Evaluating AI capabilities in detecting conspiracy theories on YouTube

• URL: http://arxiv.org/abs/2505.23570v2
• Date: Fri, 04 Jul 2025 14:02:59 GMT
• Title: Evaluating AI capabilities in detecting conspiracy theories on YouTube
• Abstract summary: This study explores the use of open-weight Large Language Models (LLMs), both text-only and multimodal, for identifying conspiracy theory videos on YouTube.<n>We evaluate a variety of LLMs in a zero-shot setting and compare their performance to a fine-tuned RoBERTa baseline.<n>Results show that text-based LLMs achieve high recall but lower precision, leading to increased false positives.<n>Multimodal models lag behind their text-only counterparts, indicating limited benefits from visual data integration.
• Score: 0.1474723404975345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a leading online platform with a vast global audience, YouTube's extensive reach also makes it susceptible to hosting harmful content, including disinformation and conspiracy theories. This study explores the use of open-weight Large Language Models (LLMs), both text-only and multimodal, for identifying conspiracy theory videos shared on YouTube. Leveraging a labeled dataset of thousands of videos, we evaluate a variety of LLMs in a zero-shot setting and compare their performance to a fine-tuned RoBERTa baseline. Results show that text-based LLMs achieve high recall but lower precision, leading to increased false positives. Multimodal models lag behind their text-only counterparts, indicating limited benefits from visual data integration. To assess real-world applicability, we evaluate the most accurate models on an unlabeled dataset, finding that RoBERTa achieves performance close to LLMs with a larger number of parameters. Our work highlights the strengths and limitations of current LLM-based approaches for online harmful content detection, emphasizing the need for more precise and robust systems.

