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Ambitious Social Media Strategy for Project

Audience:

For my project, I plan to reach out to a diverse audience of individuals, scholars, and community organizations for my project. For the individuals, I am seeking adults between the ages of 18 and 29. For the scholars, I am seeking undergraduate, graduates, and post graduates. For the organizations, I am seeking specific Asian American community organizations in major cities, especially in the South.

Platforms:

In order to reach my target audiences, I plan to use Pinterest and Twitter. With Pinterest, I can spark their interest by having the audience view my Inspiration board. With Twitter, I can reach out to an audience quicker with brief messages. Also, I can add images and videos that may also spark their interest and hopefully lead the audience to my project site.

Messages:

The message that will appeal to the audience is the importance of recording and preserving the history of Asian Americans, particularly of the South. I want to convey that American history needs to include more information about Asian Americans. I want the audience to start giving it a significant presence in American history by adding their voice.

By Summer 2017, I will begin to reach out to my audience via Twitter and/or Pinterest. I want to gain at least 100 followers. From the 100 followers, at least 50 followers will visit my project site. Within 6 months, more than 150 people would have visited my project site. In Fall 2018, the followers for my social media platforms will increase from 150 to 300 followers; and more than 100 followers would have contributed to my project. By this time, I also want to begin working with the Dallas Historical Society to record the history of Asian Americans in the Dallas, Texas area.

A Reflection on Crowdsourcing

Crowdsourcing allows members of the public to contribute to existing digitized projects. Each project has its own type of collection, so it attracts different contributors. For my crowdsourcing comparison activity, I was able to review and compare 4 different projects. Also, I was able to take part in the contributor’s role by selecting two different projects.

For the Transcribe Bentham project, its “volunteers are proof that a partnership between the general public and academia works!” (UCL). 1. Contributors can transcribe the manuscripts “to preserve a collection of enormous international historical and philosophical importance” (UCL). 2. Contributors can encode transcripts because “by encoding your transcripts, you are helping to create a richer resource: researchers and students interested in Bentham’s writing process, his deletions and revisions, will be afforded the opportunity to pursue this, owing to your work” (UCL). By completing these tasks, the goals include broadening the access to manuscripts, to preserve the collection, and to generate scholarship for future research.

For the second project, the contributors are people who have knowledge about the building history. For example, contributors can provide information pertaining to “buildings long ago destroyed, streets renamed, whole neighborhoods redrawn or redefined” (The Building Inspector). The collected information from the contributors allows “Making these lost places findable via contemporary digital maps allows [The Building Inspector] to drill down through the layers of urban change and study the city in profound new ways.” There are four tasks that contributors undertake: check footprints, fix footprints, enter addresses, and check colors. For check footprints, the contributor checks “it is whether right, wrong, or close but in need of fixing” for the buildings shown on the computer (The Building Inspector). For the fix footprints, the contributor makes corrections to the imperfect footprints for recording history. For enter addresses, the contributor help provide “original street numbers” in order for TBI “to reference specific buildings in their historical context (and, eventually, to see who lived/worked there).” For check colors, the contributor helps with identifying the color coded buildings to distinguish residential vs. commercial. The contributors’ tasks meet the following goals of The Building Inspector: “It will allow our interfaces to drop pins accurately on digital maps when you search for a forgotten place. It will allow you to explore a city’s past on foot with your mobile device, ‘checking in’ to ghostly establishments. And it will allow us to link other historical documents to those places: archival records, old newspapers, business directories, photographs, restaurant menus, theater playbills etc., opening up new ways to research, learn, and discover the past” (The Building Inspector).

For the third project, “Trove brings together content from libraries, museums, archives, repositories and other research and collecting organisations big and small” (Trove). Also, Tim Sherratt explains, “So there’s a number of ways that people can participate and contribute to Trove, the most obvious one being text-corrections, so digitized newspapers, the OCR.” Sherratt contends that people contribute for the following reason: “In some ways I like to call it a collection of collections because what Trove does is it brings together collections from archives, museums, libraries, down to sort of little historical societies in little country towns.” As a content partner, the library or organization contributes records to the Trove collection. In the “For Content Partners,” there is more information on how to contribute by suggesting several methods. By contributing to Trove, “it is a collaboration between the National Library, Australia’s State and Territory libraries and hundreds of cultural and research institutions around Australia, working together to create a legacy of Australia’s knowledge for now and into the future.” Also, “It’s that aggregation of the data and what that makes possible in terms of other people building new tools, new interfaces, and creating new forms of analysis to work across that material” (Sherratt). It also a collaboration between the general public and libraries and organizations.

