For the network analysis praxis assignment, I looked at what hashtags appear in tweets that contain “Ted Cruz” or “#tedcruz” during an 18-minute time frame. I used the Twitter Streaming Importer plugin in Gephi (video tutorial), which collects tweets according to set criteria and presents a network visualization of the results. Seeing as the Texas Senate race is one of the most visible in the midterm elections – and because the senator draws polarizing opinions – I was curious to see whether there would be a disparity in the kind of hashtags associated with Cruz tweets. I went with the short period of time so that the visualization would not get too crowded, and because the incoming data was becoming a bit much for my computer.

A few questions going into the assignment:

1. Easiest one: Which hashtags appear the most?
2. Will one side of the political spectrum tend to use certain hashtags (that don’t clearly indicate preference) over the other side?
3. Will there be more hashtags from presumed Democrats or Republicans? That is, based on hashtags that clearly show political preference.
4. Are any [hashtagged] figures frequently mentioned in “Ted Cruz tweets,” such as Trump or opponent Beto O’Rourke?

Hashtags resulting from an 18-minute search of tweets containing “Ted Cruz” or “#tedcruz.” The darker-colored nodes contain the hashtags that appeared the most frequently, and are connected to the nodes with hashtags that also appeared in the same tweets.

It took a very long time to figure out how to establish this kind of visual hierarchy through color and size but I was amazed at how Gephi could map out what looked like a mishmash of hashtags, collected through the plugin, into something observable.

I highlighted in blue and red (not through Gephi) what appeared to be hashtags coming from Democrats and Republicans, with the rest possibly going either way.

Some casual observations:

• The most prominent hashtag “on the right” is #maga, which is tweeted alongside references to Trump (#trump, #trumprally), nationalism (#trueamerican, #cruzcaresaboutamerica) and the caravan (#stopthecaravan, #stoptheinvasion).
• Meanwhile, uses of “vote” (#voteblue, #votedemocrat, #voteforamerica) dominate “on the left” side with numerous mentions of O’Rourke.
• Trump hashtags appear less than I thought they would on the left side.
• While Texas is certainly mentioned numerous times, the country as a whole is well represented on both sides, perhaps more so than usual given the current political atmosphere.
• It seems there are a bit more Democratic-leaning hashtags in this pool, though it is certainly not an indication of how many tweets the two candidates in this particular race are generating – only of tweets that include Cruz’s full name.

Limitations, questions and possibilities
In addition to the brief period of time from which the tweets were drawn, the sample of “Ted Cruz” tweets is also limited in terms of the sample of Twitter users – specifically, those who use hashtags, and of course those who mentioned Cruz. And there’s always the question of who or what is generating the tweets, and how this can determine the usefulness of the data.

I applied the timeline feature, which Micki showed in her presentation, to see the growth of the number of nodes (though it was slightly more satisfying to actually see the feature itself working). With more tweets and a longer time frame, it would be interesting to see if there are any patterns based on time of day, clumps of hashtags appearing at once (i.e. ample use in a single tweet), the increased variety of hashtags, etc.

The plugin also has the option of pulling only usernames or emojis which for this “experiement” seemed more difficult to discern patterns, or pulling everything (hashtags, handles, tweets) which when laid out on the visualization was a bit of a mess and seemed to require beyond-beginner skills to clean up. For my attempt at using Gephi, going the hashtag route seemed the easiest to manage while still producing something that could be examined.

From what I could see, using the hashtag option only returned hashtags and timestamps. Through the latter you can tell which hashtags likely appeared in the same tweet and I believe it’s visualized through the ones that appear to be grouped together. The other options in the plugin for pulling information produced more data – that helped in tracing back to the original tweets – so would provide other kinds of opportunities for analysis, as would the statistical algorithms included in Gephi.

So for my praxis assignment (after spending an extensive evening trying to understand and figure out Gephi) I decided to go with Palladio, the engine developed by Stanford University. After playing with the provided sample dataset provided n Palladio, I encountered the frustrating issue of finding a dataset that I wanted to work with and worked in Palladio. The initial set I wanted to use was not available in the form of a comma separated value file (.csv), so I had to keep looking. This is when I found www.data.gov,  which provided over 18,000 datasets from our area alone. Now that I was in a dataset wonderland, I had found more than several interesting data topics I wanted to work with from disability to car accident statistics. However, this is when I ran into the final pothole in the road to success with this praxis assignment. The sheer size of the dataset played heavily in my ability to use. Plugging in a dataset with well over 4,000 brackets of data tended to cause Palladio to crash entirely. Given Palladio is an open-source digital tool, I’m sure its power to remold datasets only goes so far. Finally, I found a dataset that came in the proper format and was a reasonable size to plug into Palladio.

