Characterizing the Internet Research Agency’s Social Media Operations During the 2016 U.S. Presidential Election using Linguistic Analyses
Characterizing the Internet Research Agency’s Social Media Operations During the 2016 U.S. Presidential Election using Linguistic Analyses
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使用语言分析描述 2016 年美国总统选举期间互联网研究机构的社交媒体运作
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
E. Horvitz
中科院分区：
文献类型：
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作者：
Ryan L. Boyd;Alexander Spangher;Adam Fourney;Besmira Nushi;G. Ranade;J. Pennebaker;E. Horvitz
多个机构/代理人的调查为俄罗斯干涉2016年美国总统大选提供了压倒性的证据。作为最近报告的一部分（和结果），捕捉互联网研究机构（伊拉）行为者所采取行动的多个数据集已向公众发布。在目前的论文中，我们介绍了几个初步的法医分析报告，这些分析是由伊拉在选举周期中运行的Facebook广告数据和Twitter巨魔账户。通过使用语言分析，我们的特点的演变过程中的选举周期的伊拉的内容，提供了一个基础，了解如何左倾和右倾的意识形态有区别地针对美国选民之间传播敌意。此外，通过句法结构的分析，我们发现，由爱尔兰伊拉在Twitter上产生的内容是语言上的唯一从一个控制样本的英语Twitter帐户。总的来说，我们的调查结果表明，伊拉的行动在很大程度上是简单的和“低预算”的性质，没有认真的尝试在原产地混淆正在采取。
Converging investigations on the part of multiple agencies/agents have provided overwhelming evidence for Russian interference in the 2016 U.S. presidential election. As a part (and consequence) of recent reports, multiple datasets that capture actions taken by actors of the Internet Research Agency (IRA), have been released to the public. In the cur-rent paper, we present and abridged report of several preliminary forensic analyses of Facebook ad data and Twitter troll accounts that were run by the IRA during the election cycle. Through the use of language analysis, we characterize the evolution of IRA content over the course of the election cycle, providing a basis for understanding how left- and right-leaning ideologies were differentially targeted to spread enmity among the American electorate. Additionally, through an analysis of syntactic constructions, we find that the content produced by the IRA on Twitter was linguistically unique from a control sample of English-speaking Twitter accounts. Altogether, our findings suggest that the IRA’s operations were largely unsophisticated and “low-budget” in nature, with no serious attempts at point-of-origin obfuscation being taken.
俄罗斯新闻的框架和议程设置：复杂政治策略的计算分析
DOI：
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发表时间：
2018
期刊：
2018 Conference on Empirical Methods in Natural Language Processing (EMNLP
影响因子：
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作者：
Field, Anjalie;Kliger, Doron;Wintner, Shuly;Pan, Jennifer;Jurafsky, Dan;Tsvetkov, Yulia
通讯作者：
Tsvetkov, Yulia
影响因子：
3.3
作者：
Chung, Cindy K.;Pennebaker, James W.
通讯作者：
Pennebaker, James W.