Humanities scholars have analyzed sources and moments in history in various ways. Many conduct research through the traditional means of reading and analyzing sources and even delve into the digital world through different tools, including maps, graphs, and text mining. However, there is a reasonably recent medium that humanities scholars have begun to use to analyze and research: podcasts. Podcasts have become a viral medium for people to consume different topics. According to the Pew Research Center, “Roughly half of U.S. adults say they have listened to a podcast in the past year . . . including one-in-five who report listening to podcasts at least a few times a week. Among adults under 30, about a third listen to podcasts with such frequency.” (Pew Research Center, Podcasts as a Source of News and Information, 2) With it being a popular medium for people to use, historians have begun to use podcasts to exhibit their research.

There are many different reasons why historians have stepped their toes into the Podcast medium. One reason is that it vastly differs from other modes of research and analysis. Liz Covart, in her article History Podcasts: An Overview of the Field (2022), states, “Podcasts serve as the perfect medium for our mobile, digital age. They are downloadable and streamable audio files that you can listen to whenever and wherever you want. They provide convenience.” (Covart, History Podcasts: An Overview of the Field, 221) Podcasts are a way for historians to reach a wider audience than they would have with traditional means. They are also a form for historians to connect with other people. Covart explains, “When riders and drivers listen through the sound systems in their cars, they invite podcast hosts to keep them company on their trip. Given the human need for stories and connection, podcasts humanize stories in a powerful way unique to the medium. The intimacy, oral storytelling, and this power to humanize make podcasts an effective tool for historians to convey the past.” (Covart, 221) Podcasts connect people to the hosts and the stories they tell, and historians have turned to this medium in the hopes of the same connections to history.

While podcasts are vastly different from traditional and even some other digital modes of research and exhibiting, they share some similarities, too. One of the things that I noticed between podcasts and other digital tools is that they are all new ways to analyze and present data. When someone listens to a history podcast, they go into the host’s mind and connect to the story that the host wants to tell. This is not different from other digital tools. For example, when a historian makes an exhibit using Omeka and text mining tools like Voyant, they want the audience to connect with what they see rather than listen like with a podcast. All of these digital tools together change the way that research is done. In his keynote at the Sound Education conference, Dan Cohen states, “We can take some helpful clues about this situation from other new media formats that have flourished on the web over the past quarter-century. For instance, since the advent of the web, and its ability to serve a wide array of text, in difference lengths, sizes, and contexts, we have seen the birth of new genres that challenge traditional writing and break out of the constraints of print publication.” (Cohen, Sound Education) Podcasts and other digital tools are making waves in how historians analyze and research different material.

Crowdsourcing is how historians in the Digital Humanities field can create projects that create new ways of analyzing material. Collaborators to The Collective Wisdom Handbook state, “Crowdsourcing can open up new possibilities for research that could not have existed otherwise, fundamentally changing authoritative practices, definitions, and classifications.” (The Collective Wisdom 2) Along with changing the way one analyzes data, there is a component of collaboration. The collaborators on The Collective Wisdom Handbook explain, “At its best, crowdsourcing increases public access to cultural heritage collections while inviting the public to engage deeply with these materials.” (The Collective Wisdom 3) The publics’ collaboration with these projects is one of the main components crowdsourcing. These projects come about in many different ways and what tasks the public undertakes. One of the best examples of these different tasks is transcription.

Transcription in crowdsourcing projects puts the task directly into the public’s hands. The project creators upload documents and ask volunteers to help transcribe them for an online archive. One of the first projects to bring this idea forward was Transcribe Bentham. The Transcribe Bentham project uses documents created by philosopher Jeremy Bentham “with the intention of engaging the public with Bentham’s thought and works, creating a searchable digital repository of the collection, and quickening the pace of transcription and publication by recruiting unpaid online volunteers to assist in transcribing the remaining manuscripts.” (Causer, Tonra, and Wallace, Transcription maximized, 120) The project aims to have volunteers transcribe the documents that creators upload in a collaborative effort to build an archive of Bentham’s documents.

Another project that uses transcription in a crowdsourcing project is By the People. By the People is a project created by the Library of Congress, designed similarly to Transcribe Bentham, that encourages volunteers to help transcribe documents uploaded to create an archive. The documents in this project vary widely “into ‘Campaigns’ and presented to volunteers along with transcription conventions, a discussion platform, and explanatory material to help folks learn a bit about the subjects of the documents.” (Hyning and Jones, Data’s Destinations, 9) This learning of the material is how these projects keep participants engaged in transcribing the documents. With Transcribe Bentham, the creators discovered that volunteers “were motivated by a sense of contributing to the greater good by contributing to the production of the Collected Works and making available Bentham’s writings to others, whereas some even found transcribing fun.” (Causer, Tonra, and Wallace, Transcription maximized, 127) To keep volunteers excited and engaged to transcribing the material, they need to feel like they play a significant factor in the process and it has to be fun.

