# Openbots

## TL;DR

This paper analyzes open source social bot code to understand their sophistication, origins, and trends, revealing most are modular automation tools with limited use of advanced AI, and proposes a framework for monitoring bot development.

## Contribution

It provides an empirical analysis of publicly available bot code, highlighting the current state, limitations, and trends in social bot development on open source platforms.

## Key findings

- Most open source bot code is modular automation support.
- Building fully fledged social bots requires significant additional effort.
- Advanced AI methods are currently rarely used in publicly available bot code.

## Abstract

Social bots have recently gained attention in the context of public opinion manipulation on social media platforms. While a lot of research effort has been put into the classification and detection of such (semi-)automated programs, it is still unclear how sophisticated those bots actually are, which platforms they target, and where they originate from. To answer these questions, we gathered repository data from open source collaboration platforms to identify the status-quo as well as trends of publicly available bot code. Our findings indicate that most of the code on collaboration platforms is of supportive nature and provides modules of automation instead of fully fledged social bot programs. Hence, the cost (in terms of additional programming effort) for building social bots with the goal of topic-specific manipulation is higher than assumed and that methods in context of machine- or deep-learning currently only play a minor role. However, our approach can be applied as multifaceted knowledge discovery framework to monitor trends in public bot code evolution to detect new developments and streams.