Papers of the War Department requests help from the community because “PWD’s work with community transcription is part of a larger project to make crowdsourcing possible for archivists and documentary editors with digital collections” (PWD). In the “Become a Transcription Associate” page, PWD suggests teachers, researchers, and doctoral candidates to participate in the transcription. Here are some of the examples/suggestions for who should consider contributing: “a doctoral candidate working on early federal economic issues might contribute transcriptions of account records and correspondence with pay masters that she made for her dissertation work; an instructor teaching the U.S. history survey course might work with students to transcribe a set of translated Indian speeches and treaties so that they can investigate the relationship between the government and Native Americans just after the revolution; a genealogist researching a distant relative who served in the Revolutionary War might transcribe correspondence between that soldier’s widow and the War Department about his pension” (PWD)

All four projects feature different interfaces. 1. In Transcribe Bentham, “For each manuscript to be transcribed, the Transcription Desk shows a digital image of the manuscript and an online text editor to enter and edit your transcription of the text” (UCL). The manuscript is transcribed online by viewing the image of it. The contributor can use the “edit” tool to transcribe parts of the manuscript. The word part is on the left side of the manuscript for the transcription. Also, the transcript can be encoded on the same online source by using the editing tool. 2. The Building Inspector uses scanned maps with an open source software with Map Warper. For each task, there is a map with buttons for the contributor to complete each task. For example, for the check colors, the contributor has to select a color button to indicate which type of building that is shown on the map. For the check footprints, the contributor has to select one of the three buttons for the selected building: fix, no, or yes. Each task is interactive and user friendly. The Map Warper allows the contributor to zoom in and take a closer look at the map. 3. Trove uses OCR platform. The corrections can be made on a digital format. However, according to Sherratt, “And of course as you would expect running OCR on these historical newspapers, the results aren’t always that great.” 4. The Papers of the War Department Project uses Scripto. When the document is selected for transcription, in plain text, there are 3 or 4 tabs and empty white space for each tab selection. If the transcription is completed or partially done, it will appear in “View Transcription.” After the contributor selects “Transcribe this Document,” the contributor selects “Transcribe” to transcribe the scanned document. The editing tool appears at the bottom of the transcription box or window. After the transcription is saved, the contributor views the results by selecting the “View Transcription” tab. There are also “View Discussion” and “Discuss this Page” tabs to view the transcription and editing exchange.

According to the UCL Transcribe Bentham, “All volunteers who transcribe manuscripts will be acknowledged in the relevant volumes of The Collected Works.” Also, the website features “Hall of Fame page that displays”a list of all volunteer transcribers who have contributed to Transcribe Bentham since the initiative launched in September 2010″ (UCL). Contributions are validated by consensus. So, a group of people are looking at the same map and conducting similar tasks. According to The Building Inspector website, “Every time you inspect a building, you’re essentially casting a vote alongside your fellow Inspectors. We show the same footprint and task to several people and tally up those votes to decide whether they agree.” Also, “If the jury’s still out, we keep the footprint in circulation until consensus is reached, focusing our collective efforts on the buildings most in need” (TBI). For Trove, the contributions are validated on a web page: http://trove.nla.gov.au/system/counts. It displays an updated list of contributors from libraries and organizations. There is also the Text Ccorrection Hall of Fame for contributors who corrected the text of the scanned newspapers and magazines. For the Papers of the War Department, the contributors are validated by contributing to the site as part of a community. Unfortunately, I could not find a list of contributors.

To better understand the concept of contributing to a crowdsourced project, I selected two projects. The first project was selected for transcription, and the second project was selected for correction. By contributing to both projects, I was able to see the significance of crowdsourcing for large online projects and some of the challenges that I encountered while contributing to both projects.

For the transcription project, I selected the Papers of the War Department. The interface was ok. I was allowed to view the image and change its size. There is also a “link to full size image,” which helped m view the image in a larger format. Unfortunately, I had difficulty with deciphering the handwriting for most of the selected scanned documents. I was able to transcribe numbers and a few words. I spent more time reading the handwriting and trying to decipher the letters and words. Here is an example of my transcription for “The Payment of Invalids”: http://wardepartmentpapers.org/scripto/index.php?documentId=4185&pageId=23257.

After I completed my correction, I viewed my contributions by clicking on “View Transcription.” Also, in my login page, I can see the which documents were transcribed and what was transcribed. Over time, I do not think I would continue contributing to this project because I had so much difficulty with deciphering/transcribing the handwriting. However, contributing to this project increased my interest in the importance of transcribing and preserving the PWD collection online.

This experience does fit into the goals of the Papers of War Project designers. As mentioned on their website, their goal “is to use the best technology of the early twenty-first century to recover and make widely available this vital record of American history that was seemingly lost at the dawn of the nineteenth century” (PWD). By allowing the public to access and contribute to the collection, my experience with transcribing fits in with the goals of the project designer. However, it takes certain people (e.g. handwriting experts) to decipher the handwriting of valuable documents.

For the correction project, I selected Trove. Trove’s text editing tool was easy to use. I was able to make corrections on the left side of the screen while viewing the scanned image of the newspaper page on the right side of the screen. I could zoom in and out and move the image. After I made the corrections for selective Obituaries, Marriages, and Crimes, I was able to see a saved list of my corrections. Here is an example from one of my corrections: http://trove.nla.gov.au/newspaper/article/143335662. The next examples are my corrections for Stoker Henry Davis and Mr. John E. Hammond’s Obituaries: http://trove.nla.gov.au/newspaper/article/58244432.

