Maya Návar

MIT Department: Anthropology
Faculty Mentor: Prof. Graham Jones
Research Supervisor: Casey Hong
Undergraduate Institution: Stanford University
Hometown: El Paso, Texas, USA
Website: LinkedIn
Research Poster
Lightning Talk

Biography


I am a rising senior at Stanford University majoring in comparative literature and linguistics. Born and raised in El Paso, Texas, I’m broadly interested in the relationship between language, violence, and human rights at the US-Mexico border, with a specific focus on the experiences of asylum seekers awaiting court hearings in ICE detention and across the border in Ciudad Juárez, Chihuahua. I am also interested in border narratives, and am currently pursuing an honors thesis in comparative literature focused on literary representations of El Paso and Juárez, the power of water and fertility in the region, and femicide at the border. I hope to further explore these interests in graduate school. Outside of academics, I am an involved member of the National Hispanic Institute and love painting with my mom, going on walks, reading short stories, and drinking té de manzanilla con miel.

 

2021 Abstract

 

 

UFOs, apparently:

Evidential strategies and epistemic adverbs in scientific discourse on Twitter’s #ufotwitter community

Maya Návar1, Casey Hong2, Graham Jones3
1Department of Linguistics, Stanford University
2Institute for Data, Systems, and Society, Massachusetts Institute of Technology
3Department of Anthropology, Massachusetts Institute of Technology

An increasingly legitimized site of scientific discourse, Twitter offers unique insight into the interactional nature of data stabilization—the process by which information comes to be accepted as evidence across a community. Using frameworks rooted in linguistic anthropology and interactional stance, we examine the evidential strategies used by members of #ufotwitter, a community of ufo enthusiasts for whom evidence is a frequently-discussed and highly-contested topic. Our project focuses on the use of epistemic adverbs to modify various forms of evidence. We compiled a list of epistemic adverbs using the categorizations proposed by Wierzbecka (2006) and Quirk et al (1985) and used the Twitter API to query tweets containing those adverbs plus the term “#ufotwitter”; we also ran a network analysis of the community to identify distinct epistemic communities and users with a large following. Drawing upon Goffman’s model of participation, we developed a coding schema that identifies whether an adverb does epistemic work on a phenomenon, author, animator, principal (socially-responsible figure), or representation of evidence. The next step in our project is to manually code the tweets using this schema and compare them across the epistemic communities found in our network analysis. We expect our findings to highlight the relationship between language, data, evidence and authority in online scientific discourse.