I decided to work with a dataset titled Hate Crimes by County and Bias Type: Beginning 2010, based on New York State counties. Being someone who fits into several different marginalized communities, this was something I was especially interested in. I was born and raised about a couple hours north of the city in Ulster & Dutchess counties. However, growing up I also spent a lot of my time with family down here in the city, so knowing about these two different cultures and lifestyles I was curious to know the difference in terms of violence against these people labeled as “other.”

I mainly utilized the graphing feature on Palladio. This feature divides the source subject into nodes, and then they connect them to the target nodes in order to represent connections in the dataset. Before diving into specific the counties, I started by showing the correlation between hate crimes against properties versus hate crimes against individual people/groups of people:

Crimes Against People Vs. Property Crimes

This was where I first learned something thanks to the visualization of the data. Notice how the connection between the nodes create a bowtie-like image, it shows that there is a great amount of overlap between these property crimes and crimes against people themselves. This is not something I imagine many people would take the time to scour through the dataset and mark down these similarities, so having this software where you can plug the data in and have it spit out a visualization like this is incredibly useful.

So another common issue I decided to look into before tackling individual counties was crimes against religious practice. We live in a post-9/11 society, and as a result, there is an unfortunately extreme level of prejudice against those within Muslim communities. So first I went ahead and searched the total incidents of hate crimes against religious practice as a whole, followed by specifying hate incidents towards Muslim practice:

Total Incidents of Anti-Religious Crimes

Total Incidents of Anti-Muslim Crimes

In comparing the numbers as well as the intensity of the nodes, we can see that there are many general hate crimes against religious practice as a whole, but if you look at the following graph that focuses primarily on anti-Muslim hate crimes you will see that a truly astounding percent of the religious hate crimes are against Muslim religious practice. This obviously reflects off of what I previously mentioned about the existing prejudice, and it validates that statement.

Alright, now for the New York State counties. For starters, I decided to look into the total number of hate crime offenders versus the number of hate crime victims:

Total Number of Offenders by County

Total Number of Victims by County

Interestingly enough, you can see by the large cluster of engorged nodes in the offenders graph that there are a significantly higher number of offenders than victims. This is because a hate crime does not always mean physically damaging a person or group. So we can see that this significant gap between offenders and victims signifies that hate crime offenders are practicing in much more subtle ways such as only serving/hiring people within a specific demographic, intentionally excluding others.

One issue I did run into while going through this process, was a strange inconsistency in the statistical graphs for anti-gay hate crimes that took place. Below I provided the total number of incidents involving hate crimes against the gay community, and the counties where these hate crimes took place:

Total Number of Anti-Gay Hate Crimes

Counties Where Anti-Gay Hate Crimes Took Place

According to the first graph, there were a staggering number of incidents in terms of anti-gay hate crimes between 2010 and early 2018. However, in looking at the specific counties, it is showing it only took place in a few different NYS counties. There could be a misconnection with the data somewhere, but I am incredibly novice at this so I am not sure where. That or a disturbing amount of hate crimes took place in only several counties.

The last example I’ll provide shows anti-black hate crimes across New York, and I think it gives up some perspective on historical events:

The Number of Anti-Black Hate Crimes

Thanks to this visual, we can see that a lot of the anti-black hate crimes take place on the southern end of New York State. Historically, there are tensions between inner-city communities and those of suburban communities such as Westchester County and Yonkers. Upon ideas such as public housing entering these suburban communities, their development was met with a lot of backlash provided by Italian and Irish citizens. This was primarily an issue when the country was still slowly desegregating, but these issues still run rampant today (as you can see by the visualization) and can be seen between New York City burrows and suburbia.

In the end, I thought this was a pretty neat little experiment to get a feel for these visualization tools. One day I will conquer Gephi, but today I am satisfied with my accomplishments regarding my analysis of this dataset using Palladio.

