Modeling the Culture of Online Collaborative Groups with Affect Control Theory

2021 ◽  
pp. 147-160
Author(s):  
Jonathan H. Morgan ◽  
Jun Zhao ◽  
Nikolas Zöller ◽  
Andrea Sedlacek ◽  
Lena Chen ◽  
...  
Keyword(s):  
Control Theory ◽  
Affect Control Theory ◽  
Collaborative Groups ◽  
Affect Control

scholarly journals Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups

2021 ◽  
Author(s):  
Jesse Hoey ◽  
Mei Nagappan ◽  
Kimberly Rogers ◽  
Tobias Schröder ◽  
Diego Dametto ◽  
...  
Keyword(s):  
Social Science ◽  
Control Theory ◽  
Symbolic Interaction ◽  
Computational Simulation ◽  
Empirical Modeling ◽  
Computational Techniques ◽  
Affect Control Theory ◽  
Collaborative Groups ◽  
Affect Control ◽  
The Social

Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups (THEMIS.COG) was an interdisciplinary research collaboration of computer scientists and social scientists from the University of Waterloo (Canada), Potsdam University of Applied Sciences (Germany), and Dartmouth College (USA). This white paper summarizes the results of our research at the end of the grant term. Funded by the Trans-Atlantic Platform’s Digging Into Data initiative, the project aimed at theoretical and empirical modeling of identity and sentiments in collaborative groups. Understanding the social forces behind self-organized collaboration is important because technological and social innovations are increasingly generated through informal, distributed processes of collaboration, rather than in formal organizational hierarchies or through market forces. Our work used a data-driven approach to explore the social psychological mechanisms that motivate such collaborations and determine their success or failure. We focused on the example of GitHub, the world’s current largest digital platform for open, collaborative software development. In contrast to most, purely inductive contemporary approaches leveraging computational techniques for social science, THEMIS.COG followed a deductive, theory-driven approach. We capitalized on affect control theory, a mathematically formalized theory of symbolic interaction originated by sociologist David R. Heise and further advanced in previous work by some of the THEMIS.COG collaborators, among others. Affect control theory states that people control their social behaviours by intuitively attempting to verify culturally shared feelings about identities, social roles, and behaviour settings. From this principle, implemented in computational simulation models, precise predictions about group dynamics can be derived. It was the goal of THEMIS.COG to adapt and apply this approach to study the GitHub collaboration ecosystem through a symbolic interactionist lens. The project contributed substantially to the novel endeavor of theory development in social science based on large amounts of naturally occurring digital data.

scholarly journals The Structure of Deference: Modeling Occupational Status Using Affect Control Theory

2018 ◽  
Vol 83 (2) ◽  
pp. 243-277 ◽  
Author(s):  
Robert E. Freeland ◽  
Jesse Hoey
Keyword(s):  
Control Theory ◽  
Occupational Status ◽  
Meaningful Work ◽  
Occupational Prestige ◽  
High Status ◽  
Affect Control Theory ◽  
Affect Control ◽  
Cultural Expectations ◽  
Definition Of ◽  
Poll Data

Current theories of occupational status conceptualize it as either a function of cultural esteem or the symbolic aspect of the class structure. Based on Weber’s definition of status as rooted in either cultural or class conditions, we argue that a consistent operationalization of occupational status must account for both of these dimensions. Using quantitative measures of cultural sentiments for occupational identities, we use affect control theory to model the network deference relations across occupations. We calculate a measure of the extent to which one occupational actor deferring to another is incongruent with cultural expectations for all possible combinations of 304 occupational titles. Because high-status actors are less likely to defer to low-status actors, the degree to which these events violate cultural expectations provides an indicator of the relative status position of different occupations. We assess the construct validity of our new deference score measure using Harris Poll data. Deference scores are more predictive of status rankings from poll data than are occupational prestige scores. We establish criterion validity using five theoretically relevant workplace outcomes: subjective work attachment, job satisfaction, general happiness, the importance of meaningful work, and perceived respect at work.

scholarly journals Can we distill fundamental sentiments from natural language use? Evaluating word embeddings as a complement to survey-based ratings of affective meaning.

2019 ◽  
Author(s):  
Austin van Loon ◽  
Jeremy Freese
Keyword(s):  
Control Theory ◽  
Computational Linguistics ◽  
Language Use ◽  
Word Embeddings ◽  
Affect Control Theory ◽  
Affective Meaning ◽  
Shared Meanings ◽  
Affect Control ◽  
Text Corpora ◽  
Behavioral Method

Central to affect control theory are culturally shared meanings of concepts. That these sentiments overlap among members of a culture presumably reflects their roots in the language use that members observe. Yet the degree to which the affective meaning of a concept is encoded in the way linguistic representations of that concept are used in everyday symbolic exchange has yet to be demonstrated. The question has methodological as well as theoretical significance for affect control theory, as language may provide an unobtrusive, behavioral method of obtaining EPA ratings complementary to those heretofore obtained via questionnaires. We pursue a series of studies that evaluate whether tools from machine learning and computational linguistics can capture the fundamental affective meaning of concepts from large text corpora. We develop an algorithm that uses word embeddings to predict EPA profiles available from a recent EPA dictionary derived from traditional questionnaires, as well as novel concepts collected using an open-source web app we have developed. Across both a held-out portion of the available data as well as the novel data, our predictions correlate with survey-based measures of the E, P, and A ratings of concepts at a magnitude greater than 0.85, 0.8, and 0.75 respectively.

scholarly journals The Gender Gap in Business Leadership: Exploring an Affect Control Theory Explanation

2019 ◽  
Vol 82 (1) ◽  
pp. 75-97 ◽  
Author(s):  
Amy Kroska ◽  
Trent C. Cason
Keyword(s):  
Control Theory ◽  
Gender Gap ◽  
Women Executives ◽  
Business Leadership ◽  
Affect Control Theory ◽  
Business Executives ◽  
Affect Control ◽  
Computer Simulation Program ◽  
Using Data ◽  
Interactional Processes

We use affect control theory (ACT) and its computer simulation program, Interact, to theoretically model the interactional dynamics that women and men business executives are likely to face in the workplace, and we show how these dynamics may contribute to the gender gap in business leadership. Using data from 520 simulated events and two analysis strategies, we use ACT to develop empirically grounded hypotheses regarding these processes. The simulations suggest that women executives face a wider range of situations that require gender deviance than men executives, many of which may be unavoidable (e.g., confronting an unreliable employee). They also suggest that observers will attribute negative characteristics to both women and men executives who engage in a gender-deviant action but that the characteristics attributed to gender-deviant women executives (e.g., ruthless, sadistic) move their identity further from the affective meaning of “an executive” than the characteristics attributed to comparably gender-deviant men executives (e.g., awestruck, gullible), patterns that are likely to make the path to and retention of business leadership positions more difficult for women. We also discuss how our approach could be used to theorize about interactional processes underlying other inequalities, including those based on race, ethnicity, class, sexual orientation, disability, and age.

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