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量化网络公共广场对对抗性操纵策略的脆弱性。

Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics.

作者信息

Truong Bao Tran, Lou Xiaodan, Flammini Alessandro, Menczer Filippo

机构信息

Observatory on Social Media, Indiana University, 1015 E 11th St, Bloomington, IN 47408, USA.

出版信息

PNAS Nexus. 2024 Jun 29;3(7):pgae258. doi: 10.1093/pnasnexus/pgae258. eCollection 2024 Jul.

DOI:10.1093/pnasnexus/pgae258
PMID:38994499
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11238850/
Abstract

Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of adversarial manipulation tactics on the quality of content. We find that the presence of hub accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.

摘要

社交媒体在一些人眼中被视为现代公共广场,容易受到操纵。通过控制冒充人类的虚假账户,恶意行为者可以在目标社区内放大虚假信息。由于收集数据以及开展会影响在线社区的伦理实验存在挑战,此类操作的后果难以评估。在此,我们使用一种社交媒体模型,该模型在经验网络中模拟信息传播,以量化对抗性操纵策略对内容质量的影响。我们发现,作为社交媒体标志之一的枢纽账户的存在,加剧了在线社区易受操纵的脆弱性。在不良行为者可采用的诸多策略中,渗透进一个社区最有可能使低质量内容迅速传播。虚假行为者用低质量但吸引人的内容充斥网络会进一步加剧这种危害,但当不良行为者将重点放在特定目标(如具有影响力或易受影响的个人)上时,危害会减轻。这些见解为平台提供了一些应对措施,可用来增强社交媒体用户抵御操纵的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/b8db83362af7/pgae258f7.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/d36ac6bd1609/pgae258f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/4e233cc98796/pgae258f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/fd456cebee17/pgae258f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/b8db83362af7/pgae258f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/d12a5ee372f9/pgae258f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/a08bee90a923/pgae258f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/302ffbc45680/pgae258f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/d36ac6bd1609/pgae258f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/4e233cc98796/pgae258f5.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6426/11238850/b8db83362af7/pgae258f7.jpg

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