R. Korolov, Justin Peabody, Allen Lavoie, Sanmay Das, M. Magdon-Ismail, W. Wallace
{"title":"Actions are louder than words in social media","authors":"R. Korolov, Justin Peabody, Allen Lavoie, Sanmay Das, M. Magdon-Ismail, W. Wallace","doi":"10.1145/2808797.2809376","DOIUrl":null,"url":null,"abstract":"We study the relationship between the level of chatter on a social medium (like TWitter) and the level of the observed actions related to the chatter. For example, in a disaster, how does relief-donation chatter on Twitter correlate with the dollar amount received? One hypothesis is that a fraction of those who act will also tweet about it, which implies linear scaling, action ∝ chatter. On the other hand, if there is a contagion effect (those who tweet about donation incite others to donate) and these incited donors tend to be \"quiet\" and not broadcast their actions, then we expect superlinear scaling action ∝ chatterγ where γ > 1. We show, using a simple model, that the degree sequence of the social media \"follower\" network plays a key role in determining the scaling exponent γ. For random graphs and power-law graphs, the scaling exponent is at or near 2 (quadratic amplification). We empirically validate the model's predictions using location-paired donation and social media data from U.S. states after Hurricane Sandy. Understanding the scaling behavior that relates social-media chatter to real physical actions is an important step for estimating the extent of a response and for determining social-media strategies to affect the response.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2808797.2809376","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 30
Abstract
We study the relationship between the level of chatter on a social medium (like TWitter) and the level of the observed actions related to the chatter. For example, in a disaster, how does relief-donation chatter on Twitter correlate with the dollar amount received? One hypothesis is that a fraction of those who act will also tweet about it, which implies linear scaling, action ∝ chatter. On the other hand, if there is a contagion effect (those who tweet about donation incite others to donate) and these incited donors tend to be "quiet" and not broadcast their actions, then we expect superlinear scaling action ∝ chatterγ where γ > 1. We show, using a simple model, that the degree sequence of the social media "follower" network plays a key role in determining the scaling exponent γ. For random graphs and power-law graphs, the scaling exponent is at or near 2 (quadratic amplification). We empirically validate the model's predictions using location-paired donation and social media data from U.S. states after Hurricane Sandy. Understanding the scaling behavior that relates social-media chatter to real physical actions is an important step for estimating the extent of a response and for determining social-media strategies to affect the response.