Social Media Strategy

Carpatho-Rusyn Villages in Slovakia

For my project I am going to be mapping the various villages lived in by the Carpatho-Rusyn people in what is today the country of Slovakia using the kepler.gl application. The Carpatho-Rusyn are an east Slavic ethnic group that reside in the Carpathian Mountains in what is today Poland, Slovakia, and Ukraine. In the late 19th and early 20th centuries, thousands of Carpatho-Rusyns immigrated to the United States largely settling in the states of Pennsylvania, Ohio, New Jersey, and New York.

Through my own genealogical research, I became interested in this ethnic group that makes up a quarter of my ancestry. This led me to want to discover more about the villages where the Carpatho-Rusyns came from. I decided the best way to analyze the different villages was to create various maps using kepler.gl.

There is estimated to be over 600 villages where the Carpatho-Rusyns have historically lived scattered across what is today Poland, Slovakia, and Ukraine. For my project, I decided to focus on the villages in Slovakia, as that is where my ancestors are from. Even in Slovakia there are over 300 villages where the Carpatho-Rusyns have historically lived. I decided to narrow my data set down to around 100 villages located across the Presov of Slovakia in order to make the project more manageable.

One of my goals once I finish my project is to be able to share my results and the data I have collected with others that may be interested in my work. The best way to share my project with a larger audience is through social media. Through social media, I can allow a diverse group of people to view more work, offer feedback and suggestions, and allow them to add and contribute to the project. In order to effectively use social media to promote my project, it is important to address who the audience I am trying to reach is, what platforms I will use to reach my audience, what message I will use to appeal to my audience, and how I will measure the success of using social media for my project.

Audience: The audiences that I want to reach and get interested in my project are: people that are interested in Carpatho-Rusyn genealogy/genealogical research, people that are interested in the history of Slovakia, and people that are interested in learning about different ethnic groups that immigrated to the United States. There can be some over lap between these different group as they are interested in similar topics. I feel that this audience would be interested in learning more about Carpatho-Rusyn villages in Slovakia and would be able to provide me with helpful feedback and suggestions to improve my project. In addition, I feel that this audience would also be interested in contributing to my project to help me expand it in the future.

Platforms: The platforms I will use to promote my project are Facebook and my personal blog. On Facebook I am a member of several groups that deal with Carpatho-Rusyn history, culture, heritage, ancestry, and genealogy/genealogical research. By making posts in these different groups about my project, I will be reaching a diverse audience around the world that all share a common interest. The members of these groups include everyone from people with a casual interest in family history, up to professional historians with an extensive knowledge of the Carpatho-Rusyn people. I will also use my personal blog to create a post where I provide a detailed description of my project and a link to the data set that I used for anyone to look at.

Messages: In the various Facebook groups, I will create a post giving a short description of my project that will discuss how I used a mapping application to create different maps looking at various characteristics of Carpatho-Rusyn villages in Slovakia. I would also include that I only looked at around 100 villages and would like to expand my project to include the remaining villages in Slovakia, and also add the villages in Poland and Ukraine. At the end of the post I would include a link to a post on my personal blog and encourage people to follow the link to read more about the project and to add to and contribute to my data set.

On my personal blog, I would create a post where I described in detail the steps I took to complete the project. I would include how I created my data set, where the information came from, and what information I included. I would then describe the different maps I created in kepler.gl and what I learned by creating the different maps. I would end the post by explaining how people can help contribute and add to the project. I will include how they can open the data set I created and add more villages to it. In addition, I would put my personal email address for people to contact me if they had any suggestions for categories I could add to my data or if there were any errors I made that I need to correct. At the bottom of the post I would include a link to the data set that I would unlock so that others can add to and edit.

Measures: I would consider my social media strategy to be a success if in the first month I could get between ten and twenty people to either add more villages to my data set, suggest new categories to add to the data set, or point out errors I made in the data. I think having between ten and twenty people engage with my project in a month would indicate that my project was of interest to others and they saw the value in my work.

 

 

 

What Can You Do with Crowdsourced Digitization?

Today, crowdsourcing has become a major way for creators of digital humanities projects to both receive help with their projects, while also engaging and allowing members of the general public to become involved in these projects. Crowdsourcing involves bringing in members of the public, who lack a vested interest in the project, to perform certain tasks necessary to complete the project. By engaging these volunteers and sparking their interest, they become invaluable in contributing to the success of a digital humanities project.

