{"title":"Different Faces of False","authors":"M. Babcock, David M. Beskow, Kathleen M. Carley","doi":"10.1145/3339468","DOIUrl":null,"url":null,"abstract":"The task of combating false information online appears daunting, in part due to a public focus on how quickly it can spread and the clamor for automated platform-based interventions. While such concerns can be warranted, threat analysis and intervention design both benefit from a fuller understanding of different types of false information and of the community responses to them. Here, we present a study of the most tweeted about movie ever (Black Panther) in which the spread of false information of four different types is compared to the ad hoc Twitter community response. We find that (1) false information tweets played a small part in the overall conversation, (2) community-based debunking and shaming responses to false posts about attacks at theaters overwhelmed such posts by orders of magnitude, (3) as another form of community response, one type of false narrative (Satire) was used to attack another (Fake Attacks), and (4) the four types of false-information tweets differed in the use of hashtags and in the role played by originating users and responding users. Overall, this work helps to illustrate the importance of investigating “on-the-ground” community responses to fake news and other types of digital false information and to inform identification and intervention design and implementation.","PeriodicalId":15582,"journal":{"name":"Journal of Data and Information Quality (JDIQ)","volume":"1 1","pages":"1 - 15"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Data and Information Quality (JDIQ)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3339468","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The task of combating false information online appears daunting, in part due to a public focus on how quickly it can spread and the clamor for automated platform-based interventions. While such concerns can be warranted, threat analysis and intervention design both benefit from a fuller understanding of different types of false information and of the community responses to them. Here, we present a study of the most tweeted about movie ever (Black Panther) in which the spread of false information of four different types is compared to the ad hoc Twitter community response. We find that (1) false information tweets played a small part in the overall conversation, (2) community-based debunking and shaming responses to false posts about attacks at theaters overwhelmed such posts by orders of magnitude, (3) as another form of community response, one type of false narrative (Satire) was used to attack another (Fake Attacks), and (4) the four types of false-information tweets differed in the use of hashtags and in the role played by originating users and responding users. Overall, this work helps to illustrate the importance of investigating “on-the-ground” community responses to fake news and other types of digital false information and to inform identification and intervention design and implementation.