Guilbeault specializes in computational social science. His work lies at the intersection of cultural sociology, cognitive science, machine learning, and organization studies. One of his core interests explores how communication networks underlie the production and diffusion of cultural content, such as linguistic categories and social norms. This involves examining how communication dynamics are shaped by various forms of sociotechnical infrastructure, such as organizational cultures and algorithmically-mediated communication technologies like social media, large language models, and search engines. As co-director of the Computational Culture Lab, Douglas harnesses and builds computationally intensive network- and language-based methods to study how organizational cultures emerge and evolve. Supervisors: Damon Centola
Proceedings of the National Academy of the Sciences, 2018
Vital scientific communications are frequently misinterpreted by
the lay public as a result of mo... more Vital scientific communications are frequently misinterpreted by the lay public as a result of motivated reasoning, where people misconstrue data to fit their political and psychological biases. In the case of climate change, some people have been found to systematically misinterpret climate data in ways that conflict with the intended message of climate scientists. While prior studies have attempted to reduce motivated reasoning through bipartisan communication networks, these networks have also been found to exacerbate bias. Popular theories hold that bipartisan networks amplify bias by exposing people to opposing beliefs. These theories are in tension with collective intelligence research, which shows that exchanging beliefs in social networks can facilitate social learning, thereby improving individual and group judgments. However, prior experiments in collective intelligence have relied almost exclusively on neutral questions that do not engage motivated reasoning. Using Amazon’s Mechanical Turk, we conducted an online experiment to test how bipartisan social networks can influence subjects’ interpretation of climate communications from NASA. Here, we show that exposure to opposing beliefs in structured bipartisan social networks substantially improved the accuracy of judgments among both conservatives and liberals, eliminating belief polarization. However, we also find that social learning can be reduced, and belief polarization maintained, as a result of partisan priming. We find that increasing the salience of partisanship during communication, both through exposure to the logos of political parties and through exposure to the political identities of network peers, can significantly reduce social learning.
Since the publication of “Complex Contagions and the Weakness of
Long Ties” in 2007, complex cont... more Since the publication of “Complex Contagions and the Weakness of Long Ties” in 2007, complex contagions have been studied across an enormous variety of social domains. In reviewing this decade of research, we discuss recent advancements in applied studies of complex contagions, particularly in the domains of health, innovation diffusion, social media, and politics. We also discuss how these empirical studies have spurred complementary advancements in the theoretical modeling of contagions, which concern the effects of network topology on diffusion, as well as the effects of individual-level attributes and thresholds. In synthesizing these developments, we suggest three main directions for future research. The first concerns the study of how multiple contagions interact within the same network and across networks, in what may be called an ecology of contagions. The second concerns the study of how the structure of thresholds and their behavioral consequences can vary by individual and social context. The third area concerns the roles of diversity and homophily in the dynamics of complex contagion, including both diversity of demographic profiles among local peers, and the broader notion of structural diversity within a network. Throughout this discussion, we make an effort to highlight the theoretical and empirical opportunities that lie ahead.
The embodied cognition paradigm has stimulated ongoing debate about whether sensory data – includ... more The embodied cognition paradigm has stimulated ongoing debate about whether sensory data – including color – contributes to the semantic structure of abstract concepts. Recent uses of linguistic data in the study of embodied cognition have been focused on textual corpora, which largely precludes the direct analysis of sensory information. Here, we develop an automated approach to multimodal content analysis that detects associations between words based on the color distributions of their Google Image search results. Crucially, we measure color using a transformation of colorspace that closely resembles human color perception. We find that words in the abstract domains of academic disciplines, emotions, and music genres, cluster in a statistically significant fashion according to their color distributions. Furthermore, we use the lexical ontology WordNet and crowdsourced human judgments to show that this clustering reflects non-arbitrary semantic structure, consistent with metaphor-based accounts of embodied cognition. In particular, we find that images corresponding to more abstract words exhibit higher variability in colorspace, and semantically similar words have more similar color distributions. Strikingly, we show that color associations often reflect shared affective dimensions between abstract domains, thus revealing patterns of aesthetic coherence in everyday language. We argue that these findings provide a novel way to synthesize metaphor-based and affect-based accounts of embodied semantics.
Political actors are now deploying software programs called social bots that use social networkin... more Political actors are now deploying software programs called social bots that use social networking services such as Facebook or Twitter to communicate with users and manipulate their behavior, creating profound issues for Internet security. Current approaches in bot control continue to fail because social media platforms supply communication resources that allow bots to escape detection and enact influence. Bots become agents by harnessing profile settings, popularity measures, and automated conversation tools, along with vast amounts of user data that social media platforms make available. This article develops an ecological approach to thinking about bots that focuses on how social media environments propel bots into agency. This habitat-based model uses bots to expose ripe targets of intervention and innovation at the level of interface design. It also situates bots in the context of platform providers with a vested interest in interface design, revealing a range of new political problems. Most important, it invites a hybrid ethics, wherein humans and bots act together to solve problems in bot security and Internet ethics more broadly.
