Prof. Margaret Roberts of UC San Diego will present on how using text matching to address confounding issues in research can supports credible causal inferences about the effects of experiencing censorship.
"Social media users in China are censored every day, but it is largely unknown how the experience of being censored affects their future online experience. Are social media users who are censored for the first time flagged by censors for increased scrutiny in the future? Is censorship “targeted” and “customized” toward specific users? Do social media users avoid writing after being censored? Do they continue to write on sensitive topics or do they avoid them?"
This is the opening to the latest research on Adjusting for Confounding with Text Matching by Prof. Margaret Roberts of UC San Diego with her research team (Brandon Stewart and Rich Nielsen). In this CIVICA Data Science Seminar session, she will present on how the method of text matching can help to address the problem of confounding in observational studies which ultimately supports credible causal inferences about the effects of experiencing censorship. She will also validate her research approach and illustrate the importance of conditioning on text to address confounding with two applications: the effect of perceptions of author gender on citation counts in the international relations literature and the effects of censorship on Chinese social media users.
