id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-227156-uy4dykhg	Albanese, Federico	Predicting Shifting Individuals Using Text Mining and Graph Machine Learning on Twitter	2020-08-24		.txt	text/plain	4940	263	50	Moreover, this machine learning framework allows us to identify not only which topics are more persuasive (using low dimensional topic embedding), but also which individuals are more likely to change their affiliation given their topological properties in a Twitter graph. Using graph topological information and detecting topics of discussion of the first network, we built and trained a model that effectively predicts when an individual will change his/her community over time, identifying persuasive topics and relevant features of the shifting users. Given that our objective was to identify shifting individuals and persuasive arguments, we implemented a predictive model whose instances are the Twitter users who were active during both time periods [34] and belonged to one of the biggest communities in both time periods networks. In this paper we presented a machine learning framework approach in order to identify shifting individuals and persuasive topics that, unlike previous works, focused on the persuadable users rather than studying the political polarization on social media as a whole.	./cache/cord-227156-uy4dykhg.txt	./txt/cord-227156-uy4dykhg.txt