groups text into assigned numbers of “bags-of-words.” This allowed us to examine which “topics” were more likely to be raised by women and which by men, and which by regular citizens and which by officials. We found that men tend to speak much more than women, but that citizens are more likely to speak than officials showing that gram sabhas were active spaces for democratic discourse. We also found that men dominated the discussion with the topics raised by them more likely to be taken up by the next speaker and responded to by officials. But having a (randomly assigned) woman president of the village corrected this gender discrimination.

Gentzkow et al (2019) also study transcripts of speeches, but in the US Congress, and develop a new estimator to measure the degree of partisanship in these speeches from 1873-2016. They find that partisanship sharply increased over the period, particularly after the Republican takeover of Congress in the 1990s and the election of New Gingrich as speaker. These patterns are very different from previous work on partisanship in Congress that used roll-call votes and did not find such a sharp change. StephenDavidowitz (2014) also studies US politics looking at trends in racial animus in Google searches from 2004-2007, and pinpoints the locations in the United States where this is more likely to be observed. He finds that areas that displayed racial animus during this period, were much less likely to vote for Barack Obama in the 2008 election relative to votes they cast for John Kerry in the 2004 presidential election. He estimates that racial bias might have cost Obama 4.2% of the popular vote.

Lieberman and Miller (2021) study 36,000 online newspaper articles, and 306,000 comments made on them, in Nigeria and South Africa – both multi-ethnic societies with a history of civil conflict. The question they are interested in is whether news media contribute to nation-building in multi-ethnic societies. Using word counts and topic-modeling, they find that when an ethnic group is explicitly named in the newspaper article, it generates more and negative references in readers’ comments – referring either to the same ethnic group mentioned in the article or to another ethnic group. In other words, news media can negatively influence inclusive nationalist frames by triggering ethnicity specific reactions among their readers.

The question is if these NLP papers can be classified as “reflexive.” They substantially expand the kinds of data that can be analyzed to study discrimination and polarization and contribute towards our understanding of bias in settings that have not been previously analyzed, but the data are analyzed in a manner similar to quantitative survey data without any explicit attempt at cognitive empathy, process understanding, or participation. Comparing my own work in Parthasarthy et al (2019) and Sanyal and Rao (2019) in analyzing the same topic – Indian village meetings, it is clear that the close reading that Paromita Sanyal and I did of the transcripts, followed by hand-coding, discussion and iterative analysis (i.e. traditional qualitative work) brought us into closer proximity with the people who participated in these village meetings, than when my co-authors and I let machines do the coding for us. This is, more generally, true of the work done using NLP to study social media. Matamorez-Fernandez and Farkas (2021) conduct a systematic review of 104 studies, across disciplines, that study hate speech, and racial and gender bias in social media. They find “a lack of geographical and platform diversity” and “an absence of researchers’ reflexive dialogue with their object of study.” This is not by any means an indictment of machine learning, but simply emphasizes the point that a set of techniques by themselves cannot move us toward greater reflexivity. This is much more a function of the question being asked, the approach taken to the research, and the data being collected and analyzed. Take the wonderful recent paper on “Folklore” by Michalopolous and Xue (2021). They begin with an archive of thousands of motifs in folklore from 958 societies across the globe that were coded by the folklorist Yuri Berezkin. They classify the motifs into different “concepts” using a supervised NLP model using a dictionary created by the MIT Media Lab. They find that the concepts correlate with geographic attributes of the regions where they were created – for instance, earthquake prone regions are more likely to have motifs related to earthquakes. After validating the motifs, the examine the concepts to see if concepts that 13