12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      The Contagious Spread of Violence Among US Adolescents Through Social Networks

      ,
      American Journal of Public Health
      American Public Health Association

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          <p class="first" id="d448107e120"> <i>Objectives.</i> To test the hypothesis that violence among US adolescents spreads like a contagious disease through social networks. </p><p id="d448107e125"> <i>Methods.</i> Participants were a nationally representative sample of 90 118 US students aged 12 to 18 years who were involved in the National Longitudinal Study of Adolescent Health. Violence was assessed by having participants report the number of times in the preceding 12 months they had been involved in a serious physical fight, had hurt someone badly, and had pulled a weapon on someone. </p><p id="d448107e130"> <i>Results.</i> Participants were 48% more likely to have been involved in a serious fight, 183% more likely to have hurt someone badly, and 140% more likely to have pulled a weapon on someone if a friend had engaged in the same behavior. The influence spread up to 4 degrees of separation (i.e., friend of friend of friend of friend) for serious fights, 2 degrees for hurting someone badly, and 3 degrees for pulling a weapon on someone. </p><p id="d448107e135"> <i>Conclusions.</i> Adolescents were more likely to engage in violent behavior if their friends did the same, and contagion of violence extended beyond immediate friends to friends of friends. </p>

          Related collections

          Most cited references19

          • Record: found
          • Abstract: found
          • Article: not found

          Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks.

          Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300-700%, and that homophily explains >50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Social influence bias: a randomized experiment.

            Our society is increasingly relying on the digitized, aggregated opinions of others to make decisions. We therefore designed and analyzed a large-scale randomized experiment on a social news aggregation Web site to investigate whether knowledge of such aggregates distorts decision-making. Prior ratings created significant bias in individual rating behavior, and positive and negative social influences created asymmetric herding effects. Whereas negative social influence inspired users to correct manipulated ratings, positive social influence increased the likelihood of positive ratings by 32% and created accumulating positive herding that increased final ratings by 25% on average. This positive herding was topic-dependent and affected by whether individuals were viewing the opinions of friends or enemies. A mixture of changing opinion and greater turnout under both manipulations together with a natural tendency to up-vote on the site combined to create the herding effects. Such findings will help interpret collective judgment accurately and avoid social influence bias in collective intelligence in the future.
              Bookmark
              • Record: found
              • Abstract: not found
              • Book: not found

              Models and Methods in Social Network Analysis

                Bookmark

                Author and article information

                Journal
                American Journal of Public Health
                Am J Public Health
                American Public Health Association
                0090-0036
                1541-0048
                February 2017
                February 2017
                : 107
                : 2
                : 288-294
                Article
                10.2105/AJPH.2016.303550
                5227928
                27997233
                743fcd50-7a49-4267-8228-0a50bfd1499d
                © 2017
                History

                Comments

                Comment on this article