Trove has a “Text Correction Hall of Fame” list that shows the username and how many times the user has made corrections. My username is listed under the the number 33. In this project database, there are many articles waiting to be corrected. My contributions to correcting some of the pages in the newspaper increased my interest in the project because it was engaging, and I was acknowledged for my work. I might continue making corrections after this course is completed. I saw the importance of allowing the interested public to make corrections. My correction experience fits in with the goals of the project’s designers. As mentioned in “Text correction guidelines” of the Trove website, “the primary purpose of text correction is to improve the accuracy of search results in Trove’s newspapers and gazette zones. Text correction also allows for more accurate transcription downloads of the article text.”

Overall, I enjoyed the correction activity more than the transcribing activity because I selected a transcription project, Papers of the War Department, that was difficult for me to transcribe. Both projects allowed me to view the scanned contents; however, I had difficulty seeing the the contents from PWD. The viewing and editing tool for the Trove project was easier to use, and it had more features. The PWD tools seemed out of date like the scanned material/contents. Also, the PWD site had two features, View Transcription and Transcribe, that seemed to be a little confusing because a user may accidentally use either one to transcribe the material. For both projects, I was able to independently transcribe and correct the scanned documents. My individual contribution allowed me to appreciate each project more because I felt as though I had contributed to the big project. Both projects have valuable documents that need to be either transcribed or corrected by contributors in order to keep the collection accessible and readable to the public.

A Deconstructive Approach to Reading a Wikipedia Article

Wikipedia is open and free to the public. It is also a crowdsourced knowledge that allows contributors to edit articles for the free encyclopedia; however, there are some stipulations for editing information in Wikipedia. Even though Wikipedia is still frowned upon by some academics and scholars, some of the supporters argue that it is a good source for connecting users to their official sites. For example, Ed Galloway and Cassandra DellaCorte contend that users will be lead to the appropriate site “by placing external links to site content from relevant Wikipedia articles” (86); therefore, “the data also indicate that multiple users followed links to the ULS digital collections from Wikipedia” (93). By providing the proper links in the article’s external links section, Wikipedia will eventually lead the user to the appropriate site for more information about the source. The posted Wikipedia articles with extensive content are what the users deem as popular. For example, Rosenzweig explains, “Participation in Wikipedia entries generally maps popular, rather than academic interest in history” (RRCHNM). Before it was deemed as popular, Wikipedia offered a place for open access and crowdsourced knowledge for Digital Humanities since 2006.

In order to understand the development of Digital Humanities in Wikipedia, deconstructing its article/entry allows the user to read a Wikipedia article with a different approach. There are several questions to ask in order to deconstruct it: when, how, and why the article was created. Also, there is the question of what was posted in the article. In order to answer the questions, the user can select “View history” for Digital Humanities to view detailed information on who created the article, who contributed, who edited, what was edited, and when. By selecting “View history,” I found out that the Digital Humanities article/entry was created on January 30, 2006 by Elijahmeeks (username). In addition to Elijahmeeks, there were also other key contributors such as Gabrielbodard, Johnunsworth, ARK, SimonMahony, Alf7e, and Willardmccarty.

By clicking on the contributor’s username in “View history,” the user can find out more about the user’s information. For example, I clicked on Elijahmeeks’ biography; and it includes his occupation as a DH specialist at Stanford University with a link to his website and links to other Wikipedia web pages that he has created and/or contributed. On the other hand, JohnUnsworth’s biography is empty in Wikipedia because he has not added information. Simon Mahony’s bio has links to his biography page in Wikipedia and a link for his workplace because he works for the UCL Centre for Digital Humanities in London. These biographies may have influenced how the editors understood the topic of Digital Humanities and how it should have been displayed for the wide audience in Wikipedia. They may have influenced the trend in DH by editing information on the page that might seem conflicting, bias, or incorrect. Also, the biographies may have influenced the editors on what types of scholarships and external links were appropriate for the DH article.

Next, I viewed the contents of the article and examined the development of the content from start to current. Initially, there was a brief definition of DH and three sections: See Also, References, and External Links. First, contributors edited External Links and See Also. Then, the Contents list became longer. On May 31, 2006, “Toolset Lens” was changed to “Objectives.” On February 27, 2009, the DH Journals section was added. DH or “Humanities Computing Projects” were added. In late 2007, the “Bibliography” section was added. From 2009 to 2010, the “External Links” was edited. Between Dec 2011 and early 2012, there were edits to the Objectives, Bibliography, and Centers. In 2011, information for Graduate Degrees or Certificate programs was added. On May 30, 2012, a contributor changed the section “Problems and Limitations” to “Criticisms.” A visual chart that displayed the Quantification of Digital Humanities was temporarily added in 2012. In the summer of 2012, there were multiple edits to the Objectives and History. In 2014, the section that was edited the most was “Centers.” In 2013, there were more edits to “Centers,” “External Links,” and Journals. In 2014, the edits focused on “Objectives” and “Centers.” Also in 2014, a contributor changed “Objectives “to “Areas of Inquiry.” From 2015 and forward, most of the changes were made in “Criticisms”, “Journals,” “Centers,” “Bibliographies.” The definition, Objectives (Areas of Inquiry), and History were edited almost every year since it appeared in Wikipedia. The changes to DH in Wikipedia reflect the awareness and emergence of DH as an academic field and public interest.

