Critical Quantitative Methodologies: Measuring Emotion and Affect for Research and Pedagogies

• In Event: 67.027 - Science in Post-Truth Times: Rethinking Research Distinctions and Methodologies

Abstract

Purpose and Aim
This paper outlines the need for critical quantitative methodologies for studying how emotion moves through social media. Exploring the gap between existing affective computing and sentiment analysis methodologies (Brady et al 2017), and the recent surge of affect theory (Ahmed 2004, Author and Davis 2018), the paper investigates the interdisciplinary possibilities of engaging the recent "affective turn" to develop critical quantitative methodologies for educational research and innovative media education pedagogies.

Context and Theoretical Framework
The post-truth media and political landscape highlights the urgent need to understand how digital media technologies "educate" and control emotions and motivate political behavior. While the threats represented by "post-truth" might invite scholars and educators to reinvest in the objectivity and the rational political actor as saviors, it is imperative that educators and scholars keep pace with the increasingly nefarious targeting of emotions.

Educational researchers and educators are thus faced with the need to develop new approaches and pedagogies for critically evaluating how beliefs and values are shaped and influenced by social media. Pairing “big data” capability with behavioral science insights into emotion and motivation, Cambridge Analytica and others have developed psy-op strategies designed to influence the emotions of the individual — for example, catalyzing fear, anger, or outrage in order to manipulate the individual's political decision or vote (Karpf, 2017; Shaw, 2017). Computational propaganda uses algorithms, automation, and human curation to distribute disinformation over social media networks, intentionally sowing confusion and mistrust of media and democratic processes (Pew Research Center, 2017; Woolley & Guilbeault 2017). Equally threatening to democracies, digital media propaganda, algorithms, and activism seek to inflame pre-existing emotional tensions surrounding racism, misogyny, homophobia, and xenophobia (Author & Davis, 2018).

However, social science and humanities scholarship has not kept up with these sophisticated modes of "educating" emotions. How do we begin developing critical quantitative approaches--in this instance, to study how emotion moves through social media, and how microtargeting seeks to exacerbate racism, misogyny, and xenophobia? How might such quantitative approaches be informed by recent developments in affect theory, and by deeper understandings of factors such as word-order and socio-political linguistic context while tracking recurrent patterns and effects mobilized within social media?

Methods and Findings

This paper is grounded in the findings from a funded, one-year Knowledge Synthesis Research Grant (2017) titled "Safeguarding democracies: An interdisciplinary synthesis of digital media studies and the politics of emotion to understand identity, belonging and trust in the post-truth media landscape" (Author and Davis 2018). I argue that there exists neither shared conception of emotion, much less a cross-disciplinary, agreed-upon methodology, for identifying and evaluating emotion and affect within digital media. I then summarize current quantitative methods of sentiment analysis and affective computing (Brady et al 2017; Papacharissi 2015), which use a static lexicon for identifying particular emotions, and study the frequency of words and retweeting. I conclude by raising questions about how nuanced conceptualizations of affect and emotion might shape new methodological directions (Author 2018).

Author

• Megan Boler, University of Toronto