Social Networks are responsible of generating a huge amount of information, intrinsically heterogeneous and coming from different sources. In the social networks domain, the number of active users is impressive, active users process and publish information in different formats and data remain heterogeneous in their topics and in the published media (text, video, images, audio, etc.). In this work, we present a general framework for event detection in processing of heterogeneous data from social networks. The framework we propose, implements some techniques that users can exploit for malicious events detection on Twitter. © 2019, Springer Nature Switzerland AG.
Textual Processing in Social Network Analysis / Amato, F.; Balzano, W.; Cozzolino, G.; de Luca, A.; Moscato, F.. - 927:(2019), pp. 822-832. [10.1007/978-3-030-15035-8_80]
Textual Processing in Social Network Analysis
Amato, F.
;Balzano, W.;Cozzolino, G.;de Luca, A.;
2019
Abstract
Social Networks are responsible of generating a huge amount of information, intrinsically heterogeneous and coming from different sources. In the social networks domain, the number of active users is impressive, active users process and publish information in different formats and data remain heterogeneous in their topics and in the published media (text, video, images, audio, etc.). In this work, we present a general framework for event detection in processing of heterogeneous data from social networks. The framework we propose, implements some techniques that users can exploit for malicious events detection on Twitter. © 2019, Springer Nature Switzerland AG.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
