A social media competitive intelligence framework for brand topic identification and customer engagement prediction

This framework differentiates itself from existing competitive intelligence frameworks by integrating the influence of external factors, such as the COVID-19 pandemic, and expanding the analysis from topic detection to customer engagement prediction. This dual focus provides a more comprehensive approach to social media competitive intelligence.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2024-11-25
DOI
https://doi.org/10.1371/journal.pone.0313191
Citations
1
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
1.13
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A social media competitive intelligence framework for brand topic identification and customer engagement prediction

Xingting Ju
1 citations
PLoS ONE
Digital Marketing and Social Media
1.13
article

A social media competitive intelligence framework for brand topic identification and customer engagement prediction

Xingting Ju
article en
1 citations

Abstract

This framework differentiates itself from existing competitive intelligence frameworks by integrating the influence of external factors, such as the COVID-19 pandemic, and expanding the analysis from topic detection to customer engagement prediction. This dual focus provides a more comprehensive approach to social media competitive intelligence.

PLoS ONEVol. 19(11)
International University of Rabat (MA)
Openalex Percentile: Top 15%
Digital Marketing and Social Media
1.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.