Analysis of Twitter Traffic based on Renewal Densities

TL;DR

This paper introduces a new low-complexity method for analyzing Twitter message timing using renewal theory, enabling detection of interaction levels, correlation, and spam-related periodic traffic.

Contribution

It extends renewal theory to Twitter traffic analysis, providing a novel approach to characterize message interaction and detect spam based solely on timing data.

Findings

Effective detection of message correlation levels.

Identification of periodic spam traffic.

Low complexity and scalable analysis method.

Abstract

In this paper we propose a novel approach for Twitter traffic analysis based on renewal theory. Even though twitter datasets are of increasing interest to researchers, extracting information from message timing remains somewhat unexplored. Our approach, extending our prior work on anomaly detection, makes it possible to characterize levels of correlation within a message stream, thus assessing how much interaction there is between those posting messages. Moreover, our method enables us to detect the presence of periodic traffic, which is useful to determine whether there is spam in the message stream. Because our proposed techniques only make use of timing information and are amenable to downsampling, they can be used as low complexity tools for data analysis.