skip to main content


Title: Mapping the Narrative Ecosystem of Conspiracy Theories in Online Anti-vaccination Discussions
Recent research on conspiracy theories labels conspiracism as a distinct and deficient epistemic process. However, the tendency to pathologize conspiracism obscures the fact that it is a diverse and dynamic collective sensemaking process, transacted in public on the web. Here, we adopt a narrative framework to introduce a new analytical approach for examining online conspiracism. Narrative plays an important role because it is central to human cognition as well as being domain agnostic, and so can serve as a bridge between conspiracism and other modes of knowledge production. To illustrate the utility of our approach, we use it to analyze conspiracy theories identified in conversations across three different anti-vaccination discussion forums. Our approach enables us to capture more abstract categories without hiding the underlying diversity of the raw data. We find that there are dominant narrative themes across sites, but that there is also a tremendous amount of diversity within these themes. Our initial observations raise the possibility that different communities play different roles in the collective construction of conspiracy theories online. This offers one potential route for understanding not only cross-sectional differentiation, but the longitudinal dynamics of the narrative in future work. In particular, we are interested to examine how activity within the framework of the narrative shifts in response to news events and social media platforms’ nascent efforts to control different types of misinformation. Such analysis will help us to better understand how collectively constructed conspiracy narratives adapt in a shifting media ecosystem.  more » « less
Award ID(s):
1908407
NSF-PAR ID:
10181336
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
International Conference on Social Media and Society
Page Range / eLocation ID:
184 to 192
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Online discussion platforms provide a forum to strengthen and propagate belief in misinformed conspiracy theories. Yet, they also offer avenues for conspiracy theorists to express their doubts and experiences of cognitive dissonance. Such expressions of dissonance may shed light on who abandons misguided beliefs and under what circumstances. This paper characterizes self-disclosures of dissonance about QAnon-a conspiracy theory initiated by a mysterious leader "Q" and popularized by their followers ?anons"-in conspiratorial subreddits. To understand what dissonance and disbelief mean within conspiracy communities, we first characterize their social imaginaries-a broad understanding of how people collectively imagine their social existence. Focusing on 2K posts from two image boards, 4chan and 8chan, and 1.2 M comments and posts from 12 subreddits dedicated to QAnon, we adopt a mixed-methods approach to uncover the symbolic language representing the movement,expectations,practices,heroes and foes of the QAnon community. We use these social imaginaries to create a computational framework for distinguishing belief and dissonance from general discussion about QAnon, surfacing in the 1.2M comments. We investigate the dissonant comments to characterize the dissonance expressed along QAnon social imaginaries. Further, analyzing user engagement with QAnon conspiracy subreddits, we find that self-disclosures of dissonance correlate with a significant decrease in user contributions and ultimately with their departure from the community. Our work offers a systematic framework for uncovering the dimensions and coded language related to QAnon social imaginaries and can serve as a toolbox for studying other conspiracy theories across different platforms. We also contribute a computational framework for identifying dissonance self-disclosures and measuring the changes in user engagement surrounding dissonance. Our work provide insights into designing dissonance based interventions that can potentially dissuade conspiracists from engaging in online conspiracy discussion communities. 
    more » « less
  2. null (Ed.)
    Widespread conspiracy theories, like those motivating anti-vaccination attitudes or climate change denial, propel collective action, and bear society-wide consequences. Yet, empirical research has largely studied conspiracy theory adoption as an individual pursuit, rather than as a socially mediated process. What makes users join communities endorsing and spreading conspiracy theories? We leverage longitudinal data from 56 conspiracy communities on Reddit to compare individual and social factors determining which users join the communities. Using a quasi-experimental approach, we first identify 30K future conspiracists?(FC) and30K matched non-conspiracists?(NC). We then provide empirical evidence of the importance of social factors across six dimensions relative to the individual factors by analyzing 6 million Reddit comments and posts. Specifically, in social factors, we find that dyadic interactions with members of the conspiracy communities and marginalization outside of the conspiracy communities are the most important social precursors to conspiracy joining-even outperforming individual factor baselines. Our results offer quantitative backing to understand social processes and echo chamber effects in conspiratorial engagement, with important implications for democratic institutions and online communities. 
    more » « less
  3. null (Ed.)
