Real-time monitoring of Twitter traffic by using semantic networks

被引：3

作者：

Bisio, Federica ^{[1
]}

Meda, Claudia ^{[1
]}

Zunino, Rodolfo ^{[1
]}

Surlinelli, Roberto ^{[2
]}

Scillia, Eugenio ^{[2
]}

Ottaviano, Augusto ^{[2
]}

机构：

[1] Univ Genoa, Dept Elect Elect & Telecommun Engn & Naval Archit, Genoa, Italy

[2] Italian Natl Police, Dept Publ Secur, Minist Interior, Genoa, Italy

来源：

PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015) | 2015年

关键词：

Text Mining; Social Network Analysis; Semantic Network;

D O I：

10.1145/2808797.2809371

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data from Social Networks and microblogs can provide useful information for prevention and investigation purposes, provided unstructured information is processed at both the lexical and the semantic level. The proposed methodology introduces a comprehensive Semantic Network (ConceptNet) in the interpretation chain of Twitter traffic. This additional interpretation level greatly enhances the effectiveness of semi-automated tools for monitoring purposes. In particular, the paper shows that the combined use of semantic and text-mining clustering tools also allows law-enforcement operators to early detect and track unscheduled events. Experimental results demonstrate the method effectiveness in real cases.

引用

页码：966 / 969

页数：4

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