Classification of Internet Memes With Geographic Support and Machine Learning
Abstract (300 words maximum)
Internet memes are ubiquitous in today’s social media dominated world, offering quick online entertainment. Utilizing Google Trends time-series data, we dissect dynamic meme popularity trends. Previous studies discerned four popularity patterns using differential equations and machine learning to both identify and classify them. Our recent expansion of the dataset to 2000 elements prompts us to collect additional trend data, centering on geography and categorical connections. Statistical analysis can then unveil new insights in meme popularity, in tandem with graph-theoretic metrics.
Academic department under which the project should be listed
CSM - Mathematics
Primary Investigator (PI) Name
Pengcheng Xiao
Classification of Internet Memes With Geographic Support and Machine Learning
Internet memes are ubiquitous in today’s social media dominated world, offering quick online entertainment. Utilizing Google Trends time-series data, we dissect dynamic meme popularity trends. Previous studies discerned four popularity patterns using differential equations and machine learning to both identify and classify them. Our recent expansion of the dataset to 2000 elements prompts us to collect additional trend data, centering on geography and categorical connections. Statistical analysis can then unveil new insights in meme popularity, in tandem with graph-theoretic metrics.