What is the value of teaching methodological tools with no inclusion of theoretical support that informs analysis? But what good is the theoretical when students struggle to learn arduous methodology in software like R Studio? Learning to program, from the outset, seems impossible. It is literally learning a new language, one that is mathematical and statistical. Andrew Goldstone articulates some very promising angles to approaching these dilemmas.

When it comes to having the methodological skillset, the big question is “so what?” What can you say about the visualization on your screen? I recently had this problem with my network analysis… okay, I curated data and created networks of characters that conversed with one another in Hamlet. What good is that for scholarship? Well, it is certainly good for my own personal scholarship. We all were told going into these Praxis assignments that the projects were more about getting experience with digital tools than necessarily revealing anything groundbreaking. You need to test the waters before you can commit to a full-on swan-dive. Goldstone understands this, but at the same time was teaching at the Ph.D. level where results mattered. His course sounded intensely painful but very rewarding at the same time.

My experience with R Studio is limited to one class I took. It is really hard software to learn because, beyond software functionality, there is also the problem of interpreting the R language and making it “do stuff” for you. In Goldstone’s fast-paced one-semester textual analysis course, the students sounded highly committed, intelligent and professional but that would be a given going into the design of the course from the outset, I mean they are Ph.D. students. How could he design pedagogy that would inform his students to create intelligent work and mobilize them to ask worthwhile questions of that work? In a very short time frame.

It seems that Goldstone had three major takeaways from his experience with this trial run of his course:

“1. Cultivating technical facility with computer tools—including programming

languages—should receive less attention than methodologies for analyzing

quantitative or aggregative evidence. Despite the widespread DH interest in the

former, it has little scholarly use without the latter.

2. Studying method requires pedagogically suitable material for study, but good

teaching datasets do not exist. It will require communal effort to create them on

the basis of existing research.

3. Following the “theory” model, DH has typically been inserted into curricula as a

single-semester course. Yet as a training in method, the analysis of aggregate data

will undoubtedly require more time, and a different rationale, than that offered by

what Gerald Graff calls “the field-coverage principle” in the curriculum.”

When I took Digital Humanities courses at NYU, the layout of the program was much different than ours. In the first semester of their sequence, you are taking an Intro to Python course that proved to be very challenging, especially to people with little programming experience (like me), because like Goldstone’s course, it met once a week. I struggled with homework and went to office hours every Monday morning. I would have benefited from this back-end approach of learning to look at and analyze what is being quantified before being expected to create it on my own. Then, when I would go back to the programming course at a later time, I would know what to expect to come out of the “other end.”

In DH courses, the internet is our oyster, to mark Goldstone’s second point. In other words, it is all of our responsibility to keep an eye out for that perfect database that has everything we all need (does that exist?). Sometimes it does take being a little creative and problem-solvable, I had to make my Hamletdataset by hand, but is that the worse thing for an intro-level course?

There isn’t much to say about the third point other than as we already have drunk the Kool-Aid of this program, we know, one semester just won’t cut it. There are so many concepts, theories, methods, programs, languages, practitioners and articles to read. We are lucky to call our program home because we get the time we need to delve into all that.

While learning to work with data, we must learn not only how to make the data “do stuff” but know how to ask the right questions of it at the right time. Because as Goldstone points out, how can one be sure a “trend is real, and not a random fluctuation?” It’s fun to look at data and believe you are pointing something worthwhile out. It’s less fun to learn what you’re looking at isn’t actually interesting by someone that knows.

It is important to learn to program because another point Goldstone makes is when using GUI interfaces, you are limited to the confines of the system. He uses Voyant for an example. Without having knowledge of coding, you are literally locked out from asking questions other than what Voyant allows you to. Perhaps this is another weakness in the tool that could be addressed in our letter to Voyant (if that hasn’t happened already).

The problem with learning too much methodology at once is what scholarly good is it serving? A balance of the methodological and the theoretical is essential for keeping checks and balances. I know in my course with R Studio, there was a great deal of both. My professor was a proponent of being sure to include theoretical readings along with practical assignments every week. I learned a great deal, and this class is what turned me on to data and DH. It is only through understanding the theoretical that the methodological clicks in such a way that scholars can ask appropriate questions. This is a very important aspect of pedagogy to me and is something that is put into practice in our program.

And of course, Goldstone makes an excellent point in that having guided datasets for beginner students is a great way for one to get their feet wet; “so that instead of being forced to fish for interesting phenomena in an empty ocean, students can follow a trajectory from exploration to valid argument.” It is always helpful to have a guide, especially when learning something so new and complex as programming and any other kind of work with data.

