Stepping out of confusion: A Digital Project on Digital Humanities – Movies and Books.

It is finally time to unleash the project I have been working on since the beginning of the semester. For the first time in 3 blog posts, I am not only granting you with a sneek a peak, but with the whole work and its boring details !
I hope you enjoy reading about the process of this research – If not, you can just skip to the interpretations and concluding statement !


1. Procedure

a. Data collection

As described in my first blog post; a Google spreadsheet was uploaded by our professor on Drive, and made available to both ENGL 205 and ENGL 207 classes.
The students were asked to go around and interview relatives, friends and colleagues and would add their findings as entries in the Spreadsheet.
Personally, I focused on my family – mainly my grandfather and great cousins. This way, I gathered information dating from the 20th century.

Getting to my second blog post, it occurred to me that cinematography could have an impact on the assigning criteria in High Schools. I then decided to assemble data and attribute a “yes” or “no” (answering whether the book was interpreted in a movie or not) to each title.
In order to do so, I used IMDb, one of the most popular sources for movie identification and rating.

b. Filtering

The total data included around 500 entries. Facing a tight time limit, I’ve decided to extract some of the data.

In order for that extraction to be accurate, I firstly disordered the spreadsheet. Secondly, I selected 20 titles, skipped another 20, and selected another 20 etc. till I reached a total of 201 entries.
To each entry I searched its possible adaptation movie on IMDb and wrote “yes” or “no”.
Overview+frequency - Duplicates

c. Studying Frequencies and Distribution in the Data

As I entered my data, I noticed a high repetition of the same titles that also were attributed a “yes”. Triggering a sense of interest, I started paying more and more attention to that phenomenon.

In order to study it statistically, I used Excel’s COUNTIF formula and added a table of frequencies. Afterwards, also using Excel’s tables and charts, I was able to configure the following graphs showing;
1.  The number of books that were repeated vs. the amount of times they were in the “No” spreadsheet”

Chart No

2. The number of books that were repeated vs. the amount of times they were in the “Yes” spreadsheet

Chart Yes

I also calculated the Average amount of repetitions in both sheets and the results were as such:

Average amount of repetition for the “Yes” sheet: 2.87

Average amount of repetition for the “No” sheet: 1.18

d. Using Palladio/Searching for an adequate digital Platform

Through my work, I endlessly tried to connect my previous findings with another field (time, space or emotion). I also wanted to work towards an opened question. My teammates tackled Space and Languages; I ended up focusing on education and books. At first, I tried to filter out only the “ST” findings. However, I figured that it wouldn’t matter whether it was during ST or FT, as what mattered to me were the emotions evoked in the reader, and how high schools should use it in order to make the student love literature.
In order to do so, I had to work on emotions.
Our professor had advised Palladio as a Digital Platform to visualize interconnections and networks between the different fields in the spreadsheet.
I did my research for two days, trying to find another Digital Platform that would express these connections with colors (such as “CartoDB”, which works with locations); hence using the color wheel of emotions we first employed.
Unfortunately, such a platform was really hard to find, and I ended up using Palladio in the most effective way possible:

  1. I noticed a larger Diversity of emotions when the book was made into a movie:
    Palladio chart emotions yes no
  2. A larger Negativity in emotions when the book wasn’t made into a movie in 21st century graduates, vs. more Intense emotions when the book wasn’t made into a movie in 20th century readers.
    Size nods + palladio emotions + years
  3. Network showing emotions related to books that were made into movies across the years.
    Yes - emotions and years

e. Finding a scholarly article to back up the research

As a last step in my procedure, I searched the AUB library databases for scholarly article regarding the topic.
I found two articles that I will be using in the interpretation section;

  • Lighting the Flame: Teaching High School Students to Love, Not Loathe, Literature – By Michael Milburn
  • Connecting Students with Shakespeare’s Poetry: Digital Creations of Close Reading – Joan Lange, Patrick Connolly, and David Lintzenich

    2. Interpretation 1: The Influence of Movies on Literary Popularity in High School

The first thing I would like to look at is how movies affected the literary world.
Whether the movie was watched or not, it is almost obvious that having a screening of the book popularizes the latter, hence increasing the amount of young adults (who are old enough to go out to the movies) reading it.

In order to follow my teammates’ steps, having the book interpreted in a movie has as well, to a certain extent, categorized it as a “Classic” or a “must” to be given in High School.

N.B: I would like to point out that I’m working on a correlation here, and not a one-sided relationship (it is also the popularity/success of the book that lead to its screening).

3. Interpretation 2: The emotional impact of Movies on High School Readers through time

As shown in the Figures above, a higher intensity in emotions (Ecstasy vs. Neutral/Happiness) is figuring when the book was made into a movie. Also, in the “Not interpreted into a movie” graph (The first one Posted); you can find “Boredom” connected with strictly 21st Century dates of Graduation, vs. Excitement/Admiration that are only found in 1985.

4. How to use these findings; the Screen-Book Correlation

In his article, Michael Milburn tries to find a way of transforming the disregard of students towards literature into an interest. Across study, he highlights the importance of High Schools going towards more popular and modern literature, even if it means leaving out some of the classics:
“But I have also learned that often one must show adolescents the way toward great literature by starting them off with ordinary-not bad or frivo- lous, just ordinary-writing. By lighting the flame” (Milburn 6) – “”We never get to read things like this,” one student enthused, flipping to the first page. I congratulated myself on taking the time to research my curriculum and basked in my newfound hipness: the students left murmuring about the forthcoming movie version starring George Clooney” (Milburn 6).
In line with my findings, the following excerpts show indeed the importance of considering the modern/popular works in a High School’s curriculum as it both lights the reading fire in the students’ hearts and increases their excitement and appreciation of literature – in correlation with the screening.
Moreover, digital platforms encourage students into close-reading literature. As mentioned in the second article, asking students to make a movie out of Shakespeare’s plays drove them into paraphrasing, analyzing and enjoying the work. To relate this to my findings, one could say that movies (that include sound, music, and visual emotion) express some things that word cannot; they have the power of unleashing hidden meanings in the written work.
To conclude, Digital Humanities should be more implemented in schools. Also, it is crucial for High Schools to consider the popularity of a reading/work in order to start lighting a passion for reading in a student’s life – which will later aid him in his university studies and career.


5. Reflection

a. Research Biases

First of all, research biases are inevitable, as the data is being extracted by College Students, who are each interrogating a limited amount of people. Some of my results may be biased because of the lack of quantity in entries from the 20th century, or an overload of entries in 2014 for example.

b. Lack of questions/Time to ask

Second of all, a lack of questions such as whether it was interpreted in a movie or not, if the movie was watched, the date of publication of the book… both biased and slowed down my research.
I also ran out of time and couldn’t interview more people or alter the data (add a new field). These suggestions should have been made at the beginning of the semester.

c. Difficulties:

One of the major difficulties I faced was the misspellings of Titles (I had to go over them all to know whether it was the same book spelled differently or not, which I had to do in order to study frequency), the different languages in which the book was translated was also an issue in the calculation of distribution and frequency.
Finally, the emotions weren’t all extracted from the wheel. Commas, additions and misspellings also slowed down my work. A strict application

6.  Suggestions for further research

For further research, I would suggest an introductionof broader fields of study inside the topic such as the Cinema alongside specifying the year of publication of the book and restricting the tags for emotions. Furthermore, as this issue was the hardest to tackle, I’d like to suggest a proposition of a variety of Digital Platforms, that tackle datasets in various ways and modes for all forms of research, and in order for the student to study his case the most efficient way.