    As our nation’s need for engineering professionals grows, a sharp rise in P-12 engineering education programs and related research has taken place (Brophy, Klein, Portsmore, & Rogers, 2008; Purzer, Strobel, & Cardella, 2014). The associated research has focused primarily on students’ perceptions and motivations, teachers’ beliefs and knowledge, and curricula and program success. The existing research has expanded our understanding of new K-12 engineering curriculum development and teacher professional development efforts, but empirical data remain scarce on how racial and ethnic diversity of student population influences teaching methods, course content, and overall teachers’ experiences. In particular, Hynes et al. (2017) note in their systematic review of P-12 research that little attention has been paid to teachers’ experiences with respect to racially and ethnically diverse engineering classrooms. The growing attention and resources being committed to diversity and inclusion issues (Lichtenstein, Chen, Smith, & Maldonado, 2014; McKenna, Dalal, Anderson, & Ta, 2018; NRC, 2009) underscore the importance of understanding teachers’ experiences with complementary research-based recommendations for how to implement engineering curricula in racially diverse schools to engage all students. Our work examines the experiences of three high school teachers as they teach an introductory engineering course in geographically and distinctly different racially diverse schools across the nation. The study is situated in the context of a new high school level engineering education initiative called Engineering for Us All (E4USA). The National Science Foundation (NSF) funded initiative was launched in 2018 as a partnership among five universities across the nation to ‘demystify’ engineering for high school students and teachers. The program aims to create an all-inclusive high school level engineering course(s), a professional development platform, and a learning community to support student pathways to higher education institutions. An introductory engineering course was developed and professional development was provided to nine high school teachers to instruct and assess engineering learning during the first year of the project. This study investigates participating teachers’ implementation of the course in high schools across the nation to understand the extent to which their experiences vary as a function of student demographic (race, ethnicity, socioeconomic status) and resource level of the school itself. Analysis of these experiences was undertaken using a collective case-study approach (Creswell, 2013) involving in-depth analysis of a limited number of cases “to focus on fewer "subjects," but more "variables" within each subject” (Campbell & Ahrens, 1998, p. 541). This study will document distinct experiences of high school teachers as they teach the E4USA curriculum. Participants were purposively sampled for the cases in order to gather an information-rich data set (Creswell, 2013). The study focuses on three of the nine teachers participating in the first cohort to implement the E4USA curriculum. Teachers were purposefully selected because of the demographic makeup of their students. The participating teachers teach in Arizona, Maryland and Tennessee with predominantly Hispanic, African-American, and Caucasian student bodies, respectively. To better understand similarities and differences among teaching experiences of these teachers, a rich data set is collected consisting of: 1) semi-structured interviews with teachers at multiple stages during the academic year, 2) reflective journal entries shared by the teachers, and 3) multiple observations of classrooms. The interview data will be analyzed with an inductive approach outlined by Miles, Huberman, and Saldaña (2014). All teachers’ interview transcripts will be coded together to identify common themes across participants. Participants’ reflections will be analyzed similarly, seeking to characterize their experiences. Observation notes will be used to triangulate the findings. Descriptions for each case will be written emphasizing the aspects that relate to the identified themes. Finally, we will look for commonalities and differences across cases. The results section will describe the cases at the individual participant level followed by a cross-case analysis. This study takes into consideration how high school teachers’ experiences could be an important tool to gain insight into engineering education problems at the P-12 level. Each case will provide insights into how student body diversity impacts teachers’ pedagogy and experiences. The cases illustrate “multiple truths” (Arghode, 2012) with regard to high school level engineering teaching and embody diversity from the perspective of high school teachers. We will highlight themes across cases in the context of frameworks that represent teacher experience conceptualizing race, ethnicity, and diversity of students. We will also present salient features from each case that connect to potential recommendations for advancing P-12 engineering education efforts. These findings will impact how diversity support is practiced at the high school level and will demonstrate specific novel curricular and pedagogical approaches in engineering education to advance P-12 mentoring efforts. 
    more » « less
  4. The prevalence and spread of online misinformation during the 2020 US presidential election served to perpetuate a false belief in widespread election fraud. Though much research has focused on how social media platforms connected people to election-related rumors and conspiracy theories, less is known about the search engine pathways that linked users to news content with the potential to undermine trust in elections. In this paper, we present novel data related to the content of political headlines during the 2020 US election period. We scraped over 800,000 headlines from Google's search engine results pages (SERP) in response to 20 election-related keywords—10 general (e.g., "Ballots") and 10 conspiratorial (e.g., "Voter fraud")—when searched from 20 cities across 16 states. We present results from qualitative coding of 5,600 headlines focused on the prevalence of delegitimizing information. Our results reveal that videos (as compared to stories, search results, and advertisements) are the most problematic in terms of exposing users to delegitimizing headlines. We also illustrate how headline content varies when searching from a swing state, adopting a conspiratorial search keyword, or reading from media domains with higher political bias. We conclude with policy recommendations on data transparency that allow researchers to continue to monitor search engines during elections. 
    more » « less
  5. Increased social media use has contributed to the greater prevalence of abusive, rude, and offensive textual comments. Machine learning models have been developed to detect toxic comments online, yet these models tend to show biases against users with marginalized or minority identities (e.g., females and African Americans). Established research in debiasing toxicity classifiers often (1) takes a static or batch approach, assuming that all information is available and then making a one-time decision; and (2) uses a generic strategy to mitigate different biases (e.g., gender and racial biases) that assumes the biases are independent of one another. However, in real scenarios, the input typically arrives as a sequence of comments/words over time instead of all at once. Thus, decisions based on partial information must be made while additional input is arriving. Moreover, social bias is complex by nature. Each type of bias is defined within its unique context, which, consistent with intersectionality theory within the social sciences, might be correlated with the contexts of other forms of bias. In this work, we consider debiasing toxicity detection as a sequential decision-making process where different biases can be interdependent. In particular, we study debiasing toxicity detection with two aims: (1) to examine whether different biases tend to correlate with each other; and (2) to investigate how to jointly mitigate these correlated biases in an interactive manner to minimize the total amount of bias. At the core of our approach is a framework built upon theories of sequential Markov Decision Processes that seeks to maximize the prediction accuracy and minimize the bias measures tailored to individual biases. Evaluations on two benchmark datasets empirically validate the hypothesis that biases tend to be correlated and corroborate the effectiveness of the proposed sequential debiasing strategy. 
    more » « less