There are a variety of different tasks that can be completed by members of the public that help to enhance a project. The tasks given to volunteers should be ones that are fairly simple, require no prior knowledge or experience, can be explained in a simple manner, keep the contributors interest, are interactive, and allow people to see the results of their work. Since contributors are volunteers, the tasks given to them should be ones that be easily picked up and anyone can understand with a simple explanation. These task must also be ones that will attract contributors to the project and keep them interested in contributing. The most common tasks given to contributors are transcription and correction. Transcription involves typing out and creating an accurate copy of the text contained in a digitized page of a manuscript. Correction involves editing and making changes to a transcript already created. Both of these tasks can be completed by individuals with very little training or prior experience.

In trying to attract contributors to a particular project it is important to keep in mind the type of tasks that will attract contributors to the project. Most individuals will contribute to a project for a variety of different reason. These reasons include: that the project falls within their particular area of interest, they are made to feel like they are contributing to something greater than themselves, and they feel as if they are giving something back by contributing. The developers of the project need to ensure that they clearly communicate with contributors what tasks they will be completing, why those tasks are important, and what is the long term goal of the project/what is the project trying to accomplish. Explaining all of the different elements of the project and recognizing the importance of the work done by contributors, allows those contributors to feel engaged and eager to contribute to the project.

Another important factor for keeping contributors engaged with a project is the kind of interface provided for them to work with. The best way to keep a contributor engaged is to have an interface that is simple to use and that does not require a large amount of time or effort to learn. In personally contributing to different crowdsourcing projects, I found myself enjoying the ones with the simpler interface more. While I did enjoy contributing to both Trove and the Paper of the War Department, I found that I enjoyed contributing to Trove more because I was able to learn how to use the interface almost immediately, this is compared to the interface for the Papers of the War Department that took me several minutes to figure out all of the different functions in the tool bar. Since contributors are volunteers using their free time to contribute to a project, the interface should be something fairly simple for them to understand and use.

How to Read a Wikipedia Article

Wikipedia, the free internet based encyclopedia, has become an integral part of the way that people learn and obtain new information. When trying to find information on a particular topic, one of the first places people will turn to is Wikipedia. In the almost twenty years since the site was launched in 2001, Wikipedia has received both high praise and frequent criticism. Since this source is so widely used for retrieving information, there are naturally questions about the quality of the information contained on the site.

Articles on Wikipedia are created through a method called crowdsourcing. Crowdsourcing means that articles are created and later edited by many different contributors. Anyone can sign up to become a contributor on Wikipedia, there is no particular background or expertise needed. This has led many people to become skeptical about the accuracy of the information contained on the site. However, Roy Rosenzweig in his article “Can History be Open Source? Wikipedia and the Future of the Past,” tests just how accuracy of the information contained in Wikipedia articles. Rosenzweig finds that when compared to other other encyclopedias such as Encyclopedia Britannica, the information in Wikipedia is just as accurate.

While Rosenzweig found the information in the articles that he read through to be accurate, it is important to ask and keep in mind a series of questions that can be used when reading any particular article on Wikipedia. Each page on Wikipedia has different tabs that the user can click on to learn more about what edits have been made to the page, when the page was created, who are the people that have edited the page, what changes did individuals make to the page, and what were some additions or changes to the page that people disagreed on. In my own personal work I read through the Wikipedia page for digital humanities and used these questions to dig deeper into this page and the information that it contained.

On the top right hand side of every Wikipedia page there is a tab that says view history. When you click on this tab it allows you to view every edit that has been made to page since it was created. If you click on a particular edit, and see what the page looked liked after that edit was made. Looking at the different edits that were made over time allows the changes that were made to the page to be overserved and the user can see how the page has developed and the role that different users have played in the changes to the page. For the page on digital humanities, I was able to find that it was created in January 2006 and observe the how the page went from just a three sentence definition of digital humanities, to a complete overview of the different elements that make up the field.

