Forty Thousand Fake Twitter Profiles: A Computational Framework for the Visual Analysis of Social Media Propaganda

IF 3 2区社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Social Science Computer Review Pub Date : 2024-08-02 DOI:10.1177/08944393241269394

Noel George, Azhar Sham, Thanvi Ajith, Marco Bastos

{"title":"Forty Thousand Fake Twitter Profiles: A Computational Framework for the Visual Analysis of Social Media Propaganda","authors":"Noel George, Azhar Sham, Thanvi Ajith, Marco Bastos","doi":"10.1177/08944393241269394","DOIUrl":null,"url":null,"abstract":"Successful disinformation campaigns depend on the availability of fake social media profiles used for coordinated inauthentic behavior with networks of false accounts including bots, trolls, and sockpuppets. This study presents a scalable and unsupervised framework to identify visual elements in user profiles strategically exploited in nearly 60 influence operations, including camera angle, photo composition, gender, and race, but also more context-dependent categories like sensuality and emotion. We leverage Google’s Teachable Machine and the DeepFace Library to classify fake user accounts in the Twitter Moderation Research Consortium database, a large repository of social media accounts linked to foreign influence operations. We discuss the performance of these classifiers against manually coded data and their applicability in large-scale data analysis. The proposed framework demonstrates promising results for the identification of fake online profiles used in influence operations and by the cottage industry specialized in crafting desirable online personas.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"75 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social Science Computer Review","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1177/08944393241269394","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

Abstract

Successful disinformation campaigns depend on the availability of fake social media profiles used for coordinated inauthentic behavior with networks of false accounts including bots, trolls, and sockpuppets. This study presents a scalable and unsupervised framework to identify visual elements in user profiles strategically exploited in nearly 60 influence operations, including camera angle, photo composition, gender, and race, but also more context-dependent categories like sensuality and emotion. We leverage Google’s Teachable Machine and the DeepFace Library to classify fake user accounts in the Twitter Moderation Research Consortium database, a large repository of social media accounts linked to foreign influence operations. We discuss the performance of these classifiers against manually coded data and their applicability in large-scale data analysis. The proposed framework demonstrates promising results for the identification of fake online profiles used in influence operations and by the cottage industry specialized in crafting desirable online personas.

查看原文本刊更多论文

四万个虚假 Twitter 简介：社交媒体宣传可视化分析的计算框架

成功的造谣活动依赖于虚假社交媒体资料的可用性，这些资料被用于与虚假账户网络（包括机器人、巨魔和 sockpuppets）协调不真实行为。本研究提出了一个可扩展的无监督框架，用于识别用户资料中的视觉元素，这些元素在近 60 次影响行动中被策略性地利用，包括拍摄角度、照片构图、性别和种族，以及感性和情感等更多依赖于上下文的类别。我们利用谷歌的 "可教机器"（Teachable Machine）和 DeepFace 库，对 Twitter 节制研究联盟数据库（Twitter Moderation Research Consortium）中的虚假用户账户进行分类，该数据库是一个与外国影响力行动相关的大型社交媒体账户库。我们讨论了这些分类器在人工编码数据方面的性能及其在大规模数据分析中的适用性。所提出的框架在识别影响行动中使用的虚假在线配置文件以及专门制作理想在线角色的山寨产业方面取得了可喜的成果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Social Science Computer Review 社会科学-计算机：跨学科应用

CiteScore

9.00

自引率

4.90%

发文量

审稿时长

>12 weeks

期刊介绍： Unique Scope Social Science Computer Review is an interdisciplinary journal covering social science instructional and research applications of computing, as well as societal impacts of informational technology. Topics included: artificial intelligence, business, computational social science theory, computer-assisted survey research, computer-based qualitative analysis, computer simulation, economic modeling, electronic modeling, electronic publishing, geographic information systems, instrumentation and research tools, public administration, social impacts of computing and telecommunications, software evaluation, world-wide web resources for social scientists. Interdisciplinary Nature Because the Uses and impacts of computing are interdisciplinary, so is Social Science Computer Review. The journal is of direct relevance to scholars and scientists in a wide variety of disciplines. In its pages you''ll find work in the following areas: sociology, anthropology, political science, economics, psychology, computer literacy, computer applications, and methodology.