Decoding Memes: A Comparative Study of Machine Learning Models for Template Identification

TL;DR

This paper evaluates various machine learning methods for automatically identifying meme templates, introducing a comprehensive framework and dataset to improve accuracy and robustness in distinguishing memes from non-memes.

Contribution

It provides a comparative analysis of existing and novel meme template identification techniques, along with a new evaluation framework and diverse dataset.

Findings

Supervised and unsupervised methods show varying accuracy levels.

The evaluation framework effectively distinguishes memes from non-memes.

Dataset from multiple social media platforms enhances robustness.

Abstract

Image-with-text memes combine text with imagery to achieve comedy, but in today's world, they also play a pivotal role in online communication, influencing politics, marketing, and social norms. A "meme template" is a preexisting layout or format that is used to create memes. It typically includes specific visual elements, characters, or scenes with blank spaces or captions that can be customized, allowing users to easily create their versions of popular meme templates by adding personal or contextually relevant content. Despite extensive research on meme virality, the task of automatically identifying meme templates remains a challenge. This paper presents a comprehensive comparison and evaluation of existing meme template identification methods, including both established approaches from the literature and novel techniques. We introduce a rigorous evaluation framework that not only assesses the ability of various methods to correctly identify meme templates but also tests their capacity to reject non-memes without false assignments. Our study involves extensive data collection from sites that provide meme annotations (Imgflip) and various social media platforms (Reddit, X, and Facebook) to ensure a diverse and representative dataset. We compare meme template identification methods, highlighting their strengths and limitations. These include supervised and unsupervised approaches, such as convolutional neural networks, distance-based classification, and density-based clustering. Our analysis helps researchers and practitioners choose suitable methods and points to future research directions in this evolving field.