Related papers

• Consistency Is the Key: Detecting Hallucinations in LLM Generated Text By Checking Inconsistencies About Key Facts [21.081815261690444]
  Large language models (LLMs) often hallucinate and generate text that is factually incorrect and not grounded in real-world knowledge.<n>This poses serious risks in domains like healthcare, finance, and customer support.<n>We introduce CONFACTCHECK, an efficient detection approach that does not leverage any external knowledge base.
  arXiv  Detail & Related papers  (2025-11-15T14:33:02Z)
• Can LLM Annotations Replace User Clicks for Learning to Rank? [112.2254432364736]
  Large-scale supervised data is essential for training modern ranking models, but obtaining high-quality human annotations is costly.<n>Click data has been widely used as a low-cost alternative, and with recent advances in large language models (LLMs), LLM-based relevance annotation has emerged as another promising annotation.<n> Experiments on both a public dataset, TianGong-ST, and an industrial dataset, Baidu-Click, show that click-supervised models perform better on high-frequency queries.<n>We explore two training strategies -- data scheduling and frequency-aware multi-objective learning -- that integrate both supervision signals.
  arXiv  Detail & Related papers  (2025-11-10T02:26:14Z)
• A Survey on Video Temporal Grounding with Multimodal Large Language Model [107.24431595873808]
  Recent advancement in temporal grounding (VTG) has significantly enhanced fine-grained video understanding.<n>With superior multimodal comprehension and reasoning abilities, VTG approaches based on MLLMs (VTG-MLLMs) are gradually surpassing traditional fine-tuned methods.<n>Despite extensive surveys on general video-language understanding, comprehensive reviews specifically addressing VTG-MLLMs remain scarce.
  arXiv  Detail & Related papers  (2025-08-07T08:52:11Z)
• Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers [59.168391398830515]
  We evaluate 12 pre-trained LLMs and one specialized fact-verifier, using a collection of examples from 14 fact-checking benchmarks.<n>We highlight the importance of addressing annotation errors and ambiguity in datasets.<n> frontier LLMs with few-shot in-context examples, often overlooked in previous works, achieve top-tier performance.
  arXiv  Detail & Related papers  (2025-06-16T10:32:10Z)
• LLM-based Semantic Augmentation for Harmful Content Detection [5.954202581988127]
  This paper introduces an approach that prompts large language models to clean noisy text and provide context-rich explanations.<n>We evaluate on the SemEval 2024 multi-label Persuasive Meme dataset and validate on the Google Jigsaw toxic comments and Facebook hateful memes datasets.<n>Our results reveal that zero-shot LLM classification underperforms on these high-context tasks compared to supervised models.
  arXiv  Detail & Related papers  (2025-04-22T02:59:03Z)
• Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection [22.804501061898616]
  Log Anomaly Detection (LAD) seeks to identify atypical patterns in log data that are crucial to assessing the security and condition of systems.<n>Although Large Language Models (LLMs) have shown tremendous success in various fields, the use of LLMs in enabling the detection of log anomalies is largely unexplored.<n>We explore the use of parameter-efficient fine-tuning techniques (PEFTs) for adapting LLMs to LAD.
  arXiv  Detail & Related papers  (2025-03-11T05:00:19Z)
• Protecting multimodal large language models against misleading visualizations [94.71976205962527]
  We introduce the first inference-time methods to improve performance on misleading visualizations.<n>We find that MLLM question-answering accuracy drops on average to the level of a random baseline.
  arXiv  Detail & Related papers  (2025-02-27T20:22:34Z)
• Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision [50.45597801390757]
  Instruct-LF is a goal-oriented latent factor discovery system.<n>It integrates instruction-following ability with statistical models to handle noisy datasets.
  arXiv  Detail & Related papers  (2025-02-21T02:03:08Z)
• FDLLM: A Dedicated Detector for Black-Box LLMs Fingerprinting [20.07438999071414]
  Large Language Models (LLMs) are rapidly transforming the landscape of digital content creation.<n>We present FD-Dataset, a comprehensive bilingual fingerprinting benchmark comprising 90,000 text samples from 20 famous proprietary and open-source LLMs.<n>We also present FDLLM, a novel fingerprinting method that leverages parameter-efficient Low-Rank Adaptation (LoRA) to fine-tune a foundation model.
  arXiv  Detail & Related papers  (2025-01-27T13:18:40Z)
• LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platforms [0.0]
  LLM-SEM (Language Model-Based Student Engagement Metric) is a novel approach that leverages video metadata and sentiment analysis of student comments to measure engagement.<n>We generate high-quality sentiment predictions to mitigate text fuzziness and normalize key features such as views and likes.<n>Our holistic method combines comprehensive metadata with sentiment polarity scores to gauge engagement at both the course and lesson levels.
  arXiv  Detail & Related papers  (2024-12-18T12:01:53Z)
• LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation [50.375567142250446]
  Large language models (LLMs), trained on vast corpora, have shown strong potential in data generation.<n>We propose a novel framework, LLM-Forest, which introduces a "forest" of few-shot prompt learning LLM "trees" with their outputs aggregated via confidence-based weighted voting.<n>This framework is established on a new concept of bipartite information graphs to identify high-quality relevant neighboring entries with both feature and value granularity.
  arXiv  Detail & Related papers  (2024-10-28T20:42:46Z)
• Scaling Up Video Summarization Pretraining with Large Language Models [73.74662411006426]
  We introduce an automated and scalable pipeline for generating a large-scale video summarization dataset. We analyze the limitations of existing approaches and propose a new video summarization model that effectively addresses them. Our work also presents a new benchmark dataset that contains 1200 long videos each with high-quality summaries annotated by professionals.
  arXiv  Detail & Related papers  (2024-04-04T11:59:06Z)
• Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance [51.30560006045442]
  Image-gRounded guIdaNcE (MARINE) is a framework that is both training-free and API-free.<n>MARINE effectively and efficiently reduces object hallucinations during inference by introducing image-grounded guidance to LVLMs.<n>Our framework's flexibility further allows for the integration of multiple vision models, enabling more reliable and robust object-level guidance.
  arXiv  Detail & Related papers  (2024-02-13T18:59:05Z)
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models [52.98743860365194]
  We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN) At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents.
  arXiv  Detail & Related papers  (2024-01-02T18:53:13Z)
• Video Understanding with Large Language Models: A Survey [97.29126722004949]
  Given the remarkable capabilities of large language models (LLMs) in language and multimodal tasks, this survey provides a detailed overview of recent advancements in video understanding.<n>The emergent capabilities Vid-LLMs are surprisingly advanced, particularly their ability for open-ended multi-granularity reasoning.<n>This survey presents a comprehensive study of the tasks, datasets, benchmarks, and evaluation methodologies for Vid-LLMs.
  arXiv  Detail & Related papers  (2023-12-29T01:56:17Z)
• Validating Large Language Models with ReLM [11.552979853457117]
  Large language models (LLMs) have been touted for their ability to generate natural-sounding text. There are growing concerns around possible negative effects of LLMs such as data memorization, bias, and inappropriate language. We introduce ReLM, a system for validating and querying LLMs using standard regular expressions.
  arXiv  Detail & Related papers  (2022-11-21T21:40:35Z)

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.