What Made the Front Page
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The document discusses the challenges of categorizing literary genres within 19th-century literature, particularly in the context of newspapers filled with diverse content. It highlights various academic perspectives on genre classification, including its complexities and limitations, while employing computational methods such as topic modeling for analysis. Key insights include the significance of genre definitions and how distant reading techniques can provide contextual understanding of literature.
What Made the Front Page
- 1. What Made the Front Page in the 19th Century? Computationally Classifying Genre in “Viral Texts” Jonathan D. Fitzgerald Northeastern University @jon_fitzgerald
- 2. Ryan Cordell, “Reprinting, Circulation, and the Network Author in Antebellum Newspapers” “When every nineteenth-century newspaper brims with original and reprinted content of all kinds, it is difficult to know where to even begin studying that content.” @jon_fitzgerald
- 5. Christof Schöch, “Topic Modeling Genre: An Exploration of French Classical and Enlightenment Drama” “The concept of literary genre is a highly complex one: not only are different genres frequently defined on several, but not necessarily the same levels of description, but consideration of genres as cognitive, social, or scholarly constructs with a rich history further complicate the matter.” @jon_fitzgerald
- 6. Ted Underwood, “Understanding Genre in a Collection of a Million Volumes" “Centuries of literary scholarship have failed to produce human consensus about genre.” @jon_fitzgerald
- 7. Ted Underwood, “Distant reading and the blurry edges of genre” “The model was trained, after all, on examples tagged by human beings; the whole point of doing that was to reproduce as much as possible the contours of the boundary that separates genres for us.” @jon_fitzgerald
- 8. Benjamin Schmidt, “Genre Classification from Topic models” “To reduce dimensionality into the model, we have been thinking of using a topic model as the classifiers instead of the tokens. The idea is that classifiers with more than several dozen variables tend to get finicky and hard to interpret, and with more than a few hundred become completely unmanageable.” @jon_fitzgerald
- 9. Topic Model of 4,000 Clusters god life world heart love man time good men make court sir called judge de states government united congress made tbe jones _ trumble noble 000 10 cent year 30 tile thle tie tihe thie water feet cold hot put years hundred year paper twenty gen men enemy general left tho bo ho nnd aro ot ii ol la aud people great country public con dr blood cure stomach health states united president state mr @jon_fitzgerald
- 11. Classification Accuracy by Genre Actual Genre Percent Right advertisement 92.99325 news 87.48239 poetry 83.76231 prose 66.91618 @jon_fitzgerald
- 12. 25 Clusters with Genre Probabilities @jon_fitzgerald
- 13. 25 Clusters with Genre Probabilities WAGES IN 1800. The condition of the wages- class of that day may well be examined; it is full of instruction for social agitators. In the great cities unskilled workmen were hired by the day, bought their own food and found their own lodgings. But in the country, on the farms, or wherever a band was employed on some public work, they were fed and lodged by the employer and given a few dollars a month. On the Pennsylvania canals the diggers ate the coarsest diet, were housed in the rudest sheds, and paid $6 a month from May to November, and $5 a month from November to May. @jon_fitzgerald
- 14. 25 Clusters with Genre Probabilities @jon_fitzgerald
- 15. 25 Clusters with Genre Probabilities @jon_fitzgerald
- 16. @jon_fitzgerald
- 17. Matthew Jockers, Macroanalysis “…lead us not only to a deeper understanding of the genres…but also to clearer definitions of genre itself.” @jon_fitzgerald
- 18. A HORSE’S PETITION TO HIS DRIVER. Going up hill, whip me not. Coming down hill, hurry me not. On level tread, spare me not. Loose in stable, forget me not. Tired or hot, wash me not. If sick or old, chill me not. With bit and reins, Oh! jerk me not, And when you are angry, strike me not @jon_fitzgerald
- 19. @jon_fitzgerald
- 20. Matthew Jockers & David Mimno, “Significant themes in 19th-century literature" “The models we present here cannot represent the full meaning of individual books any more than satellite photos can show the details of individual trees. Like the satellite view, however, these macro-, or ‘distant-,’ scale perspectives on literature offer scholars a necessary context for and complement to closer readings.” @jon_fitzgerald