UTSC Speaker Series: A Language-Based Model of Organizational Identification Demonstrates How Within-Person Changes in Identification Relate to Network Position

When and Where

Thursday, October 20, 2022 12:00 pm to 1:15 pm
Hybrid Event: In person: Room AA160
Arts & Administration Building
UTSC campus


Amir Goldberg


Shifting attachments to social groups are a constant in the modern era. They are especially pronounced in the contemporary workplace. What accounts for variation in the strength of organizational identification? Whereas prior work has mostly focused on explaining variation between individuals, we develop a network-analytic theory of within-person changes in identification. We hypothesize that identification is positively related to occupying positions characterized by local clustering–having contacts who are mutually interconnected–and global bridging–having contacts who are disproportionately connected to individuals beyond a focal actor’s direct reach. We use the tools of computational linguistics to develop a language-based measure of identification and find support for the theory using pooled data of internal communications from three disparate organizations.

This event can be attended online or in person. 

Virtual link – Zoom: https://utoronto.zoom.us/j/88911252361, Meeting ID: 889 1125 2361Passcode: dsss2022  (No registration required)

In person – Room AA160, Arts & Administration Building, UTSC campus; Doors will open at 11:45 am and a light lunch will be available 

Speaker's Bio

Amir Goldberg’s research lies at the intersection of cultural sociology, data science and organization studies. He is interested in understanding how social meanings emerge and solidify through social interaction, and what role network structures play in this process. As co-director of the computational culture lab, Amir uses and develops computationally intensive network- and language-based methods to study how new cultural categories take form as people and organizational actors interact. 


UTSC Sociology, Data Sciences Institute