Towards Synthetic Social Media Data

被引：0

作者：

Mandravickaite, Justina ^{[1
,2
]}

Songailaite, Milita ^{[1
,2
]}

Gvozdovaite, Veronika ^{[1
,3
]}

Kalinauskaite, Danguole ^{[1
,2
]}

Krilavicius, Tomas ^{[1
,2
]}

机构：

[1] Vytautas Magnus Univ, Kaunas, Lithuania

[2] Ctr Appl Res & Dev CARD, Kaunas, Lithuania

[3] Univ Oxford, Oxford, England

来源：

BALTIC JOURNAL OF MODERN COMPUTING | 2022年 / 10卷 / 03期

关键词：

social media; NLG; synthetic data; SNS; English;

D O I：

10.22364/bjmc.2022.10.3.10

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

There is an increasing need for training and testing data that can be used for the development of technologies and research. Due to data protection regulations, a lot of the real -world data - especially the data from social media - becomes unavailable to use. The problem can be solved by generating synthetic data that imitates the properties of real-world data. In this paper, we present Fabulator - a social media generator that combines text and graph structure to be used for the generation of synthetic data and, in the future, for the simulation of various events on social media.

引用

页码：372 / 381

页数：10

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