Extracting Account Attributes for Analyzing Influence on Twitter

Abstract

The last years has witnessed a surge of auto-generated content on social media. While many uses are legitimate, bots have also been deployed in influence operations to manipulate election results, affect public opinion in a desired direction, or to divert attention from a specific event or phenomenon. Today, many approaches exist to automatically identify bot-like behaviour in order to curb illegitimate influence operations. While progress has been made, existing models are exceedingly complex and nontransparent, rendering validation and model testing difficult. We present a transparent and parsimonious method to study influence operations on Twitter. We define nine different attributes that can be used to describe and reason about different characteristics of a Twitter account. The attributes can be used to group accounts that have similar characteristics and the result can be used to identify accounts that are likely to be used to influence public opinion. The method has been tested on a Twitter data set consisting of 66,000 accounts. Clustering the accounts based on the proposed features show promising results for separating between different groups of reference accounts.