The history tab also allows the reader to view the statistics about the page. This is located where it says External tools: Page statistics. Under the statistics, there is a list of the top editors for the page by their username. On the list of editors, it tells you how many time a user has edited the page along with the first and most recent time they have made an edit. If you click on a username, it will give you a little bit about that user, such as their background or areas of expertise. For the page on digital humanities, I found that most of the top 10 contributors to the page had either a professional or educational background in the humanities. They had either research positions in digital humanities at universities or had graduate degrees in humanities. These contributors were individuals that had expert and professional knowledge of what goes into and makes up the field of digital humanities from actively working in the field. Looking at the background of the contributors allows the reader to assess whether or not they are knowledgeable about the topic discussed in the page or not.

Finally, it is up to the reader to build on the base knowledge gained from reading a Wikipedia page. Wikipedia is meant to provide a general overview and background knowledge of a topic. In order to fully understand a topic, more reading outside of Wikipedia must be done. Wikipedia itself provides a list of sources where the reader can go to find more information. On the page about digital humanities, Wikipedia provides a bibliography of dozens of scholarly articles about the field of digital humanities along with external links to different digital humanities centers. The reader can use Wikipedia as a starting point to branch out to discover these scholarly sources to gain a more through understanding of a topic.

Comparison of Different Digital Tools

In working with digitized materials, there can be a variety of information contained in any collection of data. There is not one digital tool alone that can fully reveal all of the information contained in a data set. Different digital tools such as text mining/topic modeling, mapping, and networks and visualizations, each reveal a different part of the information contained within one set of data.
In my personal work with digital tools, the data set I used came from the WPA Salve Narratives, which was a collection of interviews of former slaves conducted between the years 1936-1938 as part of the Federal Writer’s Project of the Works Progress Administration. I used this same data set for each of the different tools to conduct a text analysis with Voyant 2.0, mapping with kelper.gl, and a network analysis with Palladio. Using each of these different tools, reveal something different about the text that was unique to that particular tool.
Text Analysis with Voyant 2.0:
Text analysis is used to reveal certain patterns or themes found in the actual text of a set of information, such as the most common words used, the use of certain terms, and patterns of word use. These patterns all related to the use of different words contained in a set of texts. For the WPA Slave Narratives, this involved looking at the text of the actual interviews that was recorded. Voyant used several different tools to reveal the patterns of word use contained in the text. The patterns included how many times certain words were used, what words were unique to certain states, and the location of certain words within the text. All of these tools analyzed the physical text of the WPA Slave Narratives.
Mapping with kepler.gl:
Mapping can be used with information that relates to a specific geographical locations. This is information in a digital source that deals with specific places. This tool allows the different locations to be placed on a map to discover and analyze different relationships and trends within a collection of geographical data. This involves collecting data about different location from a digital source. For the WPA Slaves Narratives, the different locations collected were where the interview was conducted, and where the person interviewed was enslaved. Kepler.gl can be used to create a variety of different types of maps, such as point, cluster, heat, time, category, and network maps, to analyze a collection of geographical data. Each of the different types of maps allow for the discovery and analysis of different relationships and trends within a set of data. For the WPA Slave Narratives, the maps revealed trends in the location of different categories of the interview such as, what type of slave the person interviewed was, where the highest amount of interviews took place, and a comparison between the interview took place and where the person interviewed was enslaved.
Network Analysis with Palladio:
Network analysis is used with information in a digital source about different sets of relationships. These relationships can be between different people, different places, different time periods, or different objects that are in some ways connected to each other. Networks and visualization tools can reveal details of the nature of different relationships between different items. Palladio creates a series of cluster graphs that are based on different networks described in a series of data. This involves collecting certain characteristics contained in a data set. Some of the characteristics I looked at for the WPA Slave Narratives were, who the interviewer was, where the interview took place, where the person interviewed was enslaved, the sex of the person interviewed, what type of slave the person interviewed was, and the age of the person interviewed. Different characteristics were compared against each other to discover and analyze the relationship between them.
Each one of the three different tools was used to reveal something different about a common digital source. Individually, each tool can be used to analyze and evaluate one aspect of a source. When they are used together, they allow for a much more complete picture of the source as a whole. For the WPA Slave Narratives that I used, using all three tools revealed how complex and rich with information this source is for studying the lives of former slaves in the United States.

