# Discovering patterns of online popularity from time series

## TL;DR

This paper introduces dipm-SC, a shape-based clustering algorithm for analyzing online content popularity over time, revealing two main patterns—bursty and steady—and showing that the growth pattern does not affect total popularity.

## Contribution

The paper presents a novel multi-dimensional shape-based clustering algorithm with a heuristic for optimal cluster number, applied to real-world Twitter data to identify popularity patterns.

## Key findings

- Identified two main popularity patterns: bursty and steady.
- Popularity growth over time does not influence total popularity.
- Validated algorithm accuracy on synthetic datasets.

## Abstract

How is popularity gained online? Is being successful strictly related to rapidly becoming viral in an online platform or is it possible to acquire popularity in a steady and disciplined fashion? What are other temporal characteristics that can unveil the popularity of online content? To answer these questions, we leverage a multi-faceted temporal analysis of the evolution of popular online contents. Here, we present dipm-SC: a multi-dimensional shape-based time-series clustering algorithm with a heuristic to find the optimal number of clusters. First, we validate the accuracy of our algorithm on synthetic datasets generated from benchmark time series models. Second, we show that dipm-SC can uncover meaningful clusters of popularity behaviors in a real-world Twitter dataset. By clustering the multidimensional time-series of the popularity of contents coupled with other domain-specific dimensions, we uncover two main patterns of popularity: bursty and steady temporal behaviors. Moreover, we find that the way popularity is gained over time has no significant impact on the final cumulative popularity.