Optimizing Influence Campaigns: Nudging under Bounded Confidence

• URL: http://arxiv.org/abs/2503.18331v1
• Date: Mon, 24 Mar 2025 04:30:58 GMT
• Title: Optimizing Influence Campaigns: Nudging under Bounded Confidence
• Authors: Yen-Shao Chen, Tauhid Zaman,
• Abstract summary: We show that to persuade under bounded confidence, an agent must nudge its targets to gradually shift their opinions.<n>We show how to construct an agent's nudging policy under the bounded confidence opinion dynamics model.<n>We find that our nudging based policies outperform other common techniques that do not consider the bounded confidence effect.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Influence campaigns in online social networks are often run by organizations, political parties, and nation states to influence large audiences. These campaigns are employed through the use of agents in the network that share persuasive content. Yet, their impact might be minimal if the audiences remain unswayed, often due to the bounded confidence phenomenon, where only a narrow spectrum of viewpoints can influence them. Here we show that to persuade under bounded confidence, an agent must nudge its targets to gradually shift their opinions. Using a control theory approach, we show how to construct an agent's nudging policy under the bounded confidence opinion dynamics model and also how to select targets for multiple agents in an influence campaign on a social network. Simulations on real Twitter networks show that a multi-agent nudging policy can shift the mean opinion, decrease opinion polarization, or even increase it. We find that our nudging based policies outperform other common techniques that do not consider the bounded confidence effect. Finally, we show how to craft prompts for large language models, such as ChatGPT, to generate text-based content for real nudging policies. This illustrates the practical feasibility of our approach, allowing one to go from mathematical nudging policies to real social media content.

Related papers

• Spotting Persuasion: A Low-cost Model for Persuasion Detection in Political Ads on Social Media [5.54991381489437]
  We introduce a lightweight model for persuasive text detection that achieves state-of-the-art performance in Subtask 3 of SemEval 2023 Task 3.<n>Our study shows how subtleties can be found in persuasive political advertisements and presents a pragmatic approach to detect and analyze such strategies with limited resources.
  arXiv  Detail & Related papers  (2025-03-18T02:33:38Z)
• On the Use of Proxies in Political Ad Targeting [49.61009579554272]
  We show that major political advertisers circumvented mitigations by targeting proxy attributes. Our findings have crucial implications for the ongoing discussion on the regulation of political advertising.
  arXiv  Detail & Related papers  (2024-10-18T17:15:13Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
  We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries. We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
  arXiv  Detail & Related papers  (2023-04-19T18:32:49Z)
• Weakly Supervised Learning for Analyzing Political Campaigns on Facebook [24.29993132301275]
  We propose a weakly supervised approach to identify the stance and issue of political ads on Facebook. We analyze the temporal dynamics of the political ads on election polls.
  arXiv  Detail & Related papers  (2022-10-19T15:35:37Z)
• Personality-Driven Social Multimedia Content Recommendation [68.46899477180837]
  We investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations.
  arXiv  Detail & Related papers  (2022-07-25T14:37:18Z)
• Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version) [73.59962178534361]
  We study an influence problem in which little is assumed to be known about the diffusion network or about the model that determines how information may propagate. In this setting, an explore-exploit approach could be used to learn the key underlying diffusion parameters, while running the campaign. We describe and compare two methods of contextual multi-armed bandits, with upper-confidence bounds on the remaining potential of influencers.
  arXiv  Detail & Related papers  (2022-01-13T22:06:10Z)
• The Spread of Propaganda by Coordinated Communities on Social Media [43.2770127582382]
  We analyze the spread of propaganda and its interplay with coordinated behavior on a large Twitter dataset about the 2019 UK general election. The combination of the use of propaganda and coordinated behavior allows us to uncover the authenticity and harmfulness of the different communities.
  arXiv  Detail & Related papers  (2021-09-27T13:39:10Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)
• Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics [43.98568073610101]
  We use a social media model to quantify the impacts of several adversarial manipulation tactics on the quality of content. We find that the presence of influential accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
  arXiv  Detail & Related papers  (2019-07-13T21:12:08Z)

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.