Proceedings of the National Academy of the Sciences, 2018
Vital scientific communications are frequently misinterpreted by
the lay public as a result of mo... more Vital scientific communications are frequently misinterpreted by the lay public as a result of motivated reasoning, where people misconstrue data to fit their political and psychological biases. In the case of climate change, some people have been found to systematically misinterpret climate data in ways that conflict with the intended message of climate scientists. While prior studies have attempted to reduce motivated reasoning through bipartisan communication networks, these networks have also been found to exacerbate bias. Popular theories hold that bipartisan networks amplify bias by exposing people to opposing beliefs. These theories are in tension with collective intelligence research, which shows that exchanging beliefs in social networks can facilitate social learning, thereby improving individual and group judgments. However, prior experiments in collective intelligence have relied almost exclusively on neutral questions that do not engage motivated reasoning. Using Amazon’s Mechanical Turk, we conducted an online experiment to test how bipartisan social networks can influence subjects’ interpretation of climate communications from NASA. Here, we show that exposure to opposing beliefs in structured bipartisan social networks substantially improved the accuracy of judgments among both conservatives and liberals, eliminating belief polarization. However, we also find that social learning can be reduced, and belief polarization maintained, as a result of partisan priming. We find that increasing the salience of partisanship during communication, both through exposure to the logos of political parties and through exposure to the political identities of network peers, can significantly reduce social learning.
Since the publication of “Complex Contagions and the Weakness of
Long Ties” in 2007, complex cont... more Since the publication of “Complex Contagions and the Weakness of Long Ties” in 2007, complex contagions have been studied across an enormous variety of social domains. In reviewing this decade of research, we discuss recent advancements in applied studies of complex contagions, particularly in the domains of health, innovation diffusion, social media, and politics. We also discuss how these empirical studies have spurred complementary advancements in the theoretical modeling of contagions, which concern the effects of network topology on diffusion, as well as the effects of individual-level attributes and thresholds. In synthesizing these developments, we suggest three main directions for future research. The first concerns the study of how multiple contagions interact within the same network and across networks, in what may be called an ecology of contagions. The second concerns the study of how the structure of thresholds and their behavioral consequences can vary by individual and social context. The third area concerns the roles of diversity and homophily in the dynamics of complex contagion, including both diversity of demographic profiles among local peers, and the broader notion of structural diversity within a network. Throughout this discussion, we make an effort to highlight the theoretical and empirical opportunities that lie ahead.
The embodied cognition paradigm has stimulated ongoing debate about whether sensory data – includ... more The embodied cognition paradigm has stimulated ongoing debate about whether sensory data – including color – contributes to the semantic structure of abstract concepts. Recent uses of linguistic data in the study of embodied cognition have been focused on textual corpora, which largely precludes the direct analysis of sensory information. Here, we develop an automated approach to multimodal content analysis that detects associations between words based on the color distributions of their Google Image search results. Crucially, we measure color using a transformation of colorspace that closely resembles human color perception. We find that words in the abstract domains of academic disciplines, emotions, and music genres, cluster in a statistically significant fashion according to their color distributions. Furthermore, we use the lexical ontology WordNet and crowdsourced human judgments to show that this clustering reflects non-arbitrary semantic structure, consistent with metaphor-based accounts of embodied cognition. In particular, we find that images corresponding to more abstract words exhibit higher variability in colorspace, and semantically similar words have more similar color distributions. Strikingly, we show that color associations often reflect shared affective dimensions between abstract domains, thus revealing patterns of aesthetic coherence in everyday language. We argue that these findings provide a novel way to synthesize metaphor-based and affect-based accounts of embodied semantics.
Political actors are now deploying software programs called social bots that use social networkin... more Political actors are now deploying software programs called social bots that use social networking services such as Facebook or Twitter to communicate with users and manipulate their behavior, creating profound issues for Internet security. Current approaches in bot control continue to fail because social media platforms supply communication resources that allow bots to escape detection and enact influence. Bots become agents by harnessing profile settings, popularity measures, and automated conversation tools, along with vast amounts of user data that social media platforms make available. This article develops an ecological approach to thinking about bots that focuses on how social media environments propel bots into agency. This habitat-based model uses bots to expose ripe targets of intervention and innovation at the level of interface design. It also situates bots in the context of platform providers with a vested interest in interface design, revealing a range of new political problems. Most important, it invites a hybrid ethics, wherein humans and bots act together to solve problems in bot security and Internet ethics more broadly.
