I haven’t worked (in this capacity) in a group for a while. I was more than happy with the group I had. They all worked hard on each of the tasks assigned. We hit the milestones, with the leadership of Madelyn. Liz, Damon and Luis nailed their presentations, it worked out well. Introspectively, with this project specifically, I realized that working with a team you can accomplish some pretty mammoth tasks. At first, I didn’t seem to grasp how we would manage such large tasks but after we did I thought; “What if we would have analyzed the whole series seasons 1 through 10?” The results would have changed (maybe) and a whole other analysis would exist. I hope I gained some friends through this Friends project.
The study below focused on the dialogue from Season 1 of Friends. Not my choice, but hey we went with it. Not a problem. I actually enjoyed it. Using the scripts and converting them to a usable format, I was able to uncover patterns of speech frequently used by the characters Chandler and Monica. While these results aren’t ground breaking, they do reveal some similarities between the two, they both like to use the word ‘oh’ a lot. Followed by the words ‘my god’ also, a lot. Monica’s most referenced friend is Rachel and Chandler’s is Joey. It was important to highlight the use of some words and what were their social interactions? What were the relationships like? How did they talk to each other? Here is the final project link:
The sitcom Friends aired on NBC from September 22, 1994-May 6, 2004, lasting ten seasons (2023, Friends Central). The cast included Jennifer Aniston (Rachel), Courteney Cox (Monica), Lisa Kudrow (Phoebe), Matt LeBlanc (Joey), Matthew Perry (Chandler) and David Schwimmer (Ross). Our group project comprised five members: Madelyn, Luis, Damon, myself and Liz. We wanted to know more about the social network between friends and more about the dialogue in the individual scripts, specifically Season 1.
Through text analysis, its possible to see the level of vocabulary of the show’s writers. Do they use complex sentence structure? Do they use unique words and so on. There are multiple pieces of software that will analyze text. Ben Blatt, Slate contributor and author of Nabokov’s Favorite Word Is Mauve says that, “writers have a singular voice they will not or cannot change” (2017, Blatt).
Who interacted with who? Who interacted with that person more frequently than others? What text appeared more regularly? To better answer those questions from the scripted dialogue between the six characters of Friends, finding a corpus (data) compatible with the text analysis tool Antconc was necessary. Antconc is a free downloadable software that analyzes corpora for text analysis. The software sorts through data, and through different settings within the software, the user can help reveal patterns in the canon of text. Voyant Tools is another analysis tool used in our analysis. Like Antconc, Voyant offers a free download but is visually more appealing than Antconc.
In this project, converting the corpus to .txt was the first order of business. Between the 5 group members, we divided up the Screenplay scripts here:
Madelyn would conduct the network analysis of the relationships between characters. The rest of the group would analyze each character’s text to decipher any patterns within. I was assigned Chandler and Monica.
Who is Monica?
Courtney Cox portrays Monica Geller, the younger sister of Ross Geller, in Friends. She is known as the “mother-hen” of the group (2023, Friends Central). Monica is a Sous-chef and is best friends with Rachel Greene. Her apartment is across from Joey and Chandler’s. She is a very tidy person, almost to the point of obsessive-compulsive.
Who is Chandler?
Chandler Bing is one of three male characters on the show. He uses wit and sarcasm as defense mechanisms; therefore, his dialogue has to be complex, right? So I wanted to find out what words were used most frequently by Chandler.
Using Google Docs and a series of spreadsheet data entries, I was ready to start using the tools for better analysis. Initially, I wanted to get an overview of the texts. It was Voyant that offered me the best option—the Word Cloud.
Monica’s word cloud using 95 of the most used terms in her dialogue from Season 1.
Chandler’s word cloud using 95 of the most used terms in her dialogue from Season 1.
The word ‘oh’ really shows through the word clouds. It’s where I started. It was a similarity. Maybe other patterns would emerge around the word ‘oh’?
Chandler’s most used word-‘oh’
Who interacts with Monica and Chandler frequently?
Antconc, while not as visually appealing as Voyant, has its own superpowers by revealing patterns between the character’s dialogue, like what character name Chandler and Monica mention the most. We don’t see the future romance between the two characters yet.
Antconc displays Monica’s mentions of other characters on the show. It seems almost to easy to know that Rachel, Monica’s best friend is the most frequently used in her dialogue with a Frequency of 41 and NormRange of .625.