While viewing the content development, I was able to analyze the issues. There were some issues with scholarship, definition/objectives, and See Also section created the most controversy on the DH page. There were also issues with consistency of the Bibliography styles. Then, there were trolls who edited and/deleted information that was either unrelated to DH or tried to discredit it. For example, on July 3, 2010, a user redefined DH as a “bs field,” and it was quickly deleted by a contributor or editor. Then, another user/contributor added an unnecessary category “summer schools” in the “See Also” section.

By deconstructing the DH Wikipedia article, I began to question if the article was informative and trustworthy. The DH page in Wikipedia provides the basic and almost neutral definition; however, it does not include the changes in the definition of DH in depth. Also, the articles or references are helpful, but they are not sufficient for understanding the trend in DH throughout the years. It does not provide enough information about the digital tools. Overall, the DH page is still evolving in Wikipedia. There will probably be more edits and additional content categories to be added to help promote DH and bring awareness to a wide audience. Also, it is somewhat trustworthy. Some of the information is valid because the contributors are associated with DH institutions. However, I am not sure the if user names of leading figures of DH studies are actually associated with the DH programs/organizations because some of the contributors do not have their biography posted in Wikipedia.

Overall, the Wikipedia page provides the very basic definition of Digital Humanities. It is a good starter page for someone who is searching for a basic understanding of DH. However, it can be a little frustrating for a scholar who is interested in learning more about DH because the information is very basic and more technical. It does not answer the following question: How is humanities related to Digital Humanities?

When reading a Wikipedia article, the user should carefully study its contents by searching “View history.” This activity opened my mind to viewing Wikipedia in a different way. If the information seems questionable, I can search “View history” to carefully examine the trustworthiness or reliability of the posted contents. Also, I can view the information for most of the key contributors. There is a “talk” option to view the exchange of communication before the edits. According to Rosenzweig, even though “a historical work without others and with multiple, anonymous authors is thus almost unimaginable in our professional culture,” “Wikipedia has become astonishingly widely read and cited” ( RRCHNM). Therefore, Wikipedia is a source for retrieving quick information about the article; however, users should not fully trust everything that is posted in Wikipedia, so it’s wise to review the contents in “View history.”

A Comparison of Voyant, CartoDB, and Palladio

Each digital tool has its benefits. Voyant, CartoDB, and Palladio offer the user different ways of viewing data in an engaging and enlightening way. Voyant is awesome when it works most of the time, and when it does function very well, the different visual representations of data help the user examine and analyze the textual data. I had some glitches with Voyant when I narrowed my search for the documents to two different states, Texas and Mississippi, in reference two words to compare their frequencies. Unfortunately, it did not display the Trends graph for the second selected word and it ignored the selected 2^nd state. The Cirrus word cloud and the Context were my favorite visual representations of data. They worked, and I learned to examine text in a different way. Cirrus, in a simplistic way, displays the bigger words with the smaller words to show the frequencies of the words used in the south. Context displays where and how the words were used in the text to convey their meanings. The dialect of the particular region or state depicts a socio-linguistic and cultural influence, which would be interesting to research.

Another digital tool I enjoyed learning is CartoDB. After humbly learning to click on the grey area of each field instead of attempting to slide the button numerous times, I began to see the benefits of CartoDB. This tool converts datasets into maps in different visual representations to help the user examine and analyze data. There are over 7 different types of maps. The basic or simple map is the point map, where the points are pinpointed on the map in reference to the dataset. Each point has an information window with fields. Some or all of the fields of data can be activated. CartoDB also gives the user the option to layer the map for optimized visual representation of the data. The user can add more than one dataset. I was able to see discrepancies in the interviews from the WPA Slave Narratives Collection. I began to question the geographic locations in relation to the time period and political and social issues when the interviews were recorded. The racial divide and social isolation were apparent in the maps.

Palladio is another mapping digital tool for visualizing data, but it is displayed in a constellation like graph. It allows the user to select specific data for comparison and visualization. The user is able to view the close connections of the data in Palladio. Specific connections between the subject and topic were displayed on each graph. I was able to see the connections between the interviewers and which interviewers conducted more interviews with the former slaves. I noticed that most of the female interviewers were close in proximity of each other, but the male interviewers were spread out. Also, I was able to examine the connections of the words in relation to the type of slave and gender of slave. The graph displays a rhetorical disconnect between inside (house) and outside (field) slaves.

All three digital tools are useful and engaging. By learning to use Voyant, CartoDB, and Palladio, I am able to see data in different ways in order to see what is missing in my data and what needs to be further researched. By using Voyant to examine the frequency of certain common words, mapping it in CartoDB’s point and heat maps, and mapping the connections of the common words used by the interviewees in Palladio, the user is able to analyze the data for future research. All three tools can complement each other by providing different visual representations of data in order for the user to analyze it and question it for future research.

A Reflection and Guide for Palladio

Palladio is a digital tool that helps you visualize data. It displays a constellation like graph to display the connections and disconnections of the data. It is user friendly, and it helps you analyze data in a different and engaging way.

