Margeret Hall, Michael Logan, Gina S Ligon, Douglas C Derrick
{"title":"Do Machines Replicate Humans? Toward a Unified Understanding of Radicalizing Content on the Open Social Web.","authors":"Margeret Hall, Michael Logan, Gina S Ligon, Douglas C Derrick","doi":"10.1002/poi3.223","DOIUrl":null,"url":null,"abstract":"<p><p>The advent of the Internet inadvertently augmented the functioning and success of violent extremist organizations. Terrorist organizations like the Islamic State in Iraq and Syria (ISIS) use the Internet to project their message to a global audience. The majority of research and practice on web-based terrorist propaganda uses human coders to classify content, raising serious concerns such as burnout, mental stress, and reliability of the coded data. More recently, technology platforms and researchers have started to examine the online content using automated classification procedures. However, there are questions about the robustness of automated procedures, given insufficient research comparing and contextualizing the difference between human and machine coding. This article compares output of three text analytics packages with that of human coders on a sample of one hundred nonindexed web pages associated with ISIS. We find that prevalent topics (e.g., holy war) are accurately detected by the three packages whereas nuanced concepts (Lone Wolf attacks) are generally missed. Our findings suggest that naïve approaches of standard applications do not approximate human understanding, and therefore consumption, of radicalizing content. Before radicalizing content can be automatically detected, we need a closer approximation to human understanding.</p>","PeriodicalId":46894,"journal":{"name":"Policy and Internet","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/poi3.223","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Policy and Internet","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1002/poi3.223","RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/9/26 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"COMMUNICATION","Score":null,"Total":0}
引用次数: 10
Abstract
The advent of the Internet inadvertently augmented the functioning and success of violent extremist organizations. Terrorist organizations like the Islamic State in Iraq and Syria (ISIS) use the Internet to project their message to a global audience. The majority of research and practice on web-based terrorist propaganda uses human coders to classify content, raising serious concerns such as burnout, mental stress, and reliability of the coded data. More recently, technology platforms and researchers have started to examine the online content using automated classification procedures. However, there are questions about the robustness of automated procedures, given insufficient research comparing and contextualizing the difference between human and machine coding. This article compares output of three text analytics packages with that of human coders on a sample of one hundred nonindexed web pages associated with ISIS. We find that prevalent topics (e.g., holy war) are accurately detected by the three packages whereas nuanced concepts (Lone Wolf attacks) are generally missed. Our findings suggest that naïve approaches of standard applications do not approximate human understanding, and therefore consumption, of radicalizing content. Before radicalizing content can be automatically detected, we need a closer approximation to human understanding.
期刊介绍:
Understanding public policy in the age of the Internet requires understanding how individuals, organizations, governments and networks behave, and what motivates them in this new environment. Technological innovation and internet-mediated interaction raise both challenges and opportunities for public policy: whether in areas that have received much work already (e.g. digital divides, digital government, and privacy) or newer areas, like regulation of data-intensive technologies and platforms, the rise of precarious labour, and regulatory responses to misinformation and hate speech. We welcome innovative research in areas where the Internet already impacts public policy, where it raises new challenges or dilemmas, or provides opportunities for policy that is smart and equitable. While we welcome perspectives from any academic discipline, we look particularly for insight that can feed into social science disciplines like political science, public administration, economics, sociology, and communication. We welcome articles that introduce methodological innovation, theoretical development, or rigorous data analysis concerning a particular question or problem of public policy.