Antconc makes it is accessible to see the frequency with which Chandler mentions other characters. For example, in the first episode, Joey tells Rachel he and Chandler live together across from Monica. This fact possibly explains one instance why Chandler references Joey most frequently with the highest Frequency of 21 and NormRange of 0.583.
‘oh God’ is used a lot
The Voyant illustration of Chandler’s most related word to ‘oh’ above led to an exploration through Antconc of both corporas and the findings using both pieces of software agree in their findings.
The Voyant illustration of Chandler’s most related word to ‘oh’ above led to an exploration through Antconc and the findings using both pieces of software agree with each other.
Collocating-words, which are words statistically likely to appear together (2022, Anthony) in Antconc. The word “God” is located near our most popular word “oh” for Chandler.
Using the KWIC readings in the dialogue in Antconc showed that Monica Geller uses the “oh” word in Season 1 next to the two words “my” and “God” frequently. There is one gosh in there. Is that ad lib dialogue or are we again presented with the writers including the phrase “oh my God” with intent?
Summary
*spoiler alert*
If you followed the show and have seen the entire series, you know that Chandler Bing and Monica Geller marry and fall in love. However, you wouldn’t say that by the text from the dialogue of Season 1. Monica references her best friend Rachel most, while Chandler greatly references Joey. ‘Oh’ is used more frequently than any other word. Both characters use the word next to or within three words before ‘God.’ Some form of ‘oh God’ or ‘oh my God’ is used frequently.
Digital humanities and its ability to reveal complex relationships help create a science of behavior in a way that traditional methods never achieved due to a lack of technological innovation. The digital path uses the medium for research, cataloging, preservation and other insightful applications. It analyzes behavior and presents it through visuals and sometimes through “plug-ins.” It is a search for answers utilizing the digital platform by either those of the DH community or curious interlopers seeking knowledge about the past. Think ancestry dot com. While still in its early years of development and study, digital humanities’ definition is subjective. Digital humanities, as a study, is a personal reflection of sorts. However, the field’s description blurs across the knowledge spectrum. Www.whatisdigitalhumanities.com, presented early in the course, I found this to be especially intriguing because everyone defines digital humanities differently. According to the site, as of January 2015, the database contains 817 rows and randomly selects a quote each time the page is loaded. (2023, Heppler). Professor and chair of Digital Studies at Davidson College, Mark Sample, says in his blog, “The heart of the digital humanities is not the production of knowledge; it’s the reproduction of knowledge” (2023, Sample). Digital humanities have a place for study, if not the most important; it is a survey of who we are and where we hail.
Digital humanities can be a personal or group reflection of the past. However, the field’s definition blurs across knowledge spectrums. The results rendered may or not fascinate those outside the field of study resulting in a lack of support. It is not until those skeptics make a personal connection through historical analysis that they realize the study’s importance. In the case of The Slave Voyages – Trans-Atlantic and Intra-American slave trade databases (2021, Eltis), Those involved may or may not want to know the reality that the slave trade is a part of their personal story. The website offers interaction with documents that may never have been available otherwise without the cataloging and preservation of manuscripts detailing those involved in the abhorrent trade. Had this site not presented these names to the current conscious, they probably would have sat elsewhere, hidden from discovery. Preserving these documents in the digital space adds an advantage for those that do not have direct access to them. It allows anyone interested and with an internet connection to study and access from anywhere.
The site uses actual names with details of en slavers.
Digital Humanists, in some respect, act similarly to museum curators and collectors of antiquities—archiving digital items into online inventory to tell a story or express a subject theme. However, compiling all things is an impossibility due to copyright laws.
Common themes are archived and compiled in one place using metadata and classification identifiers digitally. These spaces allow interaction with items not centrally located together but in a virtual space. According to the About Us page, “The Dublin Core Metadata, or “DCMI” is an organization supporting innovation in metadata design and best practices across the metadata ecology (2022, DCMI). For those that compile this data, software like Omeka is beneficial. It is another example where the user is somewhere else but would love to visit an exhibit outside of the physical location. Proximity issues are not a hindrance for those collaborating. The museum is online. An online resource such as Omeka allows collaborations to flourish. The Preserve The Baltimore Uprising Archive Project, utilizes the features of Omeka to bring the exhibit viewer closer to the protest and unrest in Baltimore following the untimely deaths of black citizens at the hands of police. The site allows users to upload relevant photos and content with metadata to archive the unrest in online collections.