For my Palladio activity, I used three datasets that Dr. Stephenson provided, and they come from the WPA Slave Narratives Collection. To begin, I did not have to create an account. I simply clicked on “Start,” and Palladio took me to a window where I added the data for the Interviewed. After clicking on “Load,” the data is displayed as a table. For each table, you are allowed to add a title. I can also select a field and add another table for comparison.

I added another dataset for the Places to add the locations. To add data the data for the Enslaved, I added another dataset. Then, I clicked on Map (top of the screen) to view the results.

In order to view specific graphs of data, I selected a certain topic and target. The selections are located in the window that is displayed on the right side of the screen. Also, I selected the highlight option to have a better view of the differences and sizes of the edges. I was able to move the graph for a better view by placing my mouse in the direction I want the graph to be displayed. You can also adjust the view from smaller to larger. When I downloaded each change in topic and target, I downloaded each graph as a .svg file.

Overall, Palladio is user friendly; and it presents data in a several different visual representations as a constellation like graphs. I was able to view 11 different graphs from the downloaded data. Each graph displayed different topics for visualization.

A Reflection and Guide for CartoDB

CartoDB, a web-based tool, allows the user to connect data with its software in order to produce mapping results. With its drag and drop functions, it “enables you to predict key insights from your location data” (Carto Builder). It allows the user to include several datasets. When they are ready to be published, the user can share them by embedding the link or sharing the URL. There are two main views: Data and Map. Also, CartoDB allows the user to select fields from the slide menu. There are different types of maps for visualization. You may select each map and adjust the speed and selected data. If the user wants to Visualize the data, he or she will select Visualize. The image can also be exported and saved. A title can be added by selecting elements. Also, text and images can be added to the map. Another key interesting feature is the layered map.

For my CartoDB activity, I used a dataset that was given by Dr. Robertson. The dataset is from the WPA Slave Narratives Collection, which belongs to the Library of Congress. Setting up a CartoDB account was easy. In addition to the “Sign Up for Free” option, there are options for monthly subscriptions, which allow more data storage and sharing. Fortunately, I was glad to have the free option. After I added the URL of the dataset to CartoDB, I was able to view the dataset. By selecting the Map at the top of the screen, I saw a map with orange points. Each point had an information window that can be seen by clicking on it. In order for the fields of data to be activated, I had to select the fields in the slide menu. After numerous attempts, I learned to simply click on the grey area by the buttons next to the field in order to activate it. Without Dr. Robertson’s advice, I would have continuously failed by sliding the button to the right to activate it.

After the fields of age, sex, location, etc. were selected, I was able to save the map and publish it. I also exported it. When I clicked on one of the orange points, I was able to see the selected fields with information. At different points on the map, it reveals that the interviews were mostly conducted throughout Alabama. Most of the interviews were near Birmingham. However, they seem to be conducted away from the major cities and closer to the rural areas. The map also reveals that there were more interviews conducted southwest of Birmingham, below the city of Tuscaloosa. After viewing each point on the map, I noticed that the ages of the former slaves ranged from 78 to 112. The “Type of Slaves” were mostly unknown. Some of them were either Field or House slaves. Not all of the former slaves were born in Alabama; some of them came from South Carolina or Georgia. The dates of the interviews ranged from late May to early July during the summer of 1937. The map also reveals that the interviews were conducted in areas that were close to the major highways that exist today but probably not in 1937. In 1937, interviewers probably had challenges with travel and weather in order to meet the interviewees, and the interviewees mostly lived in areas of familiarity and segregation. Also, the map shows the racial divide and isolation of African Americans and former slaves in the Deep South.

After viewing the Simple/Point map, I viewed five more types of maps. The Torque map reveals the location of each interview at a certain date and with animation, it shows change over time. This map shows that the first two interviews were conducted near Birmingham and Meridian. Then, the interviews were conducted south and north of Birmingham. Some of the points overlapped. I wonder if the interviews were conducted twice by two different interviewers or by the same interviewer. In comparison to the simple point map, this Torque map provides a visual reference of time and movement (location change) of how the interviews were conducted.

After viewing the Torque map, I selected the Cluster and Intensity maps. The Cluster and Intensity maps reveal the number of interviews conducted in a certain location. In comparison to the simple map and torque map, it shows the area/location that had a higher number of interviews and which areas were the hot spots. 10 or more interviews were near Meridian (12), in Birmingham and close to Montgomery (18). These were the concentrated areas of the interviews.

Next, I viewed the Heat map. The Heat map shows a better visual reference for the number of interviews in a cluster. In comparison to the Point map and Cluster map, the Heat map shows the concentration of the greater number of recorded interviews close to and in Montgomery. Birmingham and areas close to Merdian show a good number of interviews. The areas north of Birmingham show a lighter blue color, indicating less number of recorded interviews. The Heat map provides a closer look at the number of recorded interviews.

