BOTTER: A framework to analyze social bots in Twitter

TL;DR

This paper introduces BOTTER, a unified framework for analyzing social bots on Twitter, enabling objective measurement of their impact and behavior, validated through a case study around the 2019 Spanish election.

Contribution

The paper presents a novel, standardized framework for analyzing social bots, addressing heterogeneity in existing methods and providing a practical tool for impact assessment.

Findings

Social bots altered network dynamics during the election period.

Bots did not have a significant overall impact on information flow.

Semi-automated accounts may pose greater threats than fully automated bots.

Abstract

Social networks have triumphed in communicating people online, but they have also been exploited to launch influence operations for manipulating society. The deployment of software-controlled accounts (e.g., social bots) has proven to be one of the most effective enablers for that purpose, and tools for their detection have been developed and widely adopted. However, the way to analyze these accounts and measure their impact is heterogeneous in the literature, where each case study performs unique measurements. To unify these efforts, we propose a common framework to analyze the interference of social bots in Twitter. The methodology compares the non-authentic actors with the rest of users from different perspectives, thus building objective metrics to measure their actual impact. We validate the framework by applying it to a dataset of Twitter iterations dated in the weeks preceding the 2019 Spanish general election. In this sense, we check that our framework facilitates the quantitative evaluation of unauthentic groups, particularly discovering that social bots changed the natural dynamics of the network in these days, but did not have a significant impact. We also consider this methodology as a practical tool for the qualitative interpretation of experimental results, particularly suggesting within the aforementioned electoral context that semi-automated accounts are potentially more threatening than fully automated ones.