The networked question in the digital era: How do networked, bounded, and limited individuals connect at different stages in the life course?

Abstract

We used in-depth interviews with 101 participants in the East York section of Toronto, Canada to understand how digital media affects social connectivity in general—and networked individualism in particular—for people at different stages of the life course. Although people of all ages intertwined their use of digital media with their face-to-face interactions, younger adults used more types of digital media and have more diversified personal networks. People in different age-groups conserved media, tending to stick with the digital media they learned to use in earlier life stages. Approximately one-third of the participants were Networked Individuals: In each age-group, they were the most actively using digital media to maintain ties and to develop new ones. Another one-third were Socially Bounded, who often actively used digital media but kept their connectivity within a smaller set of social groups. The remaining one-third, who were Socially Limited, were the least likely to use digital media. Younger adults were the most likely to be Networked Individuals, leading us to wonder if the percentage of the population who are Bounded or Limited will decline over time.

News Media Use, Talk Networks, and Anti-Elitism across Geographic Location: Evidence from Wisconsin

“…While early studies documenting these structures (e.g. Hampton et al 2011) are clearly in need of replication, contemporary work by Wellman et al (2020) suggests this pattern persists. In this sense, Facebook networks transcend time , not space.…”

“…xxix.). And as Wellman’s most recent work shows, even the most networked individuals describe using digital media primarily to connect to people with place- or kin-based affiliations (Wellman et al 2020: 302–305).…”

Big data, computational social science, and other recent innovations in social network analysis

“…Prior to the 1990s, social network studies tended to focus on face‐to‐face relationships. Starting in the 1990s, however, the increasing ubiquity of the Internet and widespread engagement in online social networks resulted in important new lines of research on the implications of computer‐mediated communication for social networks (e.g., see Wellman et al., 1996, 2001, 2003, 2020; Hampton & Wellman, 2003, 2018, 2020; Wellman & Haythornthwaite, 2008; Quan‐Haase & Wellman, 2004; Brooks et al., 2014; Boase, 2016).…”

“…In general, they found that people tended to mix their digital and online interactions in a variety of different ways, that respondents tended to utilize the digital media they learned to use in earlier life stages, and that younger adults have more diversified personal networks and use more diverse types of digital media. On the basis of this work, Wellman et al (2020) developed a typology to characterize the ways in which people use digital communication in personal networks. About one third of study participants actively used digital media to maintain ties and to develop new ones.…”

A manifesto for computational sociology: The Canadian perspective

“…Computational methods include a gamut of different techniques including machine learning (e.g., deep learning, statistical learning, reinforcement learning), social network analysis, text and data mining (e.g., sentiment analysis, topic modelling, named‐entity recognition), agent‐based modelling, more flexible regression/estimation models (e.g., regression shrinkage and selection, Bayesian statistics, spatial regression models), advances in survey methods (e.g., survey experiments, optimum design, respondent‐driven sampling), and so on. Some sociologists in Canada have contributed directly to the development of particular methods (Alexander & Alkema, 2021; Andersen, 2008; Bignami‐Van Assche et al., forthcoming; Fosse & Winship, 2019; Fox, 2015; Fox & Andersen, 2006; Fu et al., 2020, 2021; Hayduk, 1996; Li et al., forthcoming; Miles, 2016; Nelson, 2020; Stecklov et al., 2018; Wellman et al., 2003, 2020), but more often sociologists have embraced and adapted methods developed by computer scientists, statisticians, and econometricians (Abul‐Fottouh et al., 2020; Boase, 2016; Das, 2022; Gallupe et al., 2019; Gruzd & Mai, 2020; Gu et al., 2021; Hogan & Berry, 2011; Howe et al., forthcoming; Kudla & Parnaby, 2018; Letarte et al., 2021; Li & Luo, 2020; McLevey, 2022; McMahan & McFarland, 2021; Quan‐Haase et al., 2021; Richardson et al., 2021; Roth et al., forthcoming; Shor & Miltsov, 2020; Shor et al., 2013; Silver & Silva, 2021; Smith, 2020; Sytsma et al., 2021; Veenstra & Vanzella‐Yang, 2022; Yuan et al., 2022).…”