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Week 7: Fan mail and corpus-building

Summary

This week we will explore the topic of comic book fan mail and learn techniques for building a corpus of fan mail for research investigations.

Weekly Learning Objectives

• define key terms, such as letter of comment, letterhack, and text analysis.
• discuss scholarship on comic books fan mail and other paratexts.
• express basic concepts and goals of computational text analysis.
• analyze a small set of fan mails examples from different decades.
• Use image-capture and OCR tools to convert images of comic book letters of comment pages into searchable text.

Before class: Readings, resources, and tasks

Readings

• Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. American Political Science Review, 107(2), 267–297. https://doi.org/10.1093/pan/mps028
• Underwood, T. (2020). “Machine learning and human perspective.” PMLA, 135(1), 92-209. https://doi.org/10.1632/pmla.2020.135.1.92
• Comic book letter column.. Wikipedia..
• Letterhack.. Wikipedia..
• Participate in online discussion on Canvas.

Recommended but optional reading

• Misemer, L. (2020). Reading Comics at the Threshold: A Round Table on Letter Columns & Other Comics Paratexts (Part 1).. The Middle Spaces: Comics. Music. Culture.
• Kashtan, A. (2020). How can a comic book’s use of paratexts help it appeal to young readers? in Reading Comics at the Threshold: A Round Table on Letter Columns & Other Comics Paratexts (Part 3).. Ed. L. Misemer. The Middle Spaces: Comics. Music. Culture..
• Walsh, J. A, Martin, S., & St. Germain, J. (2017) “The Spider’s Web”: An analysis of fan mail from Amazing Spider-Man, 1963–1995. Poster presented at the Comics Arts Conference / Comic-Con International, San Diego, CA, July 22, 2017.
• Underwood, T. (2015). Seven ways humanists are using computers to understand text. Retrieved from https://tedunderwood.com/2015/06/04/seven-ways-humanists-are-using-computers-to-understand-text/.
• Shoemaker, M. (2020). Text Analytics.. Temple University Libraries.
• Wermer-Colan, A. (2020). Computational Textual Analysis.. Temple University Libraries.
• Walsh, J. A., Martin, S., & St. Germain, J. (2018). “The Spider’s Web”: An analysis of fan mail from Amazing Spider-Man, 1963–1995. In J. Laubrock, J. Wildfeuer, & A. Dunst (Eds.), Empirical comics research: Digital, multimodal, and cognitive methods (pp. 62-84). New York: Routledge.

IT Skills

Converting a scan to searchable text with Adobe Acrobat

• LinkedIn Learning: “Acrobat DC Essential Training: Convert a scan to searchable text

Screenshots (Windows)

• https://techcommunity.microsoft.com/t5/windows-11/how-to-take-a-screenshot-in-windows-11-4-ways/m-p/2849736.
• https://support.microsoft.com/en-us/windows/use-snipping-tool-to-capture-screenshots-00246869-1843-655f-f220-97299b865f6b
• https://support.microsoft.com/en-us/office/copy-the-window-or-screen-contents-98c41969-51e5-45e1-be36-fb9381b32bb7

Screenshots (Mac)

• https://support.apple.com/en-us/HT201361.

Screenshots (iPadOS/iOS)

• iPadOS: https://support.apple.com/en-us/HT210781.
• iOS: https://support.apple.com/en-us/HT200289.

Tools used for today’s activities

• Adobe Acrobat (Part of Adobe Creative Cloud, available at https://iuware.iu.edu.)
• ABBYY FineReader
• Tesseract
• qpdf

In class

Lecture and tech demo (print to machine-readable text)

Important: OCR is not the goal—building a structured corpus is the goal.

• Creating screen shots
• Performing OCR with Adobe Acrobat, ABBYY FineReader, and Tesseract
• Exploring fanmail activity

Exploring fanmail activity

We will spend time in class individually looking for interesting examples of fan mail. Try to find two or three distinct examples.

Keep notes on:

• Source of the fan mail: title, issue, date;
• topics discussed, e.g., story, plot, dialogue, art, characters, creators, process, current events, mistakes, criticisms, etc.;
• details the writers reveal about themselves, e.g., gender, occupation, etc.

After working individual, we will get together in small groups to share our findings, and then groups will report back to the full class.

Sources for finding fan mail

• comics in our shared OneDrive folder
• Digital Comic Museum (DCM)
• Comic Book +
• Internet Archive

Transition: From OCR to Corpus

Computational analysis requires:

• Clean text
• Consistent structure
• Defined units
• Metadata

No corpus → No computation

Looking Ahead

Next week, we will use your corpus to:

• Count words and phrases
• Compare across issues and decades
• Identify patterns in fan discourse

Today: build the dataset
Next: analyze it

slides