For my network analysis, I explored character interactions in Shakespeare’s Hamlet. I chose this text because I just finished reading it again for my textual analysis class for a project. Since I was refamiliarized with its layout, I figured I would be able to easily spot discrepancies in the output from Gephi, the software that I used. However, everything looks like it came out as best I could have hoped. I also knew there are many characters in Hamlet, so it would make for entertaining social network analysis.

Since I don’t know of any databases with Shakesperian play edge lists, I had to make my data by hand. This was the most time-consuming part. I went through the play and created a pair of edges in Excel for each time a character interacted with another, once per scene. In other words, it was a personal choice to only mark each interaction only one time a scene, as I felt like it would have been too tedious to do so every time character’s spoke to one another, as there are many long interactions. It seemed as though there was much more room for error with this route too. However, I did wonder how different my network analysis would have looked if I did do it this way. Perhaps in the future if I have more time, I’d go back and do it this way, or perhaps there is something built in the software where I could count the number of interactions and plug it in that way as opposed to having a binary pair in Excel in two columns for each interaction.

One of the issues I had that could be seen as a weakness on behalf of the data was deciding who was exactly interacting with who. There are scenes where, say, King Claudius is speaking, so do I mark an edge with everyone who is present in the scene? It was a judgement call, but I didn’t do it that way. I came to my own conclusion who is likely being addressed (there were multiple people in some instances) and I never made an edge out of an entire cast of characters in a scene. But it is possible that I did miss some interactions, due to skipping over characters being addressed that I didn’t realize as I was going through the text. This is where better/stronger textual analysis skills would have come in handy, so I wouldn’t have had to do this manually, but I am a way’s off from writing programs that would be able to pull out this kind of data.

There are five acts of Hamlet, so I had six Excel worksheets. One for each act of five or so scenes, and the final one is a composite of all the scenes to show the relations throughout the duration of the entire play. To be clear, I only made an edge pair per one interaction per scene, which are all on one Excel worksheet per each act, and the final compounded list. That list had 77 edges and 33 nodes. However it did work out that you can see some thicker edges and that signifies that the edge pair interacted more than once per act, so that is a cool thing to have illustrated.

Once my data was imported into Gephi, I spent a good deal of time choosing how I wanted to layout my output. I couldn’t leave the nodes in place as they were because they were too close to one another to be visible or useful, so I had to drag nodes around to different areas of the Gephi workspace. I also had to play around with Gephi a bit before collecting my data to figure out how I wanted to layout my worksheets in Excel. I realized there would be a learning curve to account for how Gephi would read sheets and spreadsheets. As I found out, or at least was able to come up with, one workspace in Gephi = one worksheet in Excel. So, I would be happy to know how to combine workspaces in order to not have to do that manually in my final worksheet like I ended up doing.

I wouldn’t say my analysis really reveals anything to provocative that you wouldn’t have understood just by reading Hamlet. It is pretty apparent who speaks the most and to whom. But it was fun and meaningful taking a literary text and mining it for data and then playing around with that data in Gephi. It has been a while since I’ve used this software last, so it was good to reacquaint myself with it. One thing I never really mastered in Gephi is how to find different types of visualizations such as the work of Micki Kaufman, who mad many. Mine is pretty bare bones, but it goes to show what social network analysis looks like and does in case anyone in our class had questions about it in terms of practicality. And I felt like a real digital humanist!

Act 1

Act 2

Act 3

Act 4

Act 5

All Acts

I really appreciated Ryan Cordell’s contribution to the Debates in the Digital Humanities 2016 edition, “How Not to Teach Digital Humanities.” I think it’s important that we take his guide into consideration when building future courses for undergraduate students with the goal of piquing their interest in digital humanities. Hearing the phrase “digital humanities” can be an intimidating experience for young students who are still figuring their own paths out. Upon hearing about it and researching much more fleshed out DH projects online, the thought of tackling what is essentially computer science is terrifying.

