Proposing Botwatch: Trust-Based Community Bot Detection

Proposing Botwatch - 7hird Thoughts

Trust-Based Community Bot Detection

Botwatch

Fight bots by warning others. Support humans with verification. Build communities with trust.

Bots Among Us - 7hird Thoughts

How can we build trust in an era of bots and lies?

Exposure to social bots amplifies perceptual biases and regulation propensity

Automated accounts on social media that impersonate real users, often called “social bots,” have received a great deal of attention from academia and the public. Here we present experiments designed to investigate public perceptions and policy preferences about social bots, in particular how they are affected by exposure to bots. We find that before exposure, participants have some biases: they tend to overestimate the prevalence of bots and see others as more vulnerable to bot influence than themselves. These biases are amplified after bot exposure. Furthermore, exposure tends to impair judgment of bot-recognition self-efficacy and increase propensity toward stricter bot-regulation policies among participants. Decreased self-efficacy and increased perceptions of bot influence on others are significantly associated with these policy preference changes. We discuss the relationship between perceptions about social bots and growing dissatisfaction with the polluted social media environment.

Exposure to social bots amplifies perceptual biases and regulation propensity

Automated accounts on social media that impersonate real users, often called “social bots,” have received a great deal of attention from academia and the public. Here we present experiments designed to investigate public perceptions and policy preferences about social bots, in particular how they are affected by exposure to bots. We find that before exposure, participants have some biases: they tend to overestimate the prevalence of bots and see others as more vulnerable to bot influence than themselves. These biases are amplified after bot exposure. Furthermore, exposure tends to impair judgment of bot-recognition self-efficacy and increase propensity toward stricter bot-regulation policies among participants. Decreased self-efficacy and increased perceptions of bot influence on others are significantly associated with these policy preference changes. We discuss the relationship between perceptions about social bots and growing dissatisfaction with the polluted social media environment.

Exposure to social bots amplifies perceptual biases and regulation propensity

Automated accounts on social media that impersonate real users, often called “social bots,” have received a great deal of attention from academia and the public. Here we present experiments designed to investigate public perceptions and policy preferences about social bots, in particular how they are affected by exposure to bots. We find that before exposure, participants have some biases: they tend to overestimate the prevalence of bots and see others as more vulnerable to bot influence than themselves. These biases are amplified after bot exposure. Furthermore, exposure tends to impair judgment of bot-recognition self-efficacy and increase propensity toward stricter bot-regulation policies among participants. Decreased self-efficacy and increased perceptions of bot influence on others are significantly associated with these policy preference changes. We discuss the relationship between perceptions about social bots and growing dissatisfaction with the polluted social media environment.

Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations

AI agents, or bots, serve important roles in online communities. However, they are often designed by outsiders or a few tech-savvy members, leading to bots that may not align with the broader community's needs. How might communities collectively shape the behavior of community bots? We present Botender, a system that enables communities to collaboratively design LLM-powered bots without coding. With Botender, community members can directly propose, iterate on, and deploy custom bot behaviors tailored to community needs. Botender facilitates testing and iteration on bot behavior through case-based provocations: interaction scenarios generated to spark user reflection and discussion around desirable bot behavior. A validation study found these provocations more useful than standard test cases for revealing improvement opportunities and surfacing disagreements. During a five-day deployment across six Discord servers, Botender supported communities in tailoring bot behavior to their specific needs, showcasing the usefulness of case-based provocations in facilitating collaborative bot design.

Commence the Botwatch | Botwatch Blog

Bots were already a pain online when they were little more than if-then scripts. Now a tidal wave of slop is swamping the internet as big tech companies profit. OpenAI and the rest would love for you to believe they care about "alignment" and "values" while their AI models damage our communities, both online and off. It's time to fight back.

What is a bot? | Learning Center

An Internet bot is a software application that runs automated tasks over the Internet, ranging from helpful crawlers to malicious attackers.

First Evidence That Social Bots Play a Major Role in Spreading Fake News

Automated accounts are being programmed to spread fake news, according to the first systematic study of the way online misinformation spreads

Sybil Detection using Graph Neural Networks

Online social networks have become a central part of modern digital life, connecting billions of users worldwide. However, their open nature and vast scale make them vulnerable to various security threats, with Sybil attacks being a prevalent and challenging attack to detect. In Sybil attacks, malicious entities create fake accounts to manipulate the network, spread misinformation, or gain undue influence. Thus, detecting Sybil accounts is crucial for maintaining the integrity and trustworthiness of social networks. Effective Sybil detection can prevent the spread of fake news, protect users from scams, and ensure a fair distribution of resources and influence within the network. However, as attackers become more sophisticated, traditional detection methods increasingly fail to perform their task.

Katika Kühnreich (@Katika@chaos.social)

Attached: 1 image #SocialMedia of #realnameregistration , oh sorry, they call it real #humans , that advertises with " #AI " #slop #wsocial is more than problematic And already has #bots crawling it's #network Now they show that they are problematic concerning #aesthetics as well & advertise with tasteless #aislop @bildoperationen@tldr.nettime.org #realnameregistration is highly dangerous & if the company that wants to have your most private data can't save there net from bots they're not very trustworthy, are they? #tech

AI-driven Bot Attacks Surged 12.5x According to Thales Bad Bot Report

Bots now dominate the internet, accounting for over half of all traffic, with 40% classified as malicious.AI is erasing the line between legitimate and maliciou