Abstract:Despite the abundance of research on social networking sites, relatively little research has studied those who choose not to use such sites. This paper presents results from a questionnaire of over 400 Internet users, focusing specifically on Facebook and those users who have left the service. Results show the lack of a clear, binary distinction between use and non-use, that various practices enable diverse ways and degrees of engagement with and disengagement from Facebook. Furthermore, qualitative analysis r… Show more
“…This configuration provides a clear means of identifying reverters who did not initially intend to return as early as they did. This arrangement also provides a unique complement to more common approaches such as scraping existing data (e.g., Backstrom, Boldi, Rosa, Ugander, & Vigna, 2012;Schoenebeck, 2014), purposive university-run surveys (e.g., Baumer et al, 2013;Lampe, Vitak, & Ellison, 2013), or qualitative interviews (e.g., Portwood-Stacer, 2013;Schoenebeck, 2014). Analysis of these data, though, may not provide fully generalizable conclusions.…”
Section: The Backstorymentioning
confidence: 99%
“…The excluded are, against their will, prevented from using a technology, while the expelled previously used it but then were forced to stop. Other work suggests potential extensions, such as the "lagging resister" (Baumer et al, 2013) who has strongly considered dis-using some technology but not yet actually done so. In this article, we extend these typologies with the notion of a reverter, a rejecter who later becomes a user again.…”
Section: Related Workmentioning
confidence: 99%
“…An exploratory study of Facebook nonuse found that those who left the site reported being happy with their decision. Of those who deleted their account, very few returned, but of those who deactivated their account, almost half returned to the site (Baumer et al, 2013). Furthermore, a social contagion effect showed "respondents who knew someone that had deactivated were almost three times as likely to deactivate their account" (Baumer et al, 2013, p. 3261).…”
Section: Related Workmentioning
confidence: 99%
“…Some of this research examines rhetorical framing, for instance, of non-use as a political identity statement (Portwood-Stacer, 2013), or of the non-user as the epitome of an authentic human being (Harmon & Mazmanian, 2013). Other studies have examined motivations for leaving social media (Baumer et al, 2013), comparisons between users and non-users (Acquisti & Gross, 2006;Hargittai, 2008), or particular instances of non-use, for example, forgoing social media use during Lent (Schoenebeck, 2014). Whether an act of resistance or the result of infrastructural constraints, studying non-use illuminates the myriad factors that influence whether or not individuals use certain technologies.…”
Section: Introductionmentioning
confidence: 99%
“…The discourse around smartphone usage is, in many ways, characterized by fluctuations between extreme use and extreme non-use (Harmon & Mazmanian, 2013). In one study, nearly half the respondents who left Facebook subsequently returned to the site (Baumer et al, 2013). Another study focused on Grinder suggested departure as a gradual, tenuous, and, moreover, reversible process (Brubaker, Ananny, & Crawford, 2014).…”
This article examines social media reversion, when a user intentionally ceases using a social media site but then later resumes use of the site. We analyze a convenience sample of survey data from people who volunteered to stay off Facebook for 99 days but, in some cases, returned before that time. We conduct three separate analyses to triangulate on the phenomenon of reversion: simple quantitative predictors of reversion, factor analysis of adjectives used by respondents to describe their experiences of not using Facebook, and statistical topic analysis of free-text responses. Significant factors predicting either increased or decreased likelihood of reversion include, among others, prior use of Facebook, experiences associated with perceived addiction, issues of social boundary negotiation such as privacy and surveillance, use of other social media, and friends' reactions to non-use. These findings contribute to the growing literature on technology non-use by demonstrating how social media users negotiate, both with each other and with themselves, among types and degrees of use and non-use.