For myself, I was in a class full of English and English/Education majors when I was first introduced to the digital potential of the humanities. Being a class full of English students, whose bread and butter of work is writing papers, we were obviously a little hesitant to dive into coding literature. That’s where my professor’s pedagogical methods matched that of Cordell’s recommendations. For starters, as Cordell suggests, we started small. The class was titled “Digital Lyric,” which meant that we did not work with large pieces of literature. We worked solely with poetry and musical lyrics. This made it easier to tackle the incoming assignments. We were not so focused on reading large volumes of work, but rather on the concise prose that we eventually had to rework digitally. We start light, with simply engaging with these DH tools as a way to start a conversation amongst the class. For example, we used the digital tool Prism, which my professor worked on at the University of Virginia. As the website states, Prism is a tool for “crowdsourcing interpretation.” So my professor uploaded a poem, Ozymandias by Percy Bysshe Shelley, and we had to highlight the poem according to certain themes (each associated with a different color). In the next class, she was able to take all of our highlights and layer them over one another. It provided us with percents for each theme, and the class unfolded from there.

This was a very small start in terms of the massive tent that is DH, but it was easy and exciting. We were more prepared to slowly step into the world of digital humanities now that we understood some of what it could do for us as humanities scholars and educators. Next, Cordell suggests you integrate when possible. We went on to talk about poetic form, which brought us to the concept of deformance. After reading a brief piece on deformance (with the intention of keeping the reading light, my professor wrote up her own note sheet on it for us), we actually used a tool to create a bot that randomly provided a new apology in the form of the poem “This Is Just To Say” by William Carlos Williams. We, a class full of English students, learned some basic coding and did well! Other English staff thought my professor was wild for trying to teach us code, but it was incredibly successful. Click here if you want to see the bot and generate some funny parody poems! There is also a Twitter bot that occasionally does the same exact thing, also worth a gander! So in studying poetic form, we also learned how to play with it using technology.

Cordell’s third rule expresses how critical scaffolding is. To put it plainly, you must build up one skill on top of another in order to tackle larger projects. This is the point where we started working on our Victorian Queer Archive with students at Dickinson College. We were each assigned a queer author from the Victorian era, then we had to take one of their poems and do some research. We had to get the details on their publication, find original images, and more. Then we took that information and pieced them into this large archive, making this our first digital humanities project that we all worked on. At this point, we had a solid foundation of DH knowledge and being that there were a large number of us, it was not intimidating to participate in this project.

Cordell’s fourth and final suggestion is to think locally. He doesn’t quite mean “support your local businesses” (although we should also be doing that), but more so that we should be more focused on digital humanities projects/work that is in the interest of the students and their university rather than impressing people who aren’t of significance to the students. Think more about how this work can promote their own work in the DH direction, and how that could meet the goals of the college in question. In our particular instance, we used the digital humanities as a tool for echoing the importance of diversity and equity. This was something that was very important to our campus and student organizations, so it was very encouraging to discuss issues of race and queerness through digital literary projects.

All in all, I really liked Ryan Cordell’s points because to me, they’re just solid suggestions. These points were followed by my professor in undergrad, and now I am studying DH with you all here at The Graduate Center. This course paved a path for me, a truly lost humanities student, that I did not know existed. I’m sure there are many of us who have told our friends and family that we are studying digital humanities, only to be met with a “Nice! What is that?” Given that it is still considered an emerging field, that is okay, but we need to seize the opportunity to expose more undergraduates to this potential since I agree that it is the future of humanities departments. We need to spread this fire.

In determining what exactly constitutes a network analysis, Miriam Posner’s website directed me to an older version of Scott Weingart’s website, with his introduction to networks available here. (Weingart received the 2011 Paul Fortier Prize, among other recognitions at the top of our field, and/but/so his website — a WordPress! — is a great resource across the board. Highly recommended.) He writes that, “[i]f you’re studying something with networks, odds are you’re doing so because you think the objects of your study are interdependent rather than independent. Representing information as a network implicitly suggests not only that connections matter, but that they are required to understand whatever’s going on.”

This week, I wanted to focus on connections or relationships with a topic that I believe reflects interdependency, so I took this third praxis assignment as an opportunity to explore the concentration of power that has long defined the upper levels of government in the United States. I was curious about how network analysis might make sense of what I view as a relatively closed circuit of people, predominantly white and predominantly men, whose relationships with each other may go back to college — or earlier, in the case of certain recent high-profile hearings — and serve to further consolidate their influence.

