Paper Info
Title
Prediction of Topics Popularity on On-Line Social Networks.
Abstract
Social networks represent nowadays an important communication channel for various groups of people over the world. They offer an audience for various activities with the aim to get the attention of the related target group, e. g. marketing or political campaigns. The aim of this paper is to understand the hid-den trends and various developments in such type of data and extract possible new and interesting knowledge for business purposes. For this purpose, we used data from two different social networks: Twitter and Tom's Hardware. The completely analytical process was realised in line with CRISP-DM methodology; we selected the suitable methods of machine learning and exploratory data analysis to get the expected results. The created decision support application offers a group of methods to understand the data within the exploratory analysis, to generate a prediction model with the highest accuracy or to extract the rules supporting decision process during an on-line campaign. The best-achieved accuracy was higher than 95 % and extracted rules represent a good basis to ensure an expected popularity for selected topics in the future. Although we tested the system within a dataset closer oriented to the ICT sector, we will evaluate its applicability on a wider scale in our future work.
Year
DOI
Venue
2016
10.1007/978-3-319-43982-2_29
MULTIMEDIA AND NETWORK INFORMATION SYSTEMS, MISSI 2016
Keywords
DocType
Volume
Social network,Buzz,Analysis
Conference
506
ISSN
Citations 
PageRank 
2194-5357
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Frantisek Babic1168.02
Anna Drábiková200.34