Machine Learning Thesis Defense

  • Ph.D. Student
  • Machine Learning Department
  • Carnegie Mellon University
Thesis Orals

Statistical Text Analysis for Social Science

What can text corpora tell us about society? How can automatic text analysis algorithms efficiently and reliably analyze the social processes revealed in language production?

This work develops statistical text analyses of dynamic social and news media datasets to extract indicators of underlying social phenomena, and to reveal how social factors guide linguistic production. This is illustrated through three case studies: first, examining whether sentiment expressed in social media can track opinion polls on economic and political topics; second, analyzing how novel online slang terms can be very specific to geographic and demographic communities, and how these social factors affect their transmission over time; and third, automatically extracting political events from news articles, to assist analyses of the interactions of international actors over time.

We summarize a variety of computational, linguistic, and statistical tools that are employed for these analyses; we also contribute MiTextExplorer, an interactive system exploratory analysis of text data against document covariates, whose design was informed by the experience of researching these and other similar works. These case studies illustrate recurring themes toward developing text analysis as a social science methodology: computational and statistical complexity, and domain knowledge and linguistic assumptions.

Thesis Committee:
Noah Smith (Chair)
Tom Mitchell
Cosma Shalizi
Gary King (Harvard University)

Draft Thesis Document

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