To explore this concept in more detail, I aimed to do a network analysis of the current 100 United States Senators to discover connections in their young adult lives through their time as an undergraduates. Many Senators attended one of a few law schools, but I was curious about finding other similarities even earlier in their higher education, whether a shared undergraduate institution or a similar set of experiences as an undergraduate at different institutions. It came to me that I could possibly use the tools for this week to visualize who — if anyone — would have been at the same school at the same time, for example. I ended up creating a dataset of each Senator’s name, college, graduation date, degree type, field of study, and additional information, including fraternity or sorority affiliation and if they served as student body president, were the first in their family to graduate college, graduated as a member of Phi Beta Kappa, or a few other categories that appeared in academic/professional overviews around the Internet. It’s not that I think being in a fraternity or sorority is an accomplishment (I did have a net positive experience with my own Greek life experience, but I’d hardly call it an achievement), but I wanted to include this element of undergraduate life as a potential link between Senators.

To consolidate all of this information in a Numbers file, I took Wikipedia at its word, and dug for details about several Senators as well. Some biographies described how so-and-so “graduated from [institution] in [year] with a [B.A./B.S./B.B.A.] in [field],” which was optimal for my purpose, but many biographies instead included partial information that left rows blank in my spreadsheet: reading that a Senator “holds a B.A. from [institution” or “graduated summa cum laude from [institution] in [year]” would send me to other sources, including alumni magazine profiles and commencement speaker information. For a few Senators, even this secondary step did not turn up the details I wanted, particularly with comparatively older Senators who graduated from college in the 1960s and 1970s whose fields of study remained hidden deeper than I could dig for this week.

At first, I included a column for “additional information” all together, separated with semicolons, but this catch-all approach had its limits. For one obvious thing, this approach didn’t allow me to sort  the data by any one consideration (if I wanted to see the Senators who graduated cum laude or higher, for example) that would illuminate a potential overlap in experience. To try and fix this, I broke the column up into a few not-catchy additional columns: “PBK?”, “other academic Greek,” “Greek social,” “first-gen,” “leadership,” and “athletics.” The column for “leadership” then became “class president?” and “valedictorian?”, making the table harder to navigate than before when these considerations were in one place. I soon realized another limit of this new approach: that some of this information might not be as accurate as I wanted it to be. Besides deliberately untrue information included in a biography, if a Senator graduated as their college or university’s valedictorian, it might not have made it into their Wikipedia biography (which anyone can edit) because so much else later in life eclipsed that one title or any other reason, or maybe the college or university did not even identify a valedictorian in the first place but the person still graduated at the top of their class.

The final consideration, which is fundamental in hindsight, is that I assumed that shared titles or organizations would lead to shared experiences that would lend themselves to network analysis. To be fair, there might be something about a group of people in the same Greek-letter organization at the same institution at the same time, or even across time, as the fascinating social experience of Homecoming illustrates, that ties them together, but it is hard to generalize from this feeling to a network. Do Doug Jones (who graduated from the University of Alabama, Class of 1976, with his B.S. in Political Science) and Michael Bennet (Wesleyan University, 1987, B.A., History) feel any kinship at all for their shared status as brothers in Beta Theta Pi, for example, and would it be fair to say that this kinship has affected their politics in any way, which is what my initial interest in this entire dataset seems to suggest? Or do John Cornyn (Trinity University, 1973) and Pat Roberts (Kansas State University, 1958) have some unspoken bond thanks to their B.A. in journalism?

I originally began pulling together this information to create a network analysis of what United States Senators might have shared early in their adult lives — institutions, honors, social organizations — but encountered fundamental problems with this very curiosity, not to mention the steep learning curve to putting the data into place. In working to express the data through Palladio and Gephi, I found that the platforms did not respond to my organizational approach or my questions, giving me a string of error messages and forcing me to return to the data over and over again to fill it in and rework its structure. I am going to try a few more rounds of editing my spreadsheet and exporting it to a csv file over the next few days, but also have considered that a different method entirely might give more insight.

Much of this process of compiling and adjusting the dataset was the challenge of figuring out how to organize the data as little as possible while aiming for accuracy and consistency. To paraphrase Micki Kaufman’s answer to questions last week about her method for working with a large quantity of documents processed with optical character recognition, I wanted to remember that we look for patterns where the data is cleanest, so the moment that you begin cleaning data, you begin influencing it, even subconsciously. This lesson only felt more important the more I messed with this data, even on a small, limited scale, and realized how much of my own interests and decisions affected any potential takeaways.

