Community pooling: LDA topic modeling in Twitter

Abstract

Social networks play a fundamental role in propagation of information and news. Characterizing the content of the messages becomes vital for tasks like fake news detection or personalized message recommendation. However, Twitter posts are short and often less coherent than other text documents, which makes it challenging to apply text mining algorithms efficiently. We propose a new pooling scheme for topic modeling in Twitter, which groups tweets whose authors belong to the same community on the retweet network into a single document. Our findings contribute to an improved methodology for identifying the latent topics in a Twitter dataset, without modifying the basic machinery of a topic decomposition model. In particular, we used Latent Dirichlet Allocation (LDA) and empirically showed that this novel method achieves better results than previous pooling methods in terms of cluster quality, document retrieval tasks, supervised machine learning classification and overall run time.

Polarization dynamics: a study of individuals shifting between political communities on social media

“…The data collection process encompassed two distinct time intervals: 9 May to 16 May (hereafter referred as t 1 ) and 10 June to 16 June 2020 (hereafter referred as t 2 ). A subset of the dataset employed in this study has been previously used in [13].…”

“…To accurately capture the dynamics of multiple retweets from one user to another, we model the graph as a directed and weighted network. The edge points from the content creator to the retweeter, representing the flow of information through the network, originating from the content creator and spreading to the users who amplify the message [13].…”

“…In this context, Wang et al focus on understanding the 'bandwagon fan' phenomenon in Reddit sport communities, showing how some sports fans start following a new sports team solely because of its recent success [11]. Albanese et al propose a method based on XGBoost, the Louvain method for community detection and topic modeling to detect users who change their political community on Twitter [13]. Zolezzi et al analyze the flow of users between these communities [14].…”