Coordinated Behavior in Information Operations on Twitter

被引:2
作者
Cima, Lorenzo [1 ,2 ]
Mannocci, Lorenzo [1 ,2 ]
Avvenuti, Marco [1 ]
Tesconi, Maurizio [2 ]
Cresci, Stefano [2 ]
机构
[1] Univ Pisa, Dept Informat Engn, I-56126 Pisa, Italy
[2] Inst Informat & Telemat IIT, Natl Res Council CNR, I-00185 Rome, Italy
关键词
Social networking (online); Blogs; Chatbots; Task analysis; Media; Machine learning; Information integrity; Behavioral sciences; Fake news; Coordinated behavior; information operations; disinformation; Twitter;
D O I
10.1109/ACCESS.2024.3393482
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Online information operations (IOs) refer to organized attempts to tamper with the regular flow of information and to influence public opinion. Coordinated online behavior is a tactic frequently used by IO perpetrators to boost the spread and outreach of their messages. However, the exploitation of coordinated behavior within large-scale IOs is still largely unexplored. Here, we build a novel dataset comprising around 624K users and 4M tweets to study how online coordination was used in two recent IOs carried out on Twitter. We investigate the interplay between coordinated behavior and IOs with state-of-the-art network science and coordination detection methods, providing evidence that the perpetrators of both IOs were indeed strongly coordinated. Furthermore, we propose quantitative indicators and analyses to study the different patterns of coordination, uncovering a malicious group of users that managed to hold a central position in the discussion network, and others who remained at the periphery of the network, with limited interactions with genuine users. The nuanced results enabled by our analysis provide insights into the strategies, development, and effectiveness of the IOs. Overall, our results demonstrate that the analysis of coordinated behavior in IOs can contribute to safeguarding the integrity of online platforms.
引用
收藏
页码:61568 / 61585
页数:18
相关论文
共 85 条
  • [1] Combining advanced computational social science and graph theoretic techniques to reveal adversarial information operations
    Alassad, Mustafa
    Spann, Billy
    Agarwal, Nitin
    [J]. INFORMATION PROCESSING & MANAGEMENT, 2021, 58 (01)
  • [2] How disinformation operations against Russian opposition leader Alexei Navalny influence the international audience on Twitter
    Alieva, Iuliia
    Moffitt, J. D.
    Carley, Kathleen M.
    [J]. SOCIAL NETWORK ANALYSIS AND MINING, 2022, 12 (01)
  • [3] Alieva Iuliia, 2022, 2022 IEEE INT C BIG, P1770, DOI [10.1109/BigData55660.2022.10020223, DOI 10.1109/BIGDATA55660.2022.10020223]
  • [4] Content-based features predict social media influence operations
    Alizadeh, Meysam
    Shapiro, Jacob N.
    Buntain, Cody
    Tucker, Joshua A.
    [J]. SCIENCE ADVANCES, 2020, 6 (30)
  • [5] [Anonymous], 2017, P INT AAAI C WEB SOC
  • [6] [Anonymous], 2015, ACM CIKM
  • [7] Arif Ahmer, 2018, Proceedings of the ACM on Human-Computer Interaction, V2, DOI 10.1145/3274289
  • [8] Assenmacher L., 2020, AAAI FLAIRS, P303
  • [9] Network science
    Barabasi, Albert-Laszlo
    [J]. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2013, 371 (1987):
  • [10] "Donald Trump Is My President!": The Internet Research Agency Propaganda Machine
    Bastos, Marco
    Farkas, Johan
    [J]. SOCIAL MEDIA + SOCIETY, 2019, 5 (03): : 1 - 13