To return to Weingart’s post about network analysis, “Relationships (presumably) exist. Friendships, similarities, web links, authorships, and wires all fall into this category. Network analysis generally deals with one or a small handful of types of relationships, and then a multitude of examples of that type.” He uses the examples of authorship and collaboration as types or ways to describe relationships between types of nodes and introduces the distinction between asymmetric relationships, or directed edges that can be visualized with an arrow flowing one way, and symmetric relationships, or undirected edges that can be visualized with a line between nodes implying that the flow of the relationship is the same in both directions. For my purposes, I was only interested in finding the potential directed edges, the undergraduate-level features that current Senators have in common that could possibly indicate shared experiences and start the process of understanding various intangible “benefits of the doubt” that seem to hold real weight in political situations. Moving forward with trying to explore the concentrations of political power in the federal government, I think it might make sense to incorporate a greater sense of asymmetric relationships (who has clerked for who, for example, rather than who were classmates on the same level or who shared an experience “equally”), or else to work with nodes that offer less room for interpretation on my end.

The Digital Fellows’ “Format Your Dissertation Like a Pro: Writing with Markdown” workshop taught by Rafael Davis Portela introduced Markdown, a markup language (a way to annotate and present text in a document using tags, with HTML being one example) meant for writing dissertations, web content, notes, or really any form of text, that uses relatively limited formatting options so that the focus remains on the actual writing itself.

Basically, you’re writing your document in a plain text editor with the ability to add minimal formatting to it using syntax. There are benefits such as being able to move paragraphs around without worrying about messing up the formatting, writing lists without being concerned about the specific order, and including notes to yourself that don’t appear in the final version. The file is a plain text file so you don’t have to worry about compatibility issues and you can easily convert it to other formats when you’re finished such as a doc, pdf or html file, or even into presentation slides.

Pre-Markdown

You will need to download some tools to convert the Markdown file when you’re done and if you prefer, you can create the Markdown file itself in Terminal, the info for which is all found on Rafael’s Github page for the class. Just to try using Markdown, you can use Visual Studio Code like we did in the workshop or another text editor, and save the file as a .md file. Or try Markdown in a browser without installing anything at dillinger.io.

Markdown syntax

You’re pretty much just writing your text in the text editor but when you want some basic ways of structuring or styling your text, such as for headlines, lists or links, here’s some of the syntax you can use. (I wasn’t sure of the best way to present the syntax and the result in WordPress so I used screenshots.)

Headings: For the equivalent of headings in Word or Google Docs, you use the pound sign (or hashtag). I’m not sure the best way to present the syntax

Paragraphs and line breaks

• To start a new paragraph, leave a blank line after the previous paragraph
• For a line break, add a “\” at the end of the line

Text formatting

Block quotes: Add a “>” in front of the text to make a block quote. (Correction: The result is missing the words “in Markdown.”)

Lists

• Use *, + or – for unordered lists and add four spaces before the mark for second-level bullets.
• Write “1.” or “1)” for ordered lists – you can use 1 or any number, and repeatedly, as Markdown will automatically convert it to an ordered list. (Correction: The “A.” in the syntax should be a “1.” or any number. Doesn’t matter as Markdown will order it for you.)

Comments: A comment doesn’t appear in the final version. It can be a reminder or note to yourself. Or it can be text you’re not sure you want to include in the document but in case you change your mind, you simply remove the syntax, which is:

<!– A comment surrounded on both sides by the syntax –>

Links: Use brackets for the text you’re linking and parentheses for the URL with no space in between.

The rest

• Go to Rafael’s Github page for the workshop for more on inserting images, footnotes and converting Markdown files to pdf/doc/html files, eBooks, presentations, etc. And on automatic citation.
• Additional syntax info here and here.
• To get an idea of what Markdown could look like, here’s a screenshot of the post I wrote before putting it in WordPress:
• Before the workshop, I would use the TextEdit application in plain text format to write since I could produce a clean text that can be formatted in other software. However, I did wish I could, for example, differentiate heading text from paragraphs or make links or tables. Markdown looks like a good solution for keeping the focus on the text without, say, being distracted by multiple fonts and colors or inconsistent line spacing, while still letting you do some basic formatting to distinguish different parts of the text. I still have to become comfortable with converting the Markdown document to other formats – and figure out if there was an efficient way to “convert” it to a WordPress post – but in the meantime it seems like an efficient way to write text without many of the concerns that come with word processing software.