When I use a calculator for a math problem, I never question the answer I am given. This is because I trust the computer doing the mathematical analysis that gives me the answer. I trust the answer that I am given without challenging it. How does this differ when the computations and analysis being performed are on the humanities and digital literature? Should we trust computers the same amount? In my previous post I touched on this idea, and Meeks and Weingart mention it in their critique of Topic Modeling. They discuss how Topic Modeling extends beyond the capacities of humans and open new doors of understanding. However, their critique lies in our willingness to accept the conclusions drawn by these models without challenge or question. What stuck out to me most from this article was Meeks and Weingart’s call to use caution when accepting the results of Topic Modeling. They emphasize the interpretive capacities of human scholars, and urge us to think critically about the results that ensue. This is especially important in avoiding situations like those brought up in the Civilian Casualties article, ones where the data can reflect a bias and corroborate a stereotype that isn’t necessarily true. This isn’t to say that criminal analysis data is the same as topic modeling, but it points out the same possibility that the data we trust and the models we rely on might be producing conclusions that we do not want to take at face value. The article stressed to me the importance of not losing our human intuition to the presumption that computers know everything. Topic Modeling can open many doors, but we must look through those doors with caution.<\/p>\n","protected":false},"excerpt":{"rendered":"

When I use a calculator for a math problem, I never question the answer I am given. This is because I trust the computer doing the mathematical analysis that gives me the answer. I trust the answer that I am given without challenging it. How does this differ when the computations and analysis being performed … <\/p>\n

Continue reading “The Digital Humanities Contribution to Topic Modeling”<\/span><\/a><\/p>\n","protected":false},"author":208,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-557","post","type-post","status-publish","format-standard","hentry","category-class"],"_links":{"self":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts\/557","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/users\/208"}],"replies":[{"embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/comments?post=557"}],"version-history":[{"count":4,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts\/557\/revisions"}],"predecessor-version":[{"id":1492,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts\/557\/revisions\/1492"}],"wp:attachment":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/media?parent=557"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/categories?post=557"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/tags?post=557"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}