Network Analysis with Palladio

Palladio is a web based application that allows the user to create a series of cluster graphs that are based on different networks described in a series of data. These networks represent different types of relationships such as those between different people, different places, different time periods, or different objects that are in some ways connected to each other. Through using the application, the user is able to reveal the relationship of these networks and reveal their common traits. When using Palladio to create and analyze different networks contained in a data set, the user is asked to select two different categories that were used in that data set. One category is selected as the source and the other is selected at the target.
For my personal work with Palladio, the data I used came from the WPA Salve Narratives, which was a collection of interviews of former slaves conducted between the years 1936-1938 as part of the Federal Writer’s Project of the Works Progress Administration. I did not look at the whole interview collection for my work, instead I focused specifically on the interviews conducted in the state of Alabama. I used Palladio to construct a series of different graphs that analyzed the different networks contained in the data set. There were two main networks that I looked at in conducting my analysis.
The first network was the relationship between the interviewer and variety of different targets such as the subject of the interview, whether the person they interviewed was male or female, what type of slave the person they interviewed was, and what topics were discussed in the interviews. I was looking to see if who the interviewer was affected any of these topics. I found that it just depended on which interviewer it was. For some interviewers there was a different relationship and for others there was no difference in the relationship.
The second network that I looked at was how a variety of different factors could influence the topics of the interview. The factors I looked at were what type of slave the person interviewed was, the age of the person interviewed, and where the person interviewed was enslaved. I found a similar result with this network as I did with the first one. Some of these factors did influence what the topics discussed in the interviews were. However, there were certain topics that did overlap across the different categories.
Through using Palladio I was able to discover a variety of different networks that are contained within the WPA Slave Narratives for the state of Alabama. These networks revealed some common trends between the different categories contained in the data set, that were revealed by creating cluster graphs in Palladio.

Mapping with kepler.gl

Kepler.gl is a web based application that allows the user to create a variety of different types of maps to analyze a collection of geographical data. Through the different map types that the application offers, the user is able to discover and analyze different relationships and trends within a collection of geographical data.
For my personal work with kepler.gl, the data I used came from the WPA Salve Narratives, which was a collection of interviews of former slaves conducted between the years 1936-1938 as part of the Federal Writer’s Project of the Works Progress Administration. I did not look at the whole interview collection for my work, instead I focused specifically on the interviews conducted in the state of Alabama. In my work with kepler.gl, I created five different types of maps that I used to analyze different relationships found in this series of interviews. All of these maps were created by using the geographical coordinates of where the interviews took place and where the person interviewed was enslaved.
Types of Maps used in kepler.gl:
1. Point Map: This type of map uses dots to represent different data points in a collection of date. On the map I created, dots were used to show where the different interviews took place within the state of Alabama. A higher concentration of dots in a specific area means that a larger portion of interviews took place in that area. For the WPA Slave Narratives, the point map showed that while the interviews were conducted across the entire state of Alabama, a significant amount of the interviews took place on the southwest coast and in west-central Alabama.
2. Heat Map: This type of map can be used to demonstrate the density of data points in specific areas. The map I created showed where in the state of Alabama the highest density of interviews were conducted. The color of the dots on the map corresponded to the density of the amount of interviews conducted. The more yellow a dot appeared, meant that a higher density of interviews was conducted in that area. For the WPA Slave Interviews, the highest density of interviews were conducted in southwest and central Alabama.
3. Time Map: This type of map can be used to show dates and times of different data points. The map I created showcased when the interviews in the state of Alabama were conducted. The user is able to select the time period they want to view and then watch as the points are highlighted based on the time when the interview was conducted. My personal map demonstrated that the interviews in Alabama took place between April and August 1937 with the highest concentration of them taking place between late May and early July. This type of map also allows the route that the interviewers took through Alabama to be followed and see the how they moved through Alabama to conduct the interviews.
4. Category Map: This type of map can be used to see the relationship between two different categories contained in a data set on a map. For the map that I created, I looked at the location of different types of slaves, field vs house, in the state of Alabama. One color dot represented the location of field slaves and the other represented the location of house slaves. This type of map demonstrated that field slaves were now living in rural areas of Alabama, while house slaves were now living in the urban areas and cities of Alabama.
5. Network Map: This type of map can be used to represent two different geographical locations contained in a data set, and to analyze their relationship to each other. For the map that I created I used the location of where the interviews where conducted and the location of where the person interviewed was enslaved. The map created lines between the two locations using different colors for each of the different sets of locations contained in the data set. The map I created for the WPA Slave Narratives, showcased that many of the people interviewed were now living far away from the places where they were enslaved. Some of the people interviewed had been enslaved in states as far away as Virginia and North Carolina.