“…This configuration provides a clear means of identifying reverters who did not initially intend to return as early as they did. This arrangement also provides a unique complement to more common approaches such as scraping existing data (e.g., Backstrom, Boldi, Rosa, Ugander, & Vigna, 2012;Schoenebeck, 2014), purposive university-run surveys (e.g., Baumer et al, 2013;Lampe, Vitak, & Ellison, 2013), or qualitative interviews (e.g., Portwood-Stacer, 2013;Schoenebeck, 2014). Analysis of these data, though, may not provide fully generalizable conclusions.…”
Section: The Backstorymentioning
confidence: 99%
“…The excluded are, against their will, prevented from using a technology, while the expelled previously used it but then were forced to stop. Other work suggests potential extensions, such as the "lagging resister" (Baumer et al, 2013) who has strongly considered dis-using some technology but not yet actually done so. In this article, we extend these typologies with the notion of a reverter, a rejecter who later becomes a user again.…”
Section: Related Workmentioning
confidence: 99%
“…An exploratory study of Facebook nonuse found that those who left the site reported being happy with their decision. Of those who deleted their account, very few returned, but of those who deactivated their account, almost half returned to the site (Baumer et al, 2013). Furthermore, a social contagion effect showed "respondents who knew someone that had deactivated were almost three times as likely to deactivate their account" (Baumer et al, 2013, p. 3261).…”
Section: Related Workmentioning
confidence: 99%
“…Some of this research examines rhetorical framing, for instance, of non-use as a political identity statement (Portwood-Stacer, 2013), or of the non-user as the epitome of an authentic human being (Harmon & Mazmanian, 2013). Other studies have examined motivations for leaving social media (Baumer et al, 2013), comparisons between users and non-users (Acquisti & Gross, 2006;Hargittai, 2008), or particular instances of non-use, for example, forgoing social media use during Lent (Schoenebeck, 2014). Whether an act of resistance or the result of infrastructural constraints, studying non-use illuminates the myriad factors that influence whether or not individuals use certain technologies.…”
Section: Introductionmentioning
confidence: 99%
“…The discourse around smartphone usage is, in many ways, characterized by fluctuations between extreme use and extreme non-use (Harmon & Mazmanian, 2013). In one study, nearly half the respondents who left Facebook subsequently returned to the site (Baumer et al, 2013). Another study focused on Grinder suggested departure as a gradual, tenuous, and, moreover, reversible process (Brubaker, Ananny, & Crawford, 2014).…”
This article examines social media reversion, when a user intentionally ceases using a social media site but then later resumes use of the site. We analyze a convenience sample of survey data from people who volunteered to stay off Facebook for 99 days but, in some cases, returned before that time. We conduct three separate analyses to triangulate on the phenomenon of reversion: simple quantitative predictors of reversion, factor analysis of adjectives used by respondents to describe their experiences of not using Facebook, and statistical topic analysis of free-text responses. Significant factors predicting either increased or decreased likelihood of reversion include, among others, prior use of Facebook, experiences associated with perceived addiction, issues of social boundary negotiation such as privacy and surveillance, use of other social media, and friends' reactions to non-use. These findings contribute to the growing literature on technology non-use by demonstrating how social media users negotiate, both with each other and with themselves, among types and degrees of use and non-use.
The use of social network sites offers many potential social benefits, but also raises privacy concerns and challenges for users. The trade-off users have to make between using sites such as Facebook to connect with their friends versus protecting their personal privacy is not well understood. Furthermore, very little behavioral research has focused on how personal privacy concerns are related to information disclosures made by one's friends. Our survey study of 116 Facebook users shows that engaging with friends through tagging activity and third-party application use is associated with higher levels of personal Facebook usage and a stronger emotional attachment to Facebook. However, users who have high levels of personal privacy concern and perceive a lack of effectiveness in Facebook's privacy policies tend to engage less frequently in tagging and app activities with friends, respectively. Our model and results explore illustrate the complexity of the trade-off between privacy concerns, engaging with friends through tagging and apps, and Facebook usage.
Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method's unique advantages and drawbacks, as well as the broader roles that these methods play in the respective fields where they are often used. These insights allow us to make more informed decisions about the tradeoffs in choosing different methods for analyzing textual data. Furthermore, this comparison suggests ways that such methods might be combined in novel and compelling ways.
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