Prediction of brand stories spreading on social networks

Thi Bich Ngoc Hoang () and Josiane Mothe ()
Additional contact information
Thi Bich Ngoc Hoang: IRIT, UMR5505 CNRS
Josiane Mothe: Université de Toulouse

Advances in Data Analysis and Classification, 2022, vol. 16, issue 3, No 4, 559-591

Abstract: Abstract Online social network is a major media for many types of information communication. Although the primary purpose of social networks is to connect people, they are more and more used in online marketing to connect businesses with customers as well as to connect customers amongst themselves. Brand stories generated by consumers or businesses can be easily and widely spread. As a result, those stories have a huge influence on the marketplace and indirectly affect the brand success. Understanding and modeling how a piece of information is spread on social media and its spreading level are crucial for business managers; not only to understand the information diffusion, but also for them to better control it. In this paper, we aim at developing models in order to predict the spread of brand stories on social networks, both in term of spreadability and spreading level. We applied several machine learning algorithms using three categories of features based on user-profile, temporal, and content of tweets. Experimental results on three tweet collections about brand stories reveal that our model significantly improves the prediction accuracy by about 4% compared to the related work.

Keywords: Information retrieval; Information diffusion; Social media; Tweets analysis; Predictive model; Using machine learning; Online marketing; 68T99; 68T50; 68U15 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11634-021-00450-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:16:y:2022:i:3:d:10.1007_s11634-021-00450-x

Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2

DOI: 10.1007/s11634-021-00450-x

Access Statistics for this article

Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs

More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().