The Classification Of Internet Memes Through Supervised And Unsupervised Machine Learning Algorithms
Disciplines
Artificial Intelligence and Robotics | Data Science
Abstract (300 words maximum)
Memes, those captivating internet phenomena, effortlessly deliver online entertainment. By leveraging time-series data from Google Trends, we can vividly illustrate and dissect the dynamic trends in meme popularity. Previous studies have discerned four distinct post-peak popularity patterns— "smoothly decaying," "spikey decaying," "leveling off," and "long-term growth"—and elegantly modeled these using ordinary differential equations.
This research introduces a programmatic approach that harnesses both supervised and unsupervised machine learning algorithms. The dataset, now expanded to over 2000 elements, becomes the canvas for exploration. The K-means algorithm identifies clusters, which then serve as labels for the supervised SVC algorithm. The overarching goal is to achieve accurate classification of meme popularity patterns. Concurrently, each meme in the dataset will be categorized, such as catchphrase or viral video, facilitating an insightful analysis into the intriguing relationship between meme category and its distinctive popularity trajectory.
Academic department under which the project should be listed
CSM - Mathematics
Primary Investigator (PI) Name
Pengcheng Xiao
The Classification Of Internet Memes Through Supervised And Unsupervised Machine Learning Algorithms
Memes, those captivating internet phenomena, effortlessly deliver online entertainment. By leveraging time-series data from Google Trends, we can vividly illustrate and dissect the dynamic trends in meme popularity. Previous studies have discerned four distinct post-peak popularity patterns— "smoothly decaying," "spikey decaying," "leveling off," and "long-term growth"—and elegantly modeled these using ordinary differential equations.
This research introduces a programmatic approach that harnesses both supervised and unsupervised machine learning algorithms. The dataset, now expanded to over 2000 elements, becomes the canvas for exploration. The K-means algorithm identifies clusters, which then serve as labels for the supervised SVC algorithm. The overarching goal is to achieve accurate classification of meme popularity patterns. Concurrently, each meme in the dataset will be categorized, such as catchphrase or viral video, facilitating an insightful analysis into the intriguing relationship between meme category and its distinctive popularity trajectory.