Through using kepler.gl I was able to discover a variety of different trends and relationships that was contained in the data of the interviews conducted in Alabama as part of the WPA Slave Narratives. These trends and relationship were revealed through the maps produced using kepler.gl.

Text Analysis with Voyant 2.0

I found working with the Voyant application to be both an interesting and exciting experience. I have never used any kind of text analysis software before so it was moderately challenging to troubleshoot and figure out how the different elements of the application worked. The text files that I used were a series of interviews conducted between 1936-1938, by the Federal Writer’s Project of the Works Progress Administration, of former slaves living in seventeen different states. Creating my corpus in Voyant proved to be simple. I copied the text files onto the site and allowed the application to process through the information. Once my corpus was created, I was able to analyze the contents of the text files using the five different tools available in Voyant. The first tool Cirrus, created a word cloud of the terms appearing most frequently in the text. The bigger the word appeared, the more frequently it can be found in the texts. For the texts that I analyzed from the interviews, the most frequently occurring words were old, come, got, and time. The second tool, Reader, created a list ordered by the frequency of all the words appearing in the corpus. This tool allows the user to find specific instances of different words within the text. When a specific word is selected every instance of that word in the text will be highlighted. The next tool, Trends, creates a line graph depicting the distribution of a term’s occurrence across the whole corpus or in one specific document. These graphs allow the user to see where a certain word occurs throughout the whole corpus or in individual documents. The fourth tool, Summary, lists the basic information about the texts such as the number of words in the documents, the length of the documents, vocabulary density, and the distinctive words from each document. This tool allows the user to observe certain patterns that can be found in the different documents. The final tool, Contexts, creates a table that shows each occurrence of a specific word with the segments of text that directly precede and follow the term throughout the entire corpus or the specific document. This tool allows the user to see where in the text a specific word appears and see if there are any patterns of where words are used. All of these tools allow the user to analyze a set of texts to pick out certain patterns and identify certain common themes within this series of texts.

Metadata Review of AdViews

The source I am reviewing comes from one of the digital collections of the Duke University Library called AdViews. This database features thousands of television commercials created or collected by the D’Arcy Masius Benton & Bowles (DMB&B) advertising agency, dated 1950s – 1980s. The ad I used for the review can be found at: https://library.duke.edu/digitalcollections/adviews_cup_a_soup/

What features of the digital objects does the metadata describe?:

  • Title: Cup-a-Soup
  • Subject: Food and Beverage
  • Description: Television commercials created for Lipton from the D’Arcy Masius Benton & Bowles advertising agency archives held in the John W. Hartman Center for Sales, Advertising & Marketing History at the Duke University Libraries. The TV ads were digitized from the 16mm preservation film prints in 2009, as part of the AdViews Collection.
  • Creator: D’Arcy Masius Benton & Bowles advertising agency
  • Date: 1970s
  • Format:  video/mp4
  • Type: Digitized video from original 16mm film
  • Location: John W. Hartman Center for Sales, Advertising & Marketing History at the Duke University Libraries

What features does it not describe?:

  • Names of the people featured in the ad
  • Biographical information about the people in the ads
  • Names of the people involved in the production of the ad
  • Biographical information about the creators of the ad

What questions does the metadata allow you to ask?:

  • Searches can be conducted by:
    • Title of the ad
    • Company the ad is for
    • Subject of the ad
  • The metadata allows the user to ask questions about who the ad was for, what the ad is about, what agency created the ad, where the physical copy of the ad is held, and what format is the ad in.

What questions does it not allow you to ask?:

  • The metadata does not allow the user to ask questions of who are the people featured in the ad and who were the individuals involved in the production of the ad.

Database Review: Academic Search Complete

Academic Search Complete

Academic Search Complete is an EBSCOhost database that describes itself as a leading resource for scholarly research. The goal of this database is to support high-level research in the key areas of academic study by providing journals, periodicals, reports, books and more. Academic Search Complete contains thousands of full text peer-reviewed journals, articles, and book that cover a wide range of topics including anthropology, engineering, law, sciences and more.

