揭露LLM时代的影响力活动：基于行为的人工智能方法检测国家赞助的巨魔
Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls
AI总结：
本研究提出一种基于行为线索（如分享序列和反馈）的AI方法，通过LSTM分类器和“巨魔评分”检测国家赞助巨魔，在2016年俄罗斯干预活动中实现高准确率，且不易被LLM模仿。
摘要：
检测在社交媒体影响力活动中运作的国家赞助巨魔，是研究界面临的一个关键且尚未解决的挑战，其影响远超网络领域。为应对这一挑战，我们提出了一种新的人工智能解决方案，该方案仅通过与其分享活动序列相关的行为线索来识别巨魔账户，涵盖其行为及他人对其的反馈。我们的方法不纳入任何共享的文本内容，包含两个步骤：首先，我们利用基于LSTM的分类器来确定账户序列是否属于国家赞助巨魔或有机的合法用户。其次，我们使用分类后的序列来计算一个名为“巨魔评分”的指标，量化账户表现出巨魔般行为的程度。为评估我们方法的有效性，我们在2016年美国总统大选期间的俄罗斯干预活动的背景下检验其性能。我们的实验产生了令人信服的结果，表明我们的方法能以接近99%的AUC识别账户序列，并以91%的AUC准确区分俄罗斯巨魔和有机用户。值得注意的是，在文本和语言属性可被大型语言模型（LLM）轻易模仿的不断演变的环境中，我们的基于行为的方法具有显著优势：与现有的基于语言的技术相比，它依赖于更难复制的行为线索，确保在识别影响力活动方面具有更强的韧性，尤其是考虑到LLM用于生成不真实内容的潜在增加。最后，我们评估了我们的解决方案对驱动不同信息操作的各种实体的泛化能力，并发现了有希望的结果，这将指导未来的研究。
英文摘要：
The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm. To address this challenge, we propose a new AI-based solution that identifies troll accounts solely through behavioral cues associated with their sequences of sharing activity, encompassing both their actions and the feedback they receive from others. Our approach does not incorporate any textual content shared and consists of two steps: First, we leverage an LSTM-based classifier to determine whether account sequences belong to a state-sponsored troll or an organic, legitimate user. Second, we employ the classified sequences to calculate a metric named the "Troll Score", quantifying the degree to which an account exhibits troll-like behavior. To assess the effectiveness of our method, we examine its performance in the context of the 2016 Russian interference campaign during the U.S. Presidential election. Our experiments yield compelling results, demonstrating that our approach can identify account sequences with an AUC close to 99% and accurately differentiate between Russian trolls and organic users with an AUC of 91%. Notably, our behavioral-based approach holds a significant advantage in the ever-evolving landscape, where textual and linguistic properties can be easily mimicked by Large Language Models (LLMs): In contrast to existing language-based techniques, it relies on more challenging-to-replicate behavioral cues, ensuring greater resilience in identifying influence campaigns, especially given the potential increase in the usage of LLMs for generating inauthentic content. Finally, we assessed the generalizability of our solution to various entities driving different information operations and found promising results that will guide future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
