Tweet's popularity dynamics

TL;DR

This study explores the challenges of automatically identifying patterns in tweet popularity evolution using machine learning, highlighting the complexity of modeling virality despite initial algorithmic attempts.

Contribution

The paper investigates the application of clustering and feature extraction techniques to model tweet popularity dynamics, revealing the difficulties in automating this task.

Findings

Clustering algorithm based on point-to-point distance was insufficient for automation.

Feature extraction analyses provided insights into popularity patterns.

Automating tweet virality prediction remains a complex challenge.

Abstract

This article charts the work of a 4 month project aimed at automatically identifying patterns of tweets popularity evolution using Machine Learning and Deep Learning techniques. To apprehend both the data and the extent of the problem, a straightforward clustering algorithm based on a point to point distance is used. Then, in an attempt to refine the algorithm, various analyses especially using feature extraction techniques are conducted. Although the algorithm eventually fails to automate such a task, this exercise raises a complex but necessary issue touching on the impact of virality on social networks.