BotSim: Mitigating The Formation Of Conspiratorial Societies with Useful Bots
- URL: http://arxiv.org/abs/2601.06154v1
- Date: Tue, 06 Jan 2026 06:26:46 GMT
- Title: BotSim: Mitigating The Formation Of Conspiratorial Societies with Useful Bots
- Authors: Lynnette Hui Xian Ng, Kathleen M. Carley,
- Abstract summary: We create BotSim, an Agent-Based Model of a society in which useful bots are introduced into a small world network.<n>These useful bots are: Info-Correction Bots, which correct bad information into good, and Good Bots, which put out good messaging.<n>Our results show that, left unchecked, Bad Bots can create a conspiratorial society, and this can be mitigated by either Info-Correction Bots or Good Bots.
- Score: 2.2032950166846526
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Societies can become a conspiratorial society where there is a majority of humans that believe, and therefore spread, conspiracy theories. Artificial intelligence gave rise to social media bots that can spread conspiracies in an automated fashion. Currently, organizations combat the spread of conspiracies through manual fact-checking processes and the dissemination of counter-narratives. However, the effects of harnessing the same automation to create useful bots are not well explored. To address this, we create BotSim, an Agent-Based Model of a society in which useful bots are introduced into a small world network. These useful bots are: Info-Correction Bots, which correct bad information into good, and Good Bots, which put out good messaging. The simulated agents interact through generating, consuming and propagating information. Our results show that, left unchecked, Bad Bots can create a conspiratorial society, and this can be mitigated by either Info-Correction Bots or Good Bots; however, Good Bots are more efficient and sustainable than Info-Correction Bots . Proactive good messaging is more resource-effective than reactive information correction. With our observations, we expand the concept of bots as a malicious social media agent towards automated social media agent that can be used for both good and bad purposes. These results have implications for designing communication strategies to maintain a healthy social cyber ecosystem.
Related papers
- FATe of Bots: Ethical Considerations of Social Bot Detection [1.8470340645800405]
We examine the ethical implications for social bot detection systems through three pillars: training datasets, algorithm development, and the use of bot agents.<n>We aim to inspire more responsible and equitable approaches towards improving the social media bot detection landscape.
arXiv Detail & Related papers (2026-02-05T01:53:17Z) - RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction [51.46634975923564]
This paper proposes the first adversarial multi-agent Reinforcement learning framework for social Bot control attacks (RoBCtrl)<n> Specifically, we use a diffusion model to generate high-fidelity bot accounts by reconstructing existing account data with minor modifications.<n>We then employ a Multi-Agent Reinforcement Learning (MARL) method to simulate bots adversarial behavior.
arXiv Detail & Related papers (2025-10-16T02:41:49Z) - The Dual Personas of Social Media Bots [5.494111035517598]
Social media bots are AI agents that participate in online conversations.<n>Most studies focus on the general bot and the malicious nature of these agents.<n>However, bots have many different personas, each specialized towards a specific behavioral or content trait.
arXiv Detail & Related papers (2025-04-16T21:30:41Z) - What is a Social Media Bot? A Global Comparison of Bot and Human Characteristics [5.494111035517598]
Bots tend to use linguistic cues that can be easily automated while humans use cues that require dialogue understanding.<n>These conclusions are based on a large-scale analysis of social media tweets across 200mil users across 7 events.
arXiv Detail & Related papers (2025-01-01T14:45:43Z) - OpenBot-Fleet: A System for Collective Learning with Real Robots [45.739144410591805]
We introduce OpenBot-Fleet, a comprehensive open-source cloud robotics system for navigation.
OpenBot-Fleet uses smartphones for sensing, local compute and communication, Google for secure cloud storage and off-board compute.
In experiments we distribute 72 robots to a crowd of workers who operate them in homes, and show that OpenBot-Fleet can learn robust navigation policies.
arXiv Detail & Related papers (2024-05-13T07:22:50Z) - My Brother Helps Me: Node Injection Based Adversarial Attack on Social Bot Detection [69.99192868521564]
Social platforms such as Twitter are under siege from a multitude of fraudulent users.
Due to the structure of social networks, the majority of methods are based on the graph neural network(GNN), which is susceptible to attacks.
We propose a node injection-based adversarial attack method designed to deceive bot detection models.
arXiv Detail & Related papers (2023-10-11T03:09:48Z) - You are a Bot! -- Studying the Development of Bot Accusations on Twitter [1.7626250599622473]
In the absence of ground truth data, researchers may want to tap into the wisdom of the crowd.
Our research presents the first large-scale study of bot accusations on Twitter.
It shows how the term bot became an instrument of dehumanization in social media conversations.
arXiv Detail & Related papers (2023-02-01T16:09:11Z) - Investigating the Validity of Botometer-based Social Bot Studies [0.0]
Social bots are assumed to be automated social media accounts operated by malicious actors with the goal of manipulating public opinion.
Social bot activity has been reported in many different political contexts, including the U.S. presidential elections.
We point out a fundamental theoretical flaw in the widely-used study design for estimating the prevalence of social bots.
arXiv Detail & Related papers (2022-07-23T09:31:30Z) - Identification of Twitter Bots based on an Explainable ML Framework: the
US 2020 Elections Case Study [72.61531092316092]
This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data.
Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm.
Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
arXiv Detail & Related papers (2021-12-08T14:12:24Z) - CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement
Learning [60.348822346249854]
This study presents a framework whereby several empathetic chatbots are based on understanding users' implied feelings and replying empathetically for multiple dialogue turns.
We call these chatbots CheerBots. CheerBots can be retrieval-based or generative-based and were finetuned by deep reinforcement learning.
To respond in an empathetic way, we develop a simulating agent, a Conceptual Human Model, as aids for CheerBots in training with considerations on changes in user's emotional states in the future to arouse sympathy.
arXiv Detail & Related papers (2021-10-08T07:44:47Z) - Detection of Novel Social Bots by Ensembles of Specialized Classifiers [60.63582690037839]
Malicious actors create inauthentic social media accounts controlled in part by algorithms, known as social bots, to disseminate misinformation and agitate online discussion.
We show that different types of bots are characterized by different behavioral features.
We propose a new supervised learning method that trains classifiers specialized for each class of bots and combines their decisions through the maximum rule.
arXiv Detail & Related papers (2020-06-11T22:59:59Z)
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.