Understanding Popularity, Reputation, and Social Influence in the Twitter Society

García, David; Mavrodiev, Pavlin; Casati, Daniele; Schweitzer, Frank

doi:10.1002/poi3.151

Cited by 67 publications

(45 citation statements)

References 53 publications

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“…Our study also identified a novel network characteristic – that is, suicidal youth having higher relative popularity versus nonsuicidal peers – as predicting higher SA rates and more attempts among those considering suicide (SA/Total SI). Popularity is strongly related to social influence (Garcia, Mavrodiev, Casati, & Schweitzer, ), suggesting that popular suicidal youth normalize suicidal behavior. SA rates were also higher when suicidal youth were more clustered (i.e., homophily).…”

Section: Discussionmentioning

confidence: 99%

Peer‐adult network structure and suicide attempts in 38 high schools: implications for network‐informed suicide prevention

Wyman

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Pisani

et al. 2019

Child Psychology Psychiatry

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Background Strengthening social integration could prevent suicidal behavior. However, minimal research has examined social integration through relationship network structure. To address this important gap, we tested whether structural characteristics of school networks predict school rates of ideation and attempts. Methods In 38 US high schools, 10,291 students nominated close friends and trusted adults to construct social networks. We used mixed‐effects logistic regression models to test individual student networks and likelihood of suicidal ideation (SI) and suicide attempts (SA); and linear regression models to estimate associations between school network characteristics and school rates of SI, SA, and SA among all with ideation. Results Lower peer network integration and cohesion increased likelihood of SI and SA across individual and school‐level models. Two factors increased SA: student isolation from adults and suicidal students’ popularity and clustering. A multivariable model identified higher SA in schools where youth–adult relationships were concentrated in fewer students (B = 4.95 [1.46, 8.44]) and suicidal students had higher relative popularity versus nonsuicidal peers (B = 0.93 [0.10, 1.77]). Schools had lower SA rates when more students named the same trusted adults named by friends and many students named the same trusted adults. When adjusting for depression, violence victimization and bullying, estimates for adult network characteristics were substantially unchanged whereas some peer effects decreased. Conclusions Schoolwide peer and youth–adult relationship patterns influence SA rates beyond individual student connections. Network characteristics associated with suicide attempts map onto three theory‐informed domains: social integration versus thwarted relational needs, group cohesion, and suicidal students’ social influence. Network interventions addressing these processes, such as maximizing youth–adult connections schoolwide and heightening influence of youth with healthy coping, could create more protective schools. Longitudinal and intervention studies are needed to determine how schools differentiate in network structure and clarify reciprocal dynamics between network characteristics and suicidal behavior.

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Section: Discussionmentioning

confidence: 99%

Peer‐adult network structure and suicide attempts in 38 high schools: implications for network‐informed suicide prevention

Wyman

Pickering

Pisani

et al. 2019

Child Psychology Psychiatry

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“…Over these texts, we applied a 100-dimensional Doc2Vec [30] model trained on a separate corpus of 1.7 Million biographical texts generated in previous research [31]. Doc2Vec fits a language model that represents documents (in this case bios) as vectors such that semantically and linguistically similar documents are close in the representation space.…”

Section: User Analysismentioning

confidence: 99%

Collective aspects of privacy in the Twitter social network

et al. 2018

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Preserving individual control over private information is one of the rising concerns in our digital society. Online social networks exist in application ecosystems that allow them to access data from other services, for example gathering contact lists through mobile phone applications. Such data access might allow social networking sites to create shadow profiles with information about non-users that has been inferred from information shared by the users of the social network. This possibility motivates the shadow profile hypothesis: the data shared by the users of an online service predicts personal information of non-users of the service. We test this hypothesis for the first time on Twitter, constructing a dataset of users that includes profile biographical text, location information, and bidirectional friendship links. We evaluate the predictability of the location of a user by using only information given by friends of the user that joined Twitter before the user did. This way, we audit the historical prediction power of Twitter data for users that had not joined Twitter yet. Our results indicate that information shared by users in Twitter can be predictive of the location of individuals outside Twitter. Furthermore, we observe that the quality of this prediction increases with the tendency of Twitter users to share their mobile phone contacts and is more accurate for individuals with more contacts inside Twitter. We further explore the predictability of biographical information of non-users, finding evidence in line with our results for locations. These findings illustrate that individuals are not in full control of their online privacy and that sharing personal data with a social networking site is a decision that is collectively mediated by the decisions of others.

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“…An example of such behavior can be a selfie with lifestyle information (Alshawaf and Wen 2015), captioned by using hashtags (Page 2012). Often, they follow limited people and, thus, have a high follower to following ratio, indicating their high influence/popularity (Farwaha and Obhi 2019;Garcia et al 2017). A study also indicated that high numbers of likes can indicate how popular the posts of a person are (Chua and Chang 2016).…”

Section: Popularitymentioning

confidence: 99%

Narcissism and fame: a complex network model for the adaptive interaction of digital narcissism and online popularity

2020

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Social media like Twitter or Instagram play the role of fertile platforms for self-exhibition and allow their users to earn a good repute. People higher in grandiosity share their contents in a charismatic way and as a result, they are successful in gaining attention from others, which may also influence their responses and behaviors. Such attention and repute enable them to be a trendsetter or a socially recognized maven. In this paper, we present a complex adaptive mental network model of a narcissist to see how popularity can adaptively influence his/her behavior. To analyze and to support behavior showed by our model, we used some key performance indicators from the literature to study the popularity and narcissism of 30 Instagram profiles. The results of the—both computational and empirical—study indicate that our presented computational adaptive network model in general shows the behavior found from the empirical data.

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Understanding Popularity, Reputation, and Social Influence in the Twitter Society

Cited by 67 publications

References 53 publications

Peer‐adult network structure and suicide attempts in 38 high schools: implications for network‐informed suicide prevention

Peer‐adult network structure and suicide attempts in 38 high schools: implications for network‐informed suicide prevention

Collective aspects of privacy in the Twitter social network

Narcissism and fame: a complex network model for the adaptive interaction of digital narcissism and online popularity

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