From 1961 to 1971 the United States and the Republic of South Vietnam used chemicals to defoliate the coastal and upload forest areas of Viet Nam. The most notorious of these chemicals was named Agent Orange, a weaponized herbicide made up of two chemicals that, when combined, produced a toxic byproduct called TCDD-dioxin. Studied suggest that TCDD-dioxin causes significant human health problems in exposed American and Vietnamese veterans, and possibly their children (Agency, U.S. Environmental Protection, 2011). In the years since the end of the Vietnam War, volumes of discourse about Agent Orange has been generated, much of which is now digitally archived and machine-readable, providing rich sites of study ideal for “big data” text mining, extraction and computation. This study uses a combination of tools and text mining scripts developed in Python to study the descriptive phrases four discourse communities used across 45 years of discourse to talk about key issues in the debates over Agent Orange. Findings suggests these stakeholders describe and frame in significantly different ways, with Congress focused on taking action, the New York Times article and editorial corpus focused on controversy, and the Vietnamese News Agency focused on victimization. Findings also suggest that while new tools and methods make lighter work of mining large sets of corpora, a mixed-methods approach yields the most reliable insights. Though fully automated text analysis is still a distant reality, this method was designed to study potential effects of rhetoric on public policy and advocacy initiatives across large corpora of texts and spans of time.

At the intersection of digital identities and new language and social practice online is the concept of searchable talk (ST). ST describes the process of tagging discourse in a social networking service (SNS) with a hashtag (#), allowing it to be searchable by others. Although originating in Twitter, ST has expanded into other SNS, and is used therein not only to mark language-based posts, but also multimodal posts and images. While scholars have elucidated the structure and function of ST, their studies have primarily examined ST within language-based posts; few have researched ST with respect to images and other types of multimodal environments. In addition, ST has primarily been explored in its SNS of origin, Twitter. This project directly addresses these gaps by adopting a social semiotic approach to ST in three SNS with very different technological affordances, Twitter, Tumblr, and Pinterest. Through a multimodal discourse analysis (Kress, 2009) combining both linguistic and other visual methods, I ask how visual and linguistic choices operate semiotically across SNS environments with different affordances and constraints. Specifically, I uncover the multiple meanings of Beyoncé across a data set of 300 tweets, posts, and pins composed from entering #Beyoncé in the search engine of each SNS. I argue that 13 meaning-based identity categories emerge for Beyoncé, and link these meanings to their visual and linguistic expressions. I then compare these findings across modes and across platforms. Ultimately, I assert that this cross-platform approach elucidates Beyoncé as a cultural object subject to reinterpretation where #Beyoncé means much more than just “Beyoncé.” That is, when considering its multiple roles and meanings, #Beyoncé becomes a site of visual and linguistic indexicality in a process of entextualization. In this process, it is SNS users’ reinterpretations – linguistically and visually – that realize racist, sexist, and hegemonic Discourses, as well as those of emancipation and resistance.

This study investigates the language of “citizen sociolinguists,” everyday users of social network sites (SNS) who contribute to the discourses about language on Twitter, Reddit, and Tumblr, platforms with distinctive user demographics and technological affordances. The data were collected through keyword searches for mansplain, whitesplain, richsplain and thinsplain, metapragmatic neologisms which are lexical blends of the verb explain and one of four social categories. Disputes of macro-level ideologies are revealed by users’ creative meaning-making strategies and metapragmatic awareness of micro-level texts and utterances. Making use of the linguistic practices of the SNS, as well as the concisely-compacted semantic and pragmatic meanings of the four splain words, users come to evaluate communicative dynamics between speakers who differ from or relate with others in their experiences of sex, skin color, economic status, and physical form. Drawing on elements of Citizen Sociolinguistics (Rymes & Leone, 2014) with Critical Discourse Analysis (Fairclough, 1989) and Computer-Mediated Discourse Analysis (Herring, 2004), I question how users make metapragmatic judgements to convey varying meanings of the four focal words, and how the uses of [x]splain and the surrounding discourses illuminate socio-ideological values about language, about its intersection with gender, race, class, and body size, and the authority to speak on topics that are macro-contextually situated in discourses of privilege, power, and inequality. Lastly, I compare the findings across the three SNS platforms to understand how competing discourses differ in relation to each site’s user demographics, technological affordances and limitations, and subsequent linguistic practices.