@misc{10481/75136,
year = {2022},
month = {5},
url = {http://hdl.handle.net/10481/75136},
abstract = {The incursion of social media in our lives has been much accentuated in the last decade.
This has led to a multiplication of data mining tools aimed at obtaining knowledge from
these data sources. One of the greatest challenges in this area is to be able to obtain this
knowledge without the need for training processes, which requires structured information
and pre-labelled datasets. This is where unsupervised data mining techniques come in.
These techniques can obtain value from these unstructured and unlabelled data, providing
very interesting solutions to enhance the decision-making process. In this paper, we first
address the problem of social media mining, as well as the need for unsupervised techniques,
in particular association rules, for its treatment. We follow with a broad overview
of the applications of association rules in the domain of social media mining, specifically,
their application to the problems of mining textual entities, such as tweets. We also focus
on the strengths and weaknesses of using association rules for solving different tasks in
textual social media. Finally, the paper provides a perspective overview of the challenges
that association rules must face in the next decade within the field of social media mining.},
organization = {COPKIT project under the European Union's Horizon 2020 research and innovation program	786687},
organization = {Andalusian government},
organization = {FEDER operative program under the project BigDataMed	P18-RT-2947	
B-TIC-145-UGR18},
organization = {Spanish Government	FPU18/00150},
publisher = {Springer},
keywords = {Social media mining},
keywords = {Association rules},
keywords = {Text mining},
keywords = {Social networks},
title = {A survey on the use of association rules mining techniques in textual social media},
doi = {10.1007/s10462-022-10196-3},
author = {Díaz García, José Ángel and Ruiz Jiménez, María Dolores and Martín Bautista, María José},
}