DIFCURV: A unified framework for Diffusion Curve Fitting and prediction in Online Social Networks

Information propagation analysis in Online Social Networks (OSNs) sparks great interest due to its impact across different business sectors. In the wide range of OSNs, the famous micro-blogging service Twitter stands out for a plethora of reasons, such as the platform popularity and the ease of acce...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Charalambos Christoforou, Kalliopi Malerou, Nikolaos L. Tsitsas, Athena Vakali
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Online Social Networks Information diffusion Prediction Curve fitting Computer engineering. Computer hardware TK7885-7895 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/11807e3d2bd04ac2a688b87c9f0d957c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:11807e3d2bd04ac2a688b87c9f0d957c
record_format	dspace
spelling	oai:doaj.org-article:11807e3d2bd04ac2a688b87c9f0d957c2021-11-12T04:46:27ZDIFCURV: A unified framework for Diffusion Curve Fitting and prediction in Online Social Networks2590-005610.1016/j.array.2021.100100https://doaj.org/article/11807e3d2bd04ac2a688b87c9f0d957c2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2590005621000448https://doaj.org/toc/2590-0056Information propagation analysis in Online Social Networks (OSNs) sparks great interest due to its impact across different business sectors. In the wide range of OSNs, the famous micro-blogging service Twitter stands out for a plethora of reasons, such as the platform popularity and the ease of access to data. Activities like retweeting in the popular OSN micro-blogging Twitter service, constitute fundamental mechanisms for information diffusion. The form of such cascading activities (like retweet) in time plays a crucial role in identifying the influence and the life duration of an information source (like tweet). In this paper, we propose an integral framework with a dual functionality to: (i) examine the effectiveness of robust mathematical models in the fitting of the curves produced by the number of retweets over a period of time, and (ii) employ these mathematical models to predict the behavior of the examined retweets using only a small fraction of them (as input data). The examined mathematical models stem from simple mathematical functions or are based on the Diffusion of Innovation theory, an important theory for examining spreading phenomena which has not yet been used thoroughly in OSNs Diffusion prediction. The proposed Framework (so called DIFCURV) encapsulates proper data preprocessing procedures as well as explanatory Analysis augmented with Visualization and Statistical Analysis. In the curve fitting part of the DIFCURV Framework, an optimization method, which depends upon the curve’s slope, is deployed to the tweet stories having an error above the defined threshold, resulting in a significant reduction of the error. To predict the retweets temporal evolution, the non-linear least squares curve-fitting method was selected after detailed exploration and examination of different methods. Furthermore, for the approximation of the growth-rate variable, three methods are proposed and Mean Growth Rate is showcased as the most suitable approach for the OSNs domain. The effectiveness of the DIFCURV Framework is exhibited by presenting results of several numerical experiments for a large dataset consisting of over two million retweets in total for all examined stories. DIFCURV prediction results were also compared with similar existing works and comparisons showed that the Proposed Framework can predict Information Diffusion with higher accuracy and efficiency.Charalambos ChristoforouKalliopi MalerouNikolaos L. TsitsasAthena VakaliElsevierarticleOnline Social NetworksInformation diffusionPredictionCurve fittingComputer engineering. Computer hardwareTK7885-7895Electronic computers. Computer scienceQA75.5-76.95ENArray, Vol 12, Iss , Pp 100100- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Online Social Networks Information diffusion Prediction Curve fitting Computer engineering. Computer hardware TK7885-7895 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Online Social Networks Information diffusion Prediction Curve fitting Computer engineering. Computer hardware TK7885-7895 Electronic computers. Computer science QA75.5-76.95 Charalambos Christoforou Kalliopi Malerou Nikolaos L. Tsitsas Athena Vakali DIFCURV: A unified framework for Diffusion Curve Fitting and prediction in Online Social Networks
description	Information propagation analysis in Online Social Networks (OSNs) sparks great interest due to its impact across different business sectors. In the wide range of OSNs, the famous micro-blogging service Twitter stands out for a plethora of reasons, such as the platform popularity and the ease of access to data. Activities like retweeting in the popular OSN micro-blogging Twitter service, constitute fundamental mechanisms for information diffusion. The form of such cascading activities (like retweet) in time plays a crucial role in identifying the influence and the life duration of an information source (like tweet). In this paper, we propose an integral framework with a dual functionality to: (i) examine the effectiveness of robust mathematical models in the fitting of the curves produced by the number of retweets over a period of time, and (ii) employ these mathematical models to predict the behavior of the examined retweets using only a small fraction of them (as input data). The examined mathematical models stem from simple mathematical functions or are based on the Diffusion of Innovation theory, an important theory for examining spreading phenomena which has not yet been used thoroughly in OSNs Diffusion prediction. The proposed Framework (so called DIFCURV) encapsulates proper data preprocessing procedures as well as explanatory Analysis augmented with Visualization and Statistical Analysis. In the curve fitting part of the DIFCURV Framework, an optimization method, which depends upon the curve’s slope, is deployed to the tweet stories having an error above the defined threshold, resulting in a significant reduction of the error. To predict the retweets temporal evolution, the non-linear least squares curve-fitting method was selected after detailed exploration and examination of different methods. Furthermore, for the approximation of the growth-rate variable, three methods are proposed and Mean Growth Rate is showcased as the most suitable approach for the OSNs domain. The effectiveness of the DIFCURV Framework is exhibited by presenting results of several numerical experiments for a large dataset consisting of over two million retweets in total for all examined stories. DIFCURV prediction results were also compared with similar existing works and comparisons showed that the Proposed Framework can predict Information Diffusion with higher accuracy and efficiency.
format	article
author	Charalambos Christoforou Kalliopi Malerou Nikolaos L. Tsitsas Athena Vakali
author_facet	Charalambos Christoforou Kalliopi Malerou Nikolaos L. Tsitsas Athena Vakali
author_sort	Charalambos Christoforou
title	DIFCURV: A unified framework for Diffusion Curve Fitting and prediction in Online Social Networks
title_short	DIFCURV: A unified framework for Diffusion Curve Fitting and prediction in Online Social Networks
title_full	DIFCURV: A unified framework for Diffusion Curve Fitting and prediction in Online Social Networks
title_fullStr	DIFCURV: A unified framework for Diffusion Curve Fitting and prediction in Online Social Networks
title_full_unstemmed	DIFCURV: A unified framework for Diffusion Curve Fitting and prediction in Online Social Networks
title_sort	difcurv: a unified framework for diffusion curve fitting and prediction in online social networks
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/11807e3d2bd04ac2a688b87c9f0d957c
work_keys_str_mv	AT charalamboschristoforou difcurvaunifiedframeworkfordiffusioncurvefittingandpredictioninonlinesocialnetworks AT kalliopimalerou difcurvaunifiedframeworkfordiffusioncurvefittingandpredictioninonlinesocialnetworks AT nikolaosltsitsas difcurvaunifiedframeworkfordiffusioncurvefittingandpredictioninonlinesocialnetworks AT athenavakali difcurvaunifiedframeworkfordiffusioncurvefittingandpredictioninonlinesocialnetworks
_version_	1718431213308346368

DIFCURV: A unified framework for Diffusion Curve Fitting and prediction in Online Social Networks

Ejemplares similares