Then, I viewed the Category and Torque maps. The Torque map is a type of Category map but with animation to show the change over time. The Category maps reveal which type of slave was interviewed in each area. Most of the slaves who were interviewed were “unknown” for type of slave. In comparison to the point map and cluster map, the Category map reveals a specific area on the map and key for the type of slave. It gives the user a better visual reference and idea of how many areas on the map were actually unknown types rather than field or house slaves. The areas of higher number of recorded interviews had a higher number of unknown categories.

In addition to viewing each map, CartoDB allows the user to layer the maps. For my activity, the layered map reveals the U.S map (mostly to the east and south), states, cities, and specific locations in the cities of where the interviews were conducted. It shows that interviews were conducted in states other than Alabama. It includes interviews in Kentucky, Virginia, North Carolina, and Kentucky. Each orange point reveals an information window with age, the names of the interviewee and interviewer, the latitude and longitude, sex, date, type of slave, location of interview, place of birth, etc. It also shows the area for the types of slaves who were interviewed for each location point. My layered map is a combination of the point and category maps with added elements of title and image.

As a geospatial tool, CartoDB invites the user to examine and analyze data by providing visual representations of datasets. It transforms text into visual translations. Each map provides a different way of looking at the data on a map. For my CartoDB activity, I began to ask questions about the interviews by visualizing the various maps. What information is missing? What does the change over time signify? In some of the interviews, why is the type of slave indicated as “unknown.” I wonder if the interviewer decided not to ask the question or if the information was confidential. I also noticed the segregation or racial divide on the maps by viewing the interview locations and the lowest to highest number of interviews. The personal information about each former slave in connection to the location point of the interview on the map provided a humanistic approach. From my point of view, the data became more subjective than objective when CartoDB transformed the datasets into maps.

Voyant: A Reflection and Guide

Voyant, “a web-based tool” for “reading and analysis” of text, allows the user to discover the frequency of words in each document and corpus, which is a collection of documents (Voyant Tools). It displays visual components to help the user to examine and analyze words for text mining and topic modeling. In addition to the word cloud (Cirrus), Voyant displays several other visual displays or tools such as Summary, Reader, Trends, and Contexts. As an interactive tool, Voyant allows the user to select a word in the Cirrus and view the number frequency of the selected word. The Summary provides the provides information about the texts such as the how many times a word appears in each document and which words are distinctive in certain documents. The Reader displays the documents or corpus in which the selected word/words appear. The word is highlighted throughout the corpus. The Reader provides a visual reference for the frequency of the word/words within the corpus. Trends display a line graph to provide another visual representation of the frequency of the word/words in a document or corpus. The Contexts is an interesting tool that depicts how word/words appear frequently in various parts of the document or corpus. This tool “shows the surrounding text of the selected word/words” (Voyant Tools). All five tools in Voyant provide a visual reference to depict the frequency or occurrence of the word/words in a document and corpus. It helps the user further analyze the frequency and distinctiveness of words. Voyant engages the user to think beyond what is on the surface of viewing the frequency of the words; It questions the impact of the words, depending on location, time, etc.

For my Voyant activity, I copied the text files provided by Dr. Roberston. It was a dataset from the WPA Slave Narratives collection, which includes over a thousand interviews with former slaves from seventeen different states from 1936-1938. I copied the .txt files of the transcription, which comes from Project Gutenberg, and pasted them into Voyant. After selecting the “Reveal” button, the next page became a plethora of visual displays for examination and analysis. What caught my attention the most was Voyant’s ability to provide different visual results, which allowed me to view the words in the selected text files in a different way. The words were no longer just words; instead, they became visually significant. The Cirrus tool, a visual word cloud, displayed the frequency or occurrence of the words in different colors. The size of the text indicated the frequency of the word. I also exported the word cloud for a single view of the tool to study it in more depth.

For the first activity, I selected two words, “come” and “one,” that were higher in frequency or higher occurrence in Cirrus. Then, I viewed the Trends graph to see the visual display of each word within a certain state and document. I was able to view the word “one.” Voyant would not allow me to scale the 2nd word “come.” The Trends graph becomes blank, and the reader highlights another word. By examining the Trend graph for the first word “one,” I noticed that “one” appeared less frequently within the documents that I selected for Texas and Tennessee. However, its frequencies were higher in other documents related to states such as Oklahoma and Missouri. It might be that either the word was used less by the interviewer or the interviewee. Also, the transcriber in a specific state either used the word more frequently than the other states. The frequencies of the word can be subjective because the selected context for old can be based on someone’s point of view and not necessarily the interviewee.

When I added more text from the list provided, I noticed that there was a change in the word cloud. For this activity, I selected “dey” and “dat” for the two most common words that appear in the Corpus. I referenced the Cirrus/Word cloud. For each different state (Texas and Mississippi) that is selected for the documents, the Trends graph displays the frequencies of the word for each state. However, when the Trends graph is exported for the 2nd document for each word, the graph looks similar to the first graph. I do not know how to fix the error in Voyant. When I look back at the Trend graphs (before export) for each word and each state, I can see the changes in frequency of the selected word. Depending on the document segments, the frequencies of “dey” and “dat” differ for each state. The interviewees in that particular state used that word more frequently than the other interviewees from another state. For example, “dat” is used more frequently in Texas than Mississippi because the trend graph shows higher frequencies of the word in the documents. There is a lower frequency of “dat” in document 4 for Texas. For Mississippi, the lowest frequency of “dat” is in document 12. Another example would be the frequencies of “dey” that appear in the documents from Texas and Mississippi. The trends graphs show high frequencies of the word for both states. However, the word appears less frequently in document 4 for both states. For Mississippi, there is another low frequency of “dey” in document 12. Both states show less frequency of “dey” in document 17. The Trends graphs for the frequencies of the “dat” and day” in TX and MS show that word usage differs between states in the South. Also, the context and dialect play a role in the frequency of the words being commonly recorded by the interviewers.

