An innovative framework for supporting content-based authorship identification and analysis in social media networks

被引：1

作者：

de la Puerta, Jose Gaviria ^{[1
]}

Pastor-Lopez, Iker ^{[1
]}

Tellaeche, Alberto ^{[1
]}

Sanz, Borja ^{[1
]}

Sanjurjo-Gonzalez, Hugo ^{[1
]}

Cuzzocrea, Alfredo ^{[2
,3
]}

Bringas, Pablo G. ^{[1
]}

机构：

[1] Univ Deusto, Fac Engn, Bilbao 48007, Spain

[2] Univ Calabria, iDEA Lab, I-87036 Arcavacata Di Rende, Italy

[3] Univ Lorraine, LORIA, F-54052 Nancy, France

来源：

LOGIC JOURNAL OF THE IGPL | 2024年 / 32卷 / 04期

基金：

欧盟地平线“2020”;

关键词：

Authorship identification; Word2Vec; online social networks; content-based approach;

D O I：

10.1093/jigpal/jzae020

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Content-based authorship identification is an emerging research problem in online social media networks, due to a wide collection of issues ranging from security to privacy preservation, from radicalization to defamation detection, and so forth. Indeed, this research has attracted a relevant amount of attention from the research community during the past years. The general problem becomes harder when we consider the additional constraint of identifying the same false profile over different social media networks, under obvious considerations. Inspired by this emerging research challenge, in this paper we propose and experimentally assess an innovative framework for supporting content-based authorship identification and analysis in social media networks.

引用

页码：589 / 604

页数：16

共 35 条

[1] Large-scale and Robust Code Authorship Identification with Deep Feature Learning [J].