The digital tools Voyant, Kepler.gl, and Palladio are handy tools for historians and researchers to use when analyzing digital material. Researchers use these tools to look at relationships within, find patterns and trends, and convey meaning and understanding from the research material. Researchers can do this because of the visualizations these three tools create making finding specific patterns easier. While the goals for why researchers use these tools are similar, the tools themselves are very different in their executions.

Voyant is a text-mining tool researchers use to convey meaning from a data set. It includes five tools researchers can use together to derive meaning from selected texts. The Cirrus tool is a word cloud that shows words that appear most frequently. Like Cirrus, the Trends tool is a graph visualization that shows the frequency of a specific word across the document. The Reader tool shows the document itself where users can select a specific word to find in the document. The Summary tool shows an overview of the specific word in terms of the document. Finally, the Contexts tool shows each time the document uses the word. These tools, used together, can find meaning in how words can tell a lot about a period, place, people, and culture.

Kepler.gl is a digital mapping tool researchers can use to discover patterns and trends across space. Even a simple point map can derive meaning from the data they are researching by showing the different relationships between those points. However, that is only part of what Kepler.gl can do with a map and data. Researchers can change the map to look at the data in different ways. Instead of points, one can change it to a cluster map to see the quantity of the data and compare them. A heat map can do the same thing, showing the density of the data. Other items can also be added, including a timeline showing how that data changed over time. All of this can give researchers more information than just traditional research.

Similar to Kepler.gl, Palladio can also make use of maps in a digital medium. However, the tool’s main component is using network graphs to show the relationships between different data. These graphs, overlaid on a map, can show the connections across space. Nonetheless, some of the most helpful information comes from looking at the network graphs themselves and limiting which data is present. Limiting the data can show different connections that otherwise go unseen in traditional research or are harder to see. For example, when I worked with Palladio, I limited the information to only look at topics discussed between male and female interviewees. While both groups shared similar topics, some were limited to a particular group. These topics tell me a lot about the subjects themselves and the culture at the time of their interviews.

While these three tools have different modes of executions, researchers should look at them in a variety of ways. Researchers can use these tools together to find meaning, patterns, and trends with the material and data they choose to research. While Palladio can use maps with the network graphs, it only shows a certain amount of information. A researcher should also include using Kepler.gl to get the information one can get from using a map that otherwise Palladio lacks. At the same time, Palladio also relies heavily on using words especially in the network graphs. Voyant makes the perfect complement to that program to delve deeper into using those words in the data.

Network graphs are helpful tools that researchers can use to convey meaning between information connections. According to authors Ruth Ahnert, Sebastian E. Ahnert, Catherine Nicole Coleman, and Scott B. Weingart, in their book The Network Turn: Changing Perspectives in the Humanities (2020), “The conventional network graph of node and edge (points connected by lines) makes it possible to convey a tremendous amount of information all at once, in one view. Networks express an internal logic of relationships between entities that is inherently intuitive.” (The Network Turn 57). The connections shown through these graphs can give researchers information about the material they are studying.

Many projects have used network graphs to help better understand, gain meaning, and answer questions about the material they are graphing. One such project includes Viral Texts. In this project, researchers, including Ryan Cordell, examined nineteenth-century American newspapers and their connections. What they found with these newspapers recirculated many articles, making the researchers wonder why. They found the answers to many of the different questions posed by the different types of recirculated texts related to the culture during the Antebellum period in the United States. Mapping the Republic of Letters is another project that used network graphs similar to Viral Texts. Like in the Viral Texts project, the Mapping the Republic of Letters project used network graphs overlaid on a map to show connections between letters from historical figures. From these connections and the use of a map, researchers discovered the importance of travel and that the letters were a way “of communicating ideas and shaping opinion, and also as a process of intellectual self-definition.” (“Historical Research in a Digital Age” 407-9). Another project that uses network graphs is Linked Jazz. Linked Jazz, like the other two projects, uses these graphs to show connections with the researchers’ material, in this case different jazz musicians. What the researchers discovered was “data about concert performances and recording dates gives . . . rich information about not just collaborations between musicians, but also about time and place, musical works, songs, and songwriters, and record labels and releases . . . .” (Hwang, Levay, and Provo “Contributing to Linked Jazz” 2015). Network graphs can show the connections between material and convey meaning about a time, place, people, or even a genre of music.

I discovered much information and meaning from using these tools when starting with Palladio and network graphs. A simple network graph overlaid on a map shows the connections across the space. It gave me a scope of how and where these connections happened.

I then started playing around with the network graphs and limiting certain information to see what I could find. One such graph shows the topics that male and female interviewees discussed. While they shared many of the topics, some were limited to either only male or only female interviewees. Using that information, I gathered that those topics were important to that gender specifically for a reason. For example, only female interviewees discussed elections which made me think about how they did not get the right to vote like their male counterparts which is probably why that topic is important to them.

Another graph shows the topics categorized based on their job. Again the topic of elections stood out to me since it was limited to only people who worked in the house. It led me to think that perhaps it was because they heard more gossip and politics from their masters while working in the house.