Online Human-Bot Interactions: Detection, Estimation, and Characterization

TL;DR

This paper presents a comprehensive framework for detecting and characterizing social bots on Twitter using a wide range of features, achieving high accuracy and revealing insights into bot behaviors and prevalence.

Contribution

The study introduces a novel detection framework leveraging over a thousand features, validated on diverse datasets, and provides detailed characterization of different bot types and their interaction patterns.

Findings

Bots constitute 9-15% of active Twitter accounts.

Simple bots tend to interact with more human-like bots.

Different bot subclasses exhibit distinct content and interaction strategies.

Abstract

Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of different nature. Our estimates suggest that between 9% and 15% of active Twitter accounts are bots. Characterizing ties among accounts, we observe that simple bots tend to interact with bots that exhibit more human-like behaviors. Analysis of content flows reveals retweet and mention strategies adopted by bots to interact with different target groups. Using clustering analysis, we characterize several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.