@ARTICLE{Hogg2015hcomp,
  author =       {Tad Hogg and Kristina Lerman},
  title =        {Disentangling the Effects of Social Signals},
  journal =      {Human Computation Journal},
  year =         {2015},
  volume =       {2},
  number =       {2},
  pages =        {189--208},
  abstract =     {Peer recommendation is a crowdsourcing task that leverages the opinions of
  many to identify interesting content online, such as news, images, or videos. Peer
  recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presentation order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social  signals in peer recommendation. Specifically, after controlling for variation due to item content  and its position, we find that social  signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social  signals exacerbate the ``rich get richer'' phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a ``herding'' effect that biases people's judgments about the content. Despite this, we find that social  signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality.
},
 url={http://arxiv.org/abs/1410.6744}
}

{"_id":"MrJrSP8DaWZZpgTEt","bibbaseid":"hogg-lerman-disentanglingtheeffectsofsocialsignals-2015","downloads":0,"creationDate":"2016-03-17T21:31:57.141Z","title":"Disentangling the Effects of Social Signals","author_short":["Hogg, T.","Lerman, K."],"year":2015,"bibtype":"article","biburl":"https://bibbase.org/network/files/iNQKC4NCiGYaef6D9","bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["Tad"],"propositions":[],"lastnames":["Hogg"],"suffixes":[]},{"firstnames":["Kristina"],"propositions":[],"lastnames":["Lerman"],"suffixes":[]}],"title":"Disentangling the Effects of Social Signals","journal":"Human Computation Journal","year":"2015","volume":"2","number":"2","pages":"189–208","abstract":"Peer recommendation is a crowdsourcing task that leverages the opinions of many to identify interesting content online, such as news, images, or videos. Peer recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presentation order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social signals in peer recommendation. Specifically, after controlling for variation due to item content and its position, we find that social signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social signals exacerbate the ``rich get richer'' phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a ``herding'' effect that biases people's judgments about the content. Despite this, we find that social signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality. ","url":"http://arxiv.org/abs/1410.6744","bibtex":"@ARTICLE{Hogg2015hcomp,\n author = {Tad Hogg and Kristina Lerman},\n title = {Disentangling the Effects of Social Signals},\n journal = {Human Computation Journal},\n year = {2015},\n volume = {2},\n number = {2},\n pages = {189--208},\n abstract = {Peer recommendation is a crowdsourcing task that leverages the opinions of\n many to identify interesting content online, such as news, images, or videos. Peer\n recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presentation order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social signals in peer recommendation. Specifically, after controlling for variation due to item content and its position, we find that social signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social signals exacerbate the ``rich get richer'' phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a ``herding'' effect that biases people's judgments about the content. Despite this, we find that social signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality.\n},\n url={http://arxiv.org/abs/1410.6744}\n}\n\n\n","author_short":["Hogg, T.","Lerman, K."],"key":"Hogg2015hcomp","id":"Hogg2015hcomp","bibbaseid":"hogg-lerman-disentanglingtheeffectsofsocialsignals-2015","role":"author","urls":{"Paper":"http://arxiv.org/abs/1410.6744"},"metadata":{"authorlinks":{"lerman, k":"https://bibbase.org/show?msg=embed&bib=https://dblp.org/pid/99/433.bib"}},"downloads":0},"search_terms":["disentangling","effects","social","signals","hogg","lerman"],"keywords":[],"authorIDs":["fbwoK7T4JSHosNKvD"],"dataSources":["oLYpAfsADT9uCu5oW","P4wbzpDjKahb4Yawu","Hvc9pNwWw2boxABn6","2RX7MNkKAsdgHwq9X","TcBpSanSM7tueQcfH","kxTAWhAJ5AEaGtDRP","W9wiZPEd3CMo9Zvyj","mQx34sdtPSKFkQLhg","wYLr8SBM4nf5YstZT","wGK6ZauNs4mPREmZA","myJ9KFC5zXN4qYWpW","6h4p3KcA7HgFLQpDk","jBaLf3eYjNGbA43MA"]}