For the distinctive word activity, I viewed the Summary, Trends and Contexts more than Cirrus. The distinctive words inform me that the recording and transcription of them are subjective. The meaning of the distinctive word is used differently, and it depends on the context. The word “ta” is distinctively used in Missouri, and it is used as a preposition. Instead of “to,” it is “ta.” When it is compared to other states, “ta” is rarely used in the other states; therefore, “ta” is distinctive of Missouri. The second word “hoo” is used more frequently in Kentucky, and when it is compared to other states, the graph shows that it is also rarely used in other states. Therefore, “ta” and “hoo” are words distinctive of Missouri and Kentucky.

Voyant is an engaging digital tool for examining and analyzing text from a document or a collection of documents (corpus). It provides a visual reference for looking at text from a different perspective. It has several tools for the user to see the visual results of the frequency of words. Voyant is not perfect, but it is a great tool to for visualizing text in different ways. Despite the minor setbacks (mostly due to technical issues/glitches), Voyant goes beyond the visualization of words. It allows the user to think about words in the document or corpus and begin to question how they affect certain social, political, and/or cultural aspects of humanity.

A Review of Metadata in American Consumer Culture

Name of Database: American Consumer Culture: Market Research & American Business, 1935-1965 (via GMU Library)

The metadata describes the word related or connected to the digital object. It describes the features of a general category of produce, place of origin, product name, and type of industry. For example, when I search “fruit,” I receive results with either “banana” or “pineapple.” When I search “Hawaii,” the results show either the origin or location of where the pineapple is grown. When I search a brand name (e.g. Dole), the results show Dole product proposals pertaining to pineapple. However, the metadata does not describe the type of fruit and the physical features of the fruit. When I search for “tropical fruit,” there are no search results.

Furthermore, the metadata allows me to ask for the origin, category, taste, and brand associated with pineapple. For example, when I search for “sweet fruit,” the results displayed pineapple and pineapple juice. On the other hand, the metadata does not allow me to ask for the physical features, texture, and colors of the digital object.

Metadata matters when searching for a particular item or image. Searching for words related to the keyword will result in some interesting finds. Sometimes, the user may end up finding other things that might be of interest. Searching for the digital objects by using metadata provided resources that I did not consider at the beginning of my search. As a result, I found some interesting things about the pineapple.

Review of American Consumer Culture: Market Research & American Business, 1935-1965

Overview:

The American Consumer Culture: Market Research & American Business, 1935-1965 database features many documents and ads related to well-known companies and brands during the specified time period. The interactive and visually appealing, digital collection is divided into “Documents” and “Ad Gallery.” The documents include market research reports, memorandums, proposals, and letters by Ernest Dichter, founder of The Institute for Motivational Research. The Ad Gallery includes images of printed advertisements from the iconic American companies that were related to Dichter’s works.

In addition to the general search browser, there are advanced search and popular searches. Also, there are tabs for certain categories for quicker access: Introduction, Documents, Industries, Chronology, Ad Gallery, Further Resources, and Help. If the user needs guidance to maneuver the database, in the lower, left corner of the home page, there is a “Not sure where to start?” button.

Facts:

The date range for the documents and images is 1935-1965. When searching the documents, the user can view the digitization of the original document. There is a tab for “original” view, and there is a tab for “Thumbnail” to magnify the view. After it is downloaded, the user can make notes on the document. However, a keyword search is not allowed on the document. The entire document can be downloaded as a pdf document. The user can also download the image by range or page by page.

For each selected document, the title, report number, date of publication, location of material, copyright, type of industry (e.g. automotive, broadcasting, food and drink, retail, etc.), company, brand, method of research, keywords, and language are listed. By simply clicking on the “Find out more about this year” hyperlink, an interactive timeline displays what was happening when each proposal, report, letter, or memorandum was made. The hyperlink for the business or company information is available by clicking on “Go to Business Biography.” Also, there is a hyperlink for “Go To Glossary” next to a list of keywords that is linked to the database’s Glossary.

For the “Ad Gallery,” the images can be downloaded. The title of the advertisement, date, image type (e.g. magazine), source, industry, company, brand, keywords, publication, and image details (e.g. color or black and white) are listed. Hyperlinks to the Glossary and Chronology/Timeline are not provided for the advertisement images.

Another useful and interesting feature is “My Archive.” The user can create a folder and save the searched items for further research. The “My Archive” option is located directly above the general search browser.

