Adversarial information operations can destabilize societies by undermining
fair elections, manipulating public opinions on policies, and promoting scams.
Despite their widespread occurrence and potential impacts, our understanding of
influence campaigns is limited by manual analysis of messages and subjective
interpretation of their observable behavior. In this paper, we explore whether
these limitations can be mitigated with large language models (LLMs), using
GPT-3.5 as a case-study for coordinated campaign annotation. We first use
GPT-3.5 to scrutinize 126 identified information operations spanning over a
decade. We utilize a number of metrics to quantify the close (if imperfect)
agreement between LLM and ground truth descriptions. We next extract
coordinated campaigns from two large multilingual datasets from X (formerly
Twitter) that respectively discuss the 2022 French election and 2023 Balikaran
Philippine-U.S. military exercise in 2023. For each coordinated campaign, we
use GPT-3.5 to analyze posts related to a specific concern and extract goals,
tactics, and narrative frames, both before and after critical events (such as
the date of an election). While the GPT-3.5 sometimes disagrees with subjective
interpretation, its ability to summarize and interpret demonstrates LLMs'
potential to extract higher-order indicators from text to provide a more
complete picture of the information campaigns compared to previous methods.

使用大型语言模型 GPT-3.5 来分析对抗性信息操作，协调性运动以及提取高阶指标，以提供比先前方法更完整的信息运动图像。

大型语言模型揭示信息操作的目标、策略和叙事框架

Large Language Models Reveal Information Operation Goals, Tactics, and  Narrative Frames

We propose a novel clustering pipeline to detect and characterize influence
campaigns from documents. This approach clusters parts of document, detects
clusters that likely reflect an influence campaign, and then identifies
documents linked to an influence campaign via their association with the
high-influence clusters. Our approach outperforms both the direct
document-level classification and the direct document-level clustering approach
in predicting if a document is part of an influence campaign. We propose
various novel techniques to enhance our pipeline, including using an existing
event factuality prediction system to obtain document parts, and aggregating
multiple clustering experiments to improve the performance of both cluster and
document classification. Classifying documents on the top of clustering not
only accurately extracts the parts of the documents that are relevant to
influence campaigns, but also capture influence campaigns as a coordinated and
holistic phenomenon. Our approach makes possible more fine-grained and
interpretable characterizations of influence campaigns from documents.

我们提出了一种新颖的聚类流程来检测和表征文档中的影响力运动。该方法通过聚类文档的部分，检测反映影响力运动的聚类，并通过它们与高影响力聚类的关联识别与影响力运动相关的文档。我们的方法在预测文档是否属于影响力运动方面优于直接的基于文档级别的分类和聚类方法。我们提出了各种新技术来增强我们的流程，包括使用现有的事件真实性预测系统获取文档部分，并聚合多个聚类实验来改进聚类和文档分类的性能。在聚类的基础上对文档进行分类不仅可以精确提取与影响力运动相关的文档部分，而且将影响力运动作为一种协调和整体的现象来捕捉。我们的方法使得从文档中更细粒度、可解释性更强地表征影响力运动成为可能。

文档部分聚类：从文档中检测和描述影响运动

Clustering Document Parts: Detecting and Characterizing Influence  Campaigns From Documents

State-sponsored trolls are the main actors of influence campaigns on social
media and automatic troll detection is important to combat misinformation at
scale. Existing troll detection models are developed based on training data for
known campaigns (e.g.\ the influence campaign by Russia's Internet Research
Agency on the 2016 US Election), and they fall short when dealing with {\em
novel} campaigns with new targets. We propose MetaTroll, a text-based troll
detection model based on the meta-learning framework that enables high
portability and parameter-efficient adaptation to new campaigns using only a
handful of labelled samples for few-shot transfer. We introduce
\textit{campaign-specific} transformer adapters to MetaTroll to ``memorise''
campaign-specific knowledge so as to tackle catastrophic forgetting, where a
model ``forgets'' how to detect trolls from older campaigns due to continual
adaptation. Our experiments demonstrate that MetaTroll substantially
outperforms baselines and state-of-the-art few-shot text classification models.
Lastly, we explore simple approaches to extend MetaTroll to multilingual and
multimodal detection. Source code for MetaTroll is available at:
this https URL.

MetaTroll 是一种基于元学习框架的文本模型，利用少量标注样本进行 few-shot 转移，通过引入 “特定于活动” 的转换器适配器以针对新的活动目标，可以防止模型因为不断适应而忘记如何检测老活动中的恶意用户。实验表明，MetaTroll 明显优于基线和最先进的几种少样本文本分类模型，并且可以扩展到多语言和多模态检测。

MetaTroll: 基于 Transformer Adapter 的国家支持的 Troll 的小样本检测

MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters

Coordinated campaigns are used to influence and manipulate social media
platforms and their users, a critical challenge to the free exchange of
information online. Here we introduce a general, unsupervised network-based
methodology to uncover groups of accounts that are likely coordinated. The
proposed method constructs coordination networks based on arbitrary behavioral
traces shared among accounts. We present five case studies of influence
campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong
protests, the Syrian civil war, and cryptocurrency manipulation. In each of
these cases, we detect networks of coordinated Twitter accounts by examining
their identities, images, hashtag sequences, retweets, or temporal patterns.
The proposed approach proves to be broadly applicable to uncover different
kinds of coordination across information warfare scenarios.

提出了一种基于网络的无监督通用方法来揭示协调账户群，经过五个案例研究，证明了该方法可以广泛应用于不同的信息战争场景和协调文化。