  • Overview:
    • Search Options: The search options available for this database are to search by all text, author, title, subject terms, abstract or author-supplied abstract, author-supplied keywords, geographic terms, people, reviews & products, company entity, NAICS code or description, DUNS number, ticker symbol, journal name, ISSN, ISBN, and accession number.
    • Information on Digitization: EBSCO does not provide much information on their digitization process, only that they provide access to thousands of full text journals.
  • Facts:
    • Date Range: The database cover material from 1887-present for abstracting and indexing and 1911-present for full text items.
    • Publisher: EBSCO Information Services
    • Publisher About Page: https://www.ebsco.com/about
    • Object Type: PDF Full Text Files of academic journals and articles and HTML Full Text Files of academic journals and articles
    • Location of Original Materials: The original materials are held by the respected publishers of the individual journals.
    • Exportable Image?: Yes for individual use, but require the copyright holder’s express permission for other uses.
    • Facsimile Image?:Yes
    • Full Text Searchable?: Yes
    • Titles List Links?: Yes
  • History/Provenance:
    • Original Catalogue?: Yes
    • Digitized from Microfilm?: No
    • Original Sources?: The original sources are owned by the respected publishers of each journal.
  • Reviews:
    • Council of Chief Librarians- Electronic Access and Resources Committee Review, March 2014: https://cclibrarians.org/sites/default/files/reviews/Documents/ear_rev_ebsco-upgrades_0314.pdf
  • Access: Information about accessing EBSCOhost databases can be found at: https://www.ebsco.com/open-access
  • Info from Publisher: https://www.ebsco.com/about
  • Citing: https://www.ebsco.com/products/research-databases/academic-search-complete © 2020 EBSCO Information Services. All rights reserved.

A Guide to Digitization

When an item is digitized, there are certain qualities about the item that can and others that cannot be captured through digitization. Digitization is able to capture what an item looks like in a flat two-dimensional way. An individual looking at a digitized item is viewing what is essentially a photograph of that item. Through this image, certain qualities of the item can be determined such as size, color, and texture. However, there are certain limits that can arise from the quality of the digitization. An image that is blurry, dark in certain places, or taken from a certain angle, can make certain qualities hard to determine about an item. Digitization cannot provide an accurate representation  of an item in the three-dimensional way that it exists. Through looking at a digitized image of an object, the viewer may not be able to determine what an object looks like on all sides, or what an object sounds or smells like. Therefore, there can be both limits and positive aspects of digitization related to the human senses.

Different types of items can be best captured by different forms of digitization. Items such as photographs, maps, and illustrations, can best be captured by scanning them to create a digital image of the photograph, map, or illustration. This form of digitization works best for photographs, maps, and illustrations because these are two-dimensional objects. Scanning these items, creates a two-dimensional digital image. Since photographs, maps, and illustrations are two-dimensional to begin with, the same qualities can be observed whether viewing the original or the digital copy. Creating a digital image is not an effective form of digitization for all items. Such items include songs and films. Music and films, unlike the previous objects discussed, require more than what is captured by an image. Music and films require the creation of digital audio and video files in order so that the viewer can experience both the sights and sounds of these items. A two-dimensional image is unable to capture all of the qualities of music and films. Again, this emphasizes the point of how human senses relate to digitization, especially aspects of sight and sound.

There are both positives and negatives to working with digitized representations of items. A major advantage of having digitized representations is that they can be made available to a much wider audience. Digitized representations allow an individual to be able to analyze an item without having the physical item in front of them. This can eliminate the constraints of access to items by researchers by allowing multiple individuals access to a particular item at one time. as mentioned by Marlene Manoff in her article “The Materiality of Digital Collections: Theoretical and Historical Perspectives.” However, as Paul Conway describes in this article “Building Meaning in Digitized Photographs,” there are certain considerations that go into the creation of digitized representations that impact both how they can be used and understood by researchers. In producing digitized representations, those creating them have to decide if they want to portray the items in their current condition, or in their original condition. This decision can impact not just how an item appears to the viewer but also how the viewer is meant to understand derive meaning from the item.

 

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