History:

Three “Participating Libraries” have contributed to the digital collection of the American Consumer Culture: Market Research & American Business, 1935-1965 database. “After closing his offices in the Croton-on-Hudson “Castle” in 1982, Dichter placed his library of research studies, publications and other writings with Mercy College, where he taught marketing. However, when Mercy College sought permission in 1990 to microfilm them and destroy the originals to save space, Dichter repossessed them. He was working with a dealer to sell them to another library but was unable to make satisfactory arrangements before his final illness. These materials, plus Dr. Dichter’s remaining business correspondence and notes, were removed to Hagley from Dichter’s last residence and office in Cortlandt Manor, N.Y., in 2007” (Adam Matthew). Eventually, the Dichter family donated the documents to the Hagley Museum and Library. The American Consumer Culture database features the digital collection that was donated by the Hagley Museum and Library.

The advertisements for the “Ad Gallery” were donated by John W. Hartmen Center and The Advertising Archives. The John W. Hartmen Center is part of the Rubenstein Rare Books and Manuscript Library at Duke University. It “has provided a selection of valuable advertisements for our Ad Gallery feature, dating from the mid-1930s to 1950s” (Adam Matthew). The Advertising Archives is one of the largest advertising archives in Europe and founded by Larry Suzanne Viner in 1990. They donated advertisements from 1930s to 1960s.

Review:

According to Cheryl LaGuardia, Research Librarian for the Widener University at Harvard University, “This file delivers amply on the vendor’s claim that it “provides a unique insight into the world of buying, selling, and advertising in pre- and post-war America.” It does more than that, with sometimes chilling psychosocial analysis that will successfully serve researchers in the areas of psychology, history, business, marketing, advertising, consumerism, gender studies, ethnic and minority studies, communications, sociology, American studies, philosophy, terrorism, and politics. ”

Access:

Access is through the George Mason University database which requires an account. In the “Help” tab, there are five different categories. For example, there is a “Terms of Use” icon that can be viewed. Detailed information is found in Access and Terms of Use.

Publisher:

Adam Matthew Digital is known for publishing “unique primary source collections from archives around the world.” There are three participating libraries that have donated the resources for the digital collection.

Citing:

For each source, there is a Citation button in black and white. However, it can be exported only by RefWorks and EndNote. The user has the option to select three different citation styles. The MLA citation is not entirely correct, but it is good enough. Consult the updated MLA guidelines for proper citation by visiting the MLA or OWL Purdue MLA.

Other Information:

The database includes a great resource for educators and scholars. The “Teaching” category appears when the user selects the “Further Resources” button. There are images and exercises for the classroom. Also, there is a “Case Studies” category. The author, Stefan Schwarzkopf, explores key themes in the reports.

A Guide to Digitization

There are certain things to consider when digitizing items. First, the user needs to consider the type of item, whether it is text or a physical object. When a text in a book or print source is captured, the focus is on the text. For the photograph of an object that implies a three-dimensional view but displays a two-dimensional view, the user might consider capturing the object with a different digital tool. Second, the user has to consider the type of background and lighting for the item. For example, certain objects are captured best in dim or bright lighting. Paul Conway contends, “Ideally rendering decisions take place under controlled lighting and through a carefully calibrated computer monitor, tools that may not be readily available to the most skilled user” (“Building Meaning” 5). Dark or light backgrounds can enhance or diminish the visual display of the text or object. This part of the digitization process becomes subjective and based on the user’s skills and experience. Third, the user needs to consider that a three-dimensional view of the item is impossible to capture in a photograph. However, a 3D view of the item can be best captured with a video because it shows all sides of the item from different angles, depending on the user’s experience with the video camera. The user can also record sound of the item by using video to enhance the visual experience and evoke an additional sensory detail of an item.

Photographs capture the images of the items by providing a visual reference for them. By making a video of the text, the user can guide the reader to zoom in and out of the image. Manoff cites N. Katherine Hayles by explaining that “a reader, viewer, or listener’s experience of a text is shaped by its material characteristics” (“Materiality of Digital” 313). The still image or photograph of the the text allows the reader to view it by inviting his or her own experience with textual resources. On the other hand, photographs do not capture other sensory details such as smell and some texture.

The video is the best form of digitization of different items. It provides a better visual of the item from different angles. It displays close ups of the item, enhances the view of the texture, and it records sound. Similar to the photograph, the video does not capture smell. Depending on the videography, the item’s size can be determined. On the other hand, the photograph is a more suitable digitization of the text item since it is a flat visual source, and the user is focusing only on the text. The OCR is a great tool for enhancing the photograph of the text by capturing an visual image as if it was viewed in real time.

Working with digitized representations of items teaches scholars and digital humanists that capturing an item in its original form changes once it is captured as a photographic image and video. Something does get lost in the digitized translation of each item. According to Melissa Terras’ article, “Digitisation and Digital Resources in the Humanities,” “Digitisation programs aim to create consistent images of documents and artefacts which are fundamentally individual and inconsistent, presenting a variety of physical attributes and capture requirements to the digitiser.” Perhaps, the guidelines for digitization will become uniform for the sake of consistency; or they may change depending on the purpose, research techniques, the availability of the digital tools at the time, and rise of new types of artifacts for digitization.