researchers and other experimenters to frame data and other elements of an experiment to their own liking. In addition, I also believe that the researchers require more specificity when testing language change over time. As NL states in their post, “this becomes very relevant because of the ambiguity of the word selection. The study could have chose completely different words and may have formed a different conclusion.” The trend lines created were based off of the frequency of mostly “common function words”, causing a lot of unclear data.