Themes of humanity and the relationships created by those involved in history can be delineated using network analysis and social networks. I found demonstrating the degrees of the relationships to be especially interesting. What I found interesting is the ability to read into the relationships better with what corpus you have. It is almost like reading between the lines to see what the participants meant. For example, those networks of relationships mapped in Voltaire’s Correspondence Network show his letter correspondence to people of different importance during that time. It is beneficial in finding patterns because it breaks down the correspondences into different attributes like gender, nationality and destination. For example, you can see that the Ferney to Paris connection was essential to the Voltaire network by the size of the circle or node. The degree of centrality is a term used in network analysis. It is the number of connections to the node or how vital each node is in the network (2011, Weingart).
Ferney Shows a high degree of centrality
The majority of Voltaire’s correspondences were dispatched mainly to male recipients in France particularly Ferney.
Weingart says that analysis should not apply to every project citing danger in methodology appropriation (2011, Weingart). Assumptions could be made after analyzing the network and could be wrong or distorted is how I interpret it. Metadata is an essential aspect of a network. These seem to be the building blocks of efficient analysis. Without these crucial details, the complex nature of human relationships will not display correctly. Not only does the data need to be in shape, but the analytical part must also appear suitable, or the attempt at analysis is in vain.
Almost Jedi-like, as is the analysis of relationships in the Evelina Gabasova blog about The Star Wars social network. It is possible to take the metadata within the scripts for the movies and reveal the relationships they create. For example, the relationship between the so-called ‘terrorist groups’ residing in Boston and the characters in the Star Wars saga is the metadata, once compiled and calculated, tells about the stuff between the lines.
Gabisova’s overview graphic of the Star Wars Network from her blog
Gabasova demonstrates, using Cytoscape (as seen above), that R2-D2 was a large part of the story arc of Star Wars. Unfortunately, without further explanation, this general overview tells us nothing unless the attributes and edges are detailed. In her blog, Gabasova explains the interactions and scene presence of each of the main characters of the story line. As a Star Wars fan, I never gave much thought to which character was present in more scenes than others until seeing her research. Cytoscape is another arrow in the quiver of digital humanists in discovering and uncovering complex relationships in social networks.
Digital humanities, at its core, embodies the human experience through art, life moments, events and relationships. Revealing emotional and complex relationships between people in complex networks through different methodologies is now a reality. Illustrating the intricate details of those relationships as technology progresses will become more interactive and complex. Digital humanists can only uncover these connections with good data. Mining that data is time-consuming and often complex. Weingart states, “Nothing worth discovering has ever been found in safe waters” (2011, Weingart). Reflecting on his quote, he talks about going deeper to discover more than the obvious. Those topics or hypotheses’ are more complicated than others in finding answers and are worth finding. As technologies like VR and AI start to develop, I think the possibilities in this field are endless. Not that there is any shortage of topics to be researched.
The underlying stories are fascinating to realize once they are uncovered by someone regards to digital humanities. Relationships and digital humanities’ ability to demonstrate them in a manner that is visually appealing and understandable to non-specialists are key takeaways of this course.
References
Eltis, D. (2021). Explore the origins and forced relocations of enslaved Africans across the Atlantic World. A Brief Overview of the Trans-Atlantic Slave Trade,’ Slave Voyages: The Trans-Atlantic Slave Trade Database. Retrieved May 7, 2023, from https://www.slavevoyages.org/voyage/about.
Gabasova, E. (2015, December 15). The Star Wars Social Network. Evelina Gabasova’s Blog. Retrieved May 7, 2023, from http://evelinag.com/blog/2015/12-15-star-wars-social-network/index.html#.VnAhsTZZG6A.
Heppler, J. A. (n.d.). What is Digital Humanities. Retrieved May 7, 2023, from https://whatisdigitalhumanities.com/.
Preserve the Baltimore Uprising Archive Project. Preserve The Baltimore Uprising Archive Project. (n.d.). Retrieved May 7, 2023, from https://baltimoreuprising2015.org/.
Sample, M. (2011, May 25). The digital humanities is not about building, it’s about sharing [web log]. Retrieved May 6, 2023, from https://samplereality.com/blog/.
Weingart, S. (2011, December 14). Demystifying Networks. Retrieved May 7, 2023, from https://lms.hypothes.is/app/basic-lti-launch.
