Disrupting Networks: Amplifying Social Dissensus via Opinion Perturbation and Large Language Models
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914126748450816 |
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| author | Coppolillo, Erica Manco, Giuseppe |
| author_facet | Coppolillo, Erica Manco, Giuseppe |
| contents | We study how targeted content injection can strategically disrupt social networks. Using the Friedkin-Johnsen (FJ) model, we utilize a measure of social dissensus and show that (i) simple FJ variants cannot significantly perturb the network, (ii) extending the model enables valid graph structures where disruption at equilibrium exceeds the initial state, and (iii) altering an individual's inherent opinion can maximize disruption. Building on these insights, we design a reinforcement learning framework to fine-tune a Large Language Model (LLM) for generating disruption-oriented text. Experiments on synthetic and real-world data confirm that tuned LLMs can approach theoretical disruption limits. Our findings raise important considerations for content moderation, adversarial information campaigns, and generative model regulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_27152 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Disrupting Networks: Amplifying Social Dissensus via Opinion Perturbation and Large Language Models Coppolillo, Erica Manco, Giuseppe Social and Information Networks We study how targeted content injection can strategically disrupt social networks. Using the Friedkin-Johnsen (FJ) model, we utilize a measure of social dissensus and show that (i) simple FJ variants cannot significantly perturb the network, (ii) extending the model enables valid graph structures where disruption at equilibrium exceeds the initial state, and (iii) altering an individual's inherent opinion can maximize disruption. Building on these insights, we design a reinforcement learning framework to fine-tune a Large Language Model (LLM) for generating disruption-oriented text. Experiments on synthetic and real-world data confirm that tuned LLMs can approach theoretical disruption limits. Our findings raise important considerations for content moderation, adversarial information campaigns, and generative model regulation. |
| title | Disrupting Networks: Amplifying Social Dissensus via Opinion Perturbation and Large Language Models |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2510.27152 |