Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls

• URL: http://arxiv.org/abs/2210.08786v6
• Date: Wed, 11 Oct 2023 07:37:01 GMT
• Title: Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls
• Authors: Fatima Ezzeddine and Luca Luceri and Omran Ayoub and Ihab Sbeity and Gianluca Nogara and Emilio Ferrara and Silvia Giordano
• Abstract summary: Detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge. We propose a new AI-based solution that identifies troll accounts solely through behavioral cues associated with their sequences of sharing activity.
• Score: 8.202465737306222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Beyond Trial-and-Error: Predicting User Abandonment After a Moderation Intervention [0.6918368994425961]
  We propose and tackle the novel task of predicting the effect of a moderation intervention on Reddit. We use a dataset of 13.8M posts to compute a set of 142 features, which convey information about the activity, toxicity, relations, and writing style of the users. Our results demonstrate the feasibility of predicting the effects of a moderation intervention, paving the way for a new research direction in predictive content moderation.
  arXiv  Detail & Related papers  (2024-04-23T08:52:41Z)
• Debiasing Multimodal Large Language Models [61.6896704217147]
  Large Vision-Language Models (LVLMs) have become indispensable tools in computer vision and natural language processing. Our investigation reveals a noteworthy bias in the generated content, where the output is primarily influenced by the underlying Large Language Models (LLMs) prior to the input image. To rectify these biases and redirect the model's focus toward vision information, we introduce two simple, training-free strategies.
  arXiv  Detail & Related papers  (2024-03-08T12:35:07Z)
• Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets [92.38654521870444]
  We introduce ACES, a contrastive challenge set spanning 146 language pairs. This dataset aims to discover whether metrics can identify 68 translation accuracy errors. We conduct a large-scale study by benchmarking ACES on 50 metrics submitted to the WMT 2022 and 2023 metrics shared tasks.
  arXiv  Detail & Related papers  (2024-01-29T17:17:42Z)
• Leveraging Large Language Models to Detect Influence Campaigns in Social Media [9.58546889761175]
  Social media influence campaigns pose significant challenges to public discourse and democracy. Traditional detection methods fall short due to the complexity and dynamic nature of social media. We propose a novel detection method using Large Language Models (LLMs) that incorporates both user metadata and network structures.
  arXiv  Detail & Related papers  (2023-11-14T00:25:09Z)
• Stance Detection with Collaborative Role-Infused LLM-Based Agents [39.75103353173015]
  Stance detection is vital for content analysis in web and social media research. However, stance detection requires advanced reasoning to infer authors' implicit viewpoints. We design a three-stage framework in which LLMs are designated distinct roles. We achieve state-of-the-art performance across multiple datasets.
  arXiv  Detail & Related papers  (2023-10-16T14:46:52Z)
• Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models [31.509994889286183]
  We introduce Language Agent Tree Search (LATS) -- the first general framework that synergizes the capabilities of language models (LMs) in reasoning, acting, and planning. A key feature of our approach is the incorporation of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism. LATS achieves state-of-the-art pass@1 accuracy (92.7%) for programming on HumanEval with GPT-4 and demonstrates gradient-free performance (average score of 75.9) comparable to gradient-based fine-tuning for web navigation on WebShop with GPT
  arXiv  Detail & Related papers  (2023-10-06T17:55:11Z)
• Pushing the Limits of ChatGPT on NLP Tasks [79.17291002710517]
  Despite the success of ChatGPT, its performances on most NLP tasks are still well below the supervised baselines. In this work, we looked into the causes, and discovered that its subpar performance was caused by the following factors. We propose a collection of general modules to address these issues, in an attempt to push the limits of ChatGPT on NLP tasks.
  arXiv  Detail & Related papers  (2023-06-16T09:40:05Z)
• ReAct: Synergizing Reasoning and Acting in Language Models [44.746116256516046]
  We show that large language models (LLMs) can generate both reasoning traces and task-specific actions in an interleaved manner. We apply our approach, named ReAct, to a diverse set of language and decision making tasks. ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API.
  arXiv  Detail & Related papers  (2022-10-06T01:00:32Z)
• MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning [65.52675802289775]
  We show that an uncertainty aware classifier can solve challenging reinforcement learning problems. We propose a novel method for computing the normalized maximum likelihood (NML) distribution. We show that the resulting algorithm has a number of intriguing connections to both count-based exploration methods and prior algorithms for learning reward functions.
  arXiv  Detail & Related papers  (2021-07-15T08:19:57Z)
• Learning to Infer User Hidden States for Online Sequential Advertising [52.169666997331724]
  We propose our Deep Intents Sequential Advertising (DISA) method to address these issues. The key part of interpretability is to understand a consumer's purchase intent which is, however, unobservable (called hidden states)
  arXiv  Detail & Related papers  (2020-09-03T05:12:26Z)
• Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders [62.54431888432302]
  We study an OPE problem in an infinite-horizon, ergodic Markov decision process with unobserved confounders. We show how, given only a latent variable model for states and actions, policy value can be identified from off-policy data.
  arXiv  Detail & Related papers  (2020-07-27T22:19:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.