Pinpoint Influential Posts and Authors
Luiza Nacshon, Rami Puzis, Amparo Sanmateho

TL;DR
This paper introduces an analytical model that uses topic modeling and TF-IDF to identify influential social media posts and authors, aiding in monitoring impactful online discussions and customer relationship management.
Contribution
The paper presents a novel combination of Latent Dirichlet Allocation and TF-IDF for pinpointing influential posts and authors in social web analysis.
Findings
Effective identification of influential posts in social media
Model successfully monitors evolving online 'storms' impacting audiences
Potential for extending analysis to broader data sources
Abstract
This research presents an analytical model that aims to pin-point influential posts across a social web comprised of a corpus of posts. The model employs the Latent Dirichlet Al-location algorithm to associate posts with topics, and the TF-IDF metric to identify the key posts associated with each top-ic. The model was demonstrated in the domain of customer relationship by enabling careful monitoring of evolving "storms" created by individuals which tend to impact large audiences (either positively or negatively). Future research should be engaged in order to extend the scope of the corpus by including additional relevant publicly available sources.
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Taxonomy
TopicsComplex Network Analysis Techniques · Digital Marketing and Social Media · Sentiment Analysis and Opinion Mining