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Papers by Douglas Guilbeault
the lay public as a result of motivated reasoning, where people
misconstrue data to fit their political and psychological biases. In
the case of climate change, some people have been found to
systematically misinterpret climate data in ways that conflict with
the intended message of climate scientists. While prior studies
have attempted to reduce motivated reasoning through bipartisan
communication networks, these networks have also been found to
exacerbate bias. Popular theories hold that bipartisan networks
amplify bias by exposing people to opposing beliefs. These theories
are in tension with collective intelligence research, which shows
that exchanging beliefs in social networks can facilitate social
learning, thereby improving individual and group judgments.
However, prior experiments in collective intelligence have relied
almost exclusively on neutral questions that do not engage motivated
reasoning. Using Amazon’s Mechanical Turk, we conducted an
online experiment to test how bipartisan social networks can influence
subjects’ interpretation of climate communications from NASA.
Here, we show that exposure to opposing beliefs in structured bipartisan
social networks substantially improved the accuracy of judgments
among both conservatives and liberals, eliminating belief
polarization. However, we also find that social learning can be reduced,
and belief polarization maintained, as a result of partisan
priming. We find that increasing the salience of partisanship during
communication, both through exposure to the logos of political parties
and through exposure to the political identities of network
peers, can significantly reduce social learning.
Long Ties” in 2007, complex contagions have been studied across an
enormous variety of social domains. In reviewing this decade of research, we discuss recent advancements in applied studies of complex
contagions, particularly in the domains of health, innovation diffusion, social media, and politics. We also discuss how these empirical
studies have spurred complementary advancements in the theoretical
modeling of contagions, which concern the effects of network topology on diffusion, as well as the effects of individual-level attributes
and thresholds. In synthesizing these developments, we suggest three
main directions for future research. The first concerns the study of
how multiple contagions interact within the same network and across
networks, in what may be called an ecology of contagions. The second
concerns the study of how the structure of thresholds and their behavioral consequences can vary by individual and social context. The
third area concerns the roles of diversity and homophily in the dynamics of complex contagion, including both diversity of demographic
profiles among local peers, and the broader notion of structural diversity within a network. Throughout this discussion, we make an effort
to highlight the theoretical and empirical opportunities that lie ahead.
the lay public as a result of motivated reasoning, where people
misconstrue data to fit their political and psychological biases. In
the case of climate change, some people have been found to
systematically misinterpret climate data in ways that conflict with
the intended message of climate scientists. While prior studies
have attempted to reduce motivated reasoning through bipartisan
communication networks, these networks have also been found to
exacerbate bias. Popular theories hold that bipartisan networks
amplify bias by exposing people to opposing beliefs. These theories
are in tension with collective intelligence research, which shows
that exchanging beliefs in social networks can facilitate social
learning, thereby improving individual and group judgments.
However, prior experiments in collective intelligence have relied
almost exclusively on neutral questions that do not engage motivated
reasoning. Using Amazon’s Mechanical Turk, we conducted an
online experiment to test how bipartisan social networks can influence
subjects’ interpretation of climate communications from NASA.
Here, we show that exposure to opposing beliefs in structured bipartisan
social networks substantially improved the accuracy of judgments
among both conservatives and liberals, eliminating belief
polarization. However, we also find that social learning can be reduced,
and belief polarization maintained, as a result of partisan
priming. We find that increasing the salience of partisanship during
communication, both through exposure to the logos of political parties
and through exposure to the political identities of network
peers, can significantly reduce social learning.
Long Ties” in 2007, complex contagions have been studied across an
enormous variety of social domains. In reviewing this decade of research, we discuss recent advancements in applied studies of complex
contagions, particularly in the domains of health, innovation diffusion, social media, and politics. We also discuss how these empirical
studies have spurred complementary advancements in the theoretical
modeling of contagions, which concern the effects of network topology on diffusion, as well as the effects of individual-level attributes
and thresholds. In synthesizing these developments, we suggest three
main directions for future research. The first concerns the study of
how multiple contagions interact within the same network and across
networks, in what may be called an ecology of contagions. The second
concerns the study of how the structure of thresholds and their behavioral consequences can vary by individual and social context. The
third area concerns the roles of diversity and homophily in the dynamics of complex contagion, including both diversity of demographic
profiles among local peers, and the broader notion of structural diversity within a network. Throughout this discussion, we make an effort
to highlight the theoretical and empirical opportunities that lie ahead.