Fight bots by warning others. Support humans with verification. Build communities with trust.
Proposing Botwatch: Trust-Based Community Bot Detection
Is this account a bot? This question’s importance is only growing in a world of LLMs and state-backed influence campaigns. Proposed here is Botwatch, a community-based system for personalized bot detection. In Botwatch, users publish records indicating whether they think others are bots and records indicating trust in a user’s scores. By analyzing this network, we can create useful signals to help users distinguish between bots and humans. Such a signal would consider your trust relations and o...
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.
Continuous accountability for Verified bots
Identity alone doesn't guarantee a bot will behave responsibly. We are introducing a system to continuously evaluate whether Verified bots honor site preferences, like robots.txt and HTTP 429s, ensuring they remain accountable over time.
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.
Double Counter — Discord Security Bot
The most trusted Discord security bot. Block alt accounts, raids, and VPNs with a privacy-respecting, data-powered verification system. Trusted by 600,000+ communities since 2020.
Security Verification
Security verification page to protect against malicious bots.
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.
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.
Game Bots Provide A Different Story For AI
When applied to game systems in the future players will find it harder to distinguish between bot and human players. We will appreciate having AI support in games to help us learn or improve, without ruining the game or requiring deep dives into manuals or tutorial YouTube videos.
Getting Bots to Respect Boundaries
Getting Bots to Respect Boundaries How AI Crawlers Are Straining Web Infrastructure Audrey Hingle January 2026 Image by Janet Turra & Cambridge Diversity Fund betterimagesofai.org creativecommons.org/licenses/by/4.0 Contents Introduction: Setting up the Problem 3 Understandin...
FR#168 – LLMs Join The Fediverse
How to build a fediverse community when bots are indistinguishable from humans on applications to join?
