Analyzing Russia's propaganda tactics on Twitter using mixed methods network analysis and natural language processing: a case study of the 2022 invasion of Ukraine

被引:0
|
作者
Alieva, Iuliia [1 ]
Kloo, Ian [1 ]
Carley, Kathleen M. [1 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
基金
美国安德鲁·梅隆基金会;
关键词
Social Network Analysis; Natural Language Processing; BERTopic; Disinformation; Russia; Ukraine; Computational Propaganda;
D O I
10.1140/epjds/s13688-024-00479-w
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This paper examines Russia's propaganda discourse on Twitter during the 2022 invasion of Ukraine. The study employs network analysis, natural language processing (NLP) techniques, and qualitative analysis to identify key communities and narratives associated with the prevalent and damaging narrative of "fascism/Nazism" in discussions related to the invasion. The paper implements a methodological pipeline to identify the main topics, and influential actors, as well as to examine the most impactful messages in spreading this disinformation narrative. Overall, this research contributes to the understanding of propaganda dissemination on social media platforms and provides insights into the narratives and communities involved in spreading disinformation during the invasion.
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页数:13
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