Utilising Large Language Models for Generating Effective Counter Arguments to Anti-Vaccine Tweets

• URL: http://arxiv.org/abs/2510.16359v1
• Date: Sat, 18 Oct 2025 05:44:39 GMT
• Title: Utilising Large Language Models for Generating Effective Counter Arguments to Anti-Vaccine Tweets
• Authors: Utsav Dhanuka, Soham Poddar, Saptarshi Ghosh,
• Abstract summary: Misleading narratives around vaccination have spread widely, creating barriers to achieving high immunisation rates and undermining trust in health recommendations.<n>We explore the capabilities of LLMs to generate sound counter-argument rebuttals to vaccine misinformation.<n>Our findings demonstrate that integrating label descriptions and structured fine-tuning enhances counter-argument effectiveness.
• Score: 4.249511043531375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In an era where public health is increasingly influenced by information shared on social media, combatting vaccine skepticism and misinformation has become a critical societal goal. Misleading narratives around vaccination have spread widely, creating barriers to achieving high immunisation rates and undermining trust in health recommendations. While efforts to detect misinformation have made significant progress, the generation of real time counter-arguments tailored to debunk such claims remains an insufficiently explored area. In this work, we explore the capabilities of LLMs to generate sound counter-argument rebuttals to vaccine misinformation. Building on prior research in misinformation debunking, we experiment with various prompting strategies and fine-tuning approaches to optimise counter-argument generation. Additionally, we train classifiers to categorise anti-vaccine tweets into multi-labeled categories such as concerns about vaccine efficacy, side effects, and political influences allowing for more context aware rebuttals. Our evaluation, conducted through human judgment, LLM based assessments, and automatic metrics, reveals strong alignment across these methods. Our findings demonstrate that integrating label descriptions and structured fine-tuning enhances counter-argument effectiveness, offering a promising approach for mitigating vaccine misinformation at scale.

Related papers

• The Shifting Landscape of Vaccine Discourse: Insights From a Decade of Pre- to Post-COVID-19 Vaccine Posts on Social Media [61.575555311964706]
  We analyze how English speakers talk about vaccines on social media to understand the evolving narrative around vaccines in social media posts.<n>We first introduce a novel dataset comprising 18.7 million curated posts on vaccine discourse from 2013 to 2022.<n>Our analysis shows that the COVID-19 pandemic led to complex shifts in X users' sentiment and discourse around vaccines.
  arXiv  Detail & Related papers  (2025-11-20T22:28:59Z)
• Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring [50.164756034797136]
  Dropout is common in clinical studies, with up to half of patients leaving early due to side effects or other reasons.<n>When dropout is informative, it introduces censoring bias, because of which treatment effect estimates are also biased.<n>We propose an assumption-lean framework to assess the robustness of conditional average treatment effect estimates in survival analysis when facing censoring bias.
  arXiv  Detail & Related papers  (2025-10-15T10:51:17Z)
• VaxPulse: Monitoring of Online Public Concerns to Enhance Post-licensure Vaccine Surveillance [0.3958317527488535]
  We describe how we enhanced the reporting surveillance system of Victoria's vaccine safety service, SAEFVIC.<n>Using VaxPulse, a multi-step framework, we integrate adverse events following immunisation with sentiment analysis.<n>We emphasise the need to address non-English languages to stratify concerns across ethno-lingual communities.
  arXiv  Detail & Related papers  (2025-07-07T04:18:08Z)
• MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks [104.50239783909063]
  Multimodal large language models with Retrieval Augmented Generation (RAG) have significantly advanced tasks such as multimodal question answering.<n>This reliance on external knowledge poses a critical yet underexplored safety risk: knowledge poisoning attacks.<n>We propose MM-PoisonRAG, the first framework to systematically design knowledge poisoning in multimodal RAG.
  arXiv  Detail & Related papers  (2025-02-25T04:23:59Z)
• Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social Media [0.0]
  The recent COVID-19 pandemic has highlighted how vaccines play a crucial role in keeping us safe. The paper addresses the challenge of comprehensively understanding and categorizing these diverse concerns expressed in the context of vaccination. Our focus is on developing a robust multi-label of assigning specific concern labels tweets based on the articulated apprehensions towards vaccines.
  arXiv  Detail & Related papers  (2023-12-17T06:55:04Z)
• Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification [60.49594822215981]
  This paper presents a classification model for detecting COVID-19 vaccination related search queries. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task.
  arXiv  Detail & Related papers  (2022-12-16T13:57:41Z)
• Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy among Healthcare Workers [64.1526243118151]
  We find that doctors are overall more positive toward the COVID-19 vaccines. Doctors are more concerned with the effectiveness of the vaccines over newer variants. Nurses pay more attention to the potential side effects on children.
  arXiv  Detail & Related papers  (2022-09-11T14:22:16Z)
• Dynamics and triggers of misinformation on vaccines [0.552480439325792]
  We analyze 6 years of Italian vaccine debate across diverse social media platforms (Facebook, Instagram, Twitter, YouTube) We first use the symbolic transfer entropy analysis of news production time-series to determine which category of sources, questionable or reliable, causally drives the agenda on vaccines. We then leverage deep learning models capable to accurately classify vaccine-related content based on the conveyed stance and discussed topic.
  arXiv  Detail & Related papers  (2022-07-25T15:35:48Z)
• Insta-VAX: A Multimodal Benchmark for Anti-Vaccine and Misinformation Posts Detection on Social Media [32.252687203366605]
  Anti-vaccine posts on social media have been shown to create confusion and reduce the publics confidence in vaccines. Insta-VAX is a new multi-modal dataset consisting of a sample of 64,957 Instagram posts related to human vaccines.
  arXiv  Detail & Related papers  (2021-12-15T20:34:57Z)
• Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU [9.8215089151757]
  Vaccine hesitancy and refusal can create clusters of low vaccine coverage and reduce the effectiveness of vaccination programs. Social media provides an opportunity to estimate emerging risks to vaccine acceptance by including geographical location and detailing vaccine-related concerns. Methods for classifying social media posts, such as vaccine-related tweets, use language models (LMs) trained on general domain text. We present a novel end-to-end framework consisting of interconnected components that use domain-specific LM trained on vaccine-related tweets and models commonsense knowledge into a bidirectional gated recurrent network (CK-BiGRU) with context-aware attention.
  arXiv  Detail & Related papers  (2021-06-17T15:16:08Z)
• Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News [57.9843300852526]
  We introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset composed of 4 different types of generated articles. In addition to the valuable insights gleaned from our user study experiments, we provide a relatively effective approach based on detecting visual-semantic inconsistencies.
  arXiv  Detail & Related papers  (2020-09-16T14:13:15Z)
• Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter [65.7192861893042]
  We examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant. We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct "echo chambers" At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers.
  arXiv  Detail & Related papers  (2020-03-26T13:55:50Z)

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.