RESEARCH INTERESTS
My research takes advantage of recent advances in computational and natural language processing techniques to theorize and empirically trace how individuals and collectives communicate, search, create, innovate, problem-solve, coordinate, evaluate, judge, and decide. I study these patterns of individual and collective cognition in social systems ranging from small groups such as mountaineering expeditions and inventor teams to large-scale social systems such as online knowledge ecosystems and scientific disciplines. In so doing, my research brings an information theoretic, computational lens to classical questions within organization theory and economic sociology.
My research takes advantage of recent advances in computational and natural language processing techniques to theorize and empirically trace how individuals and collectives communicate, search, create, innovate, problem-solve, coordinate, evaluate, judge, and decide. I study these patterns of individual and collective cognition in social systems ranging from small groups such as mountaineering expeditions and inventor teams to large-scale social systems such as online knowledge ecosystems and scientific disciplines. In so doing, my research brings an information theoretic, computational lens to classical questions within organization theory and economic sociology.
WORKING PAPERS
Aceves, Pedro, and James A. Evans. "Human Languages with Greater Information Density Increase Communication Speed, but Decrease Conversation Breadth."
Language is the primary medium through which human information is communicated and coordination is achieved. One of the most important language functions is to categorize the world so messages can be communicated through conversation. While we know a great deal about how human languages vary in their encoding of information within semantic domains such as color, sound, number, locomotion, time, space, human activities, gender, body parts and biology, little is known about the global structure of semantic information and its effect on human communication. Using large-scale computation, artificial intelligence techniques, and massive, parallel corpora across 15 subject areas--including religion, economics, medicine, entertainment, politics, and technology--in 999 languages, here we show substantial variation in the information and semantic density of languages and their consequences for human communication and coordination. In contrast to prior work, we demonstrate that higher density languages communicate information much more quickly relative to lower density languages. Then, using over 9,000 real-life conversations across 14 languages and 90,000 Wikipedia articles across 140 languages, we show that because there are more ways to discuss any given topic in denser languages, conversations and articles retrace and cycle over a narrower conceptual terrain. These results demonstrate an important source of variation across the human communicative channel, suggesting that the structure of language shapes the nature and texture of conversation, with important consequences for the behavior of groups, organizations, markets, and societies.
Language is the primary medium through which human information is communicated and coordination is achieved. One of the most important language functions is to categorize the world so messages can be communicated through conversation. While we know a great deal about how human languages vary in their encoding of information within semantic domains such as color, sound, number, locomotion, time, space, human activities, gender, body parts and biology, little is known about the global structure of semantic information and its effect on human communication. Using large-scale computation, artificial intelligence techniques, and massive, parallel corpora across 15 subject areas--including religion, economics, medicine, entertainment, politics, and technology--in 999 languages, here we show substantial variation in the information and semantic density of languages and their consequences for human communication and coordination. In contrast to prior work, we demonstrate that higher density languages communicate information much more quickly relative to lower density languages. Then, using over 9,000 real-life conversations across 14 languages and 90,000 Wikipedia articles across 140 languages, we show that because there are more ways to discuss any given topic in denser languages, conversations and articles retrace and cycle over a narrower conceptual terrain. These results demonstrate an important source of variation across the human communicative channel, suggesting that the structure of language shapes the nature and texture of conversation, with important consequences for the behavior of groups, organizations, markets, and societies.
PUBLICATIONS
Evans, James A., and Pedro Aceves. 2016. “Machine Translation: Mining Text for Social Theory.” Annual Review of Sociology.
More of the social world lives within electronic text than ever before, from collective activity on the web, social media, and instant messaging to online transactions, government intelligence, and digitized libraries. This supply of text has elicited demand for natural language processing and machine learning tools to filter, search, and translate text into valuable data. We survey some of the most exciting computational approaches to text analysis, highlighting both supervised methods that extend old theories to new data and unsupervised techniques that discover hidden regularities worth theorizing. We then review recent research that uses these tools to develop social insight by exploring (a) collective attention and reasoning through content from communication; (b) social relationships through the process of communication; and (c) social states, roles, and moves identified through heterogeneous signals within communication. We highlight social questions for which these advances could offer powerful new insight.
More of the social world lives within electronic text than ever before, from collective activity on the web, social media, and instant messaging to online transactions, government intelligence, and digitized libraries. This supply of text has elicited demand for natural language processing and machine learning tools to filter, search, and translate text into valuable data. We survey some of the most exciting computational approaches to text analysis, highlighting both supervised methods that extend old theories to new data and unsupervised techniques that discover hidden regularities worth theorizing. We then review recent research that uses these tools to develop social insight by exploring (a) collective attention and reasoning through content from communication; (b) social relationships through the process of communication; and (c) social states, roles, and moves identified through heterogeneous signals within communication. We highlight social questions for which these advances could offer powerful new insight.
DISSERTATION
Title: “The Linguistic Relativity of Collective Cognition and Group Performance”
Committee: James A. Evans (Chair), John Levi Martin, Amanda Sharkey, Sameer Srivastava
The long-researched linguistic relativity hypothesis predicts that the structure of a person’s language influences their cognition. While this hypothesis has only been pursued in the context of how language affects individual cognition, my dissertation brings this argument into sociological territory by asking how the structure of language influences group performance. The idea is that the structural characteristics of a language can constrain some patterns of communication and collective cognition, while enabling others. I ask: 1) Can differences in language structure affect the performance of groups? If so, 2) what mechanisms account for this performance difference? To answer these questions, I trace the social interaction and collective cognition effects of a novel language structure attribute. I create estimates of information density, the average amount of conceptual information contained within words of a language, across the world’s languages. I then use computational, archival, and experimental methodologies to trace how the information density of a language affects the nature of social interactions and collective cognition as well as the way information flows through those interactions, thereby affecting group performance.
Committee: James A. Evans (Chair), John Levi Martin, Amanda Sharkey, Sameer Srivastava
- National Science Foundation DDRI Grant
- Winner, INFORMS/Organization Science Dissertation Proposal Competition, 2017
- Winner, Best Paper Award from the Managerial and Organizational Cognition division of AoM.
The long-researched linguistic relativity hypothesis predicts that the structure of a person’s language influences their cognition. While this hypothesis has only been pursued in the context of how language affects individual cognition, my dissertation brings this argument into sociological territory by asking how the structure of language influences group performance. The idea is that the structural characteristics of a language can constrain some patterns of communication and collective cognition, while enabling others. I ask: 1) Can differences in language structure affect the performance of groups? If so, 2) what mechanisms account for this performance difference? To answer these questions, I trace the social interaction and collective cognition effects of a novel language structure attribute. I create estimates of information density, the average amount of conceptual information contained within words of a language, across the world’s languages. I then use computational, archival, and experimental methodologies to trace how the information density of a language affects the nature of social interactions and collective cognition as well as the way information flows through those interactions, thereby affecting group performance.
