People are Strange when you're a Stranger: Impact and Influence of Bots on Social Networks

TL;DR

This study investigates how simple, non-human-like bots can gain influence and impact in social networks, revealing their ability to affect user behavior, uncover polarization, and raise privacy concerns.

Contribution

The paper demonstrates that basic social probing bots can acquire influence and reveal community polarization without mimicking human behavior.

Findings

Bots can gain social relevance through simple probing activities.

Influential bots can steer user connectivity choices.

Bot activity exposes hidden polarization and privacy issues.

Abstract

Bots are, for many Web and social media users, the source of many dangerous attacks or the carrier of unwanted messages, such as spam. Nevertheless, crawlers and software agents are a precious tool for analysts, and they are continuously executed to collect data or to test distributed applications. However, no one knows which is the real potential of a bot whose purpose is to control a community, to manipulate consensus, or to influence user behavior. It is commonly believed that the better an agent simulates human behavior in a social network, the more it can succeed to generate an impact in that community. We contribute to shed light on this issue through an online social experiment aimed to study to what extent a bot with no trust, no profile, and no aims to reproduce human behavior, can become popular and influential in a social media. Results show that a basic social probing activity can be used to acquire social relevance on the network and that the so-acquired popularity can be effectively leveraged to drive users in their social connectivity choices. We also register that our bot activity unveiled hidden social polarization patterns in the community and triggered an emotional response of individuals that brings to light subtle privacy hazards perceived by the user base.