Social networking services gain more often interest for research goals in several fields and applications thanks to the big amount of data that users daily post on them. Knowledge that has accumulated in the social sites enables to catch the reflection of real world events. In this work we present a general framework for event detection from Twitter. The framework aims to collect tweets related to a particular social event, in order to filter and classify those which can be relevant to detect malicious actions in Twitter communities. Relevant tweets are processed to raise an alert in case of anomaly within the collected set. © 2016 IEEE.
Detecting anomalies in Twitter stream for public security issues / Amato, Flora; Cozzolino, Giovanni; Mazzeo, Antonino; Romano, Sara. - (2016). (Intervento presentato al convegno 2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016; Bologna; Italy; 7 September 2016 through 9 September 2016; Category numberCFP16C29-ART; Code 124837) [10.1109/RTSI.2016.7740574].
Detecting anomalies in Twitter stream for public security issues
AMATO, FLORA;COZZOLINO, GIOVANNI;MAZZEO, ANTONINO;ROMANO, SARA
2016
Abstract
Social networking services gain more often interest for research goals in several fields and applications thanks to the big amount of data that users daily post on them. Knowledge that has accumulated in the social sites enables to catch the reflection of real world events. In this work we present a general framework for event detection from Twitter. The framework aims to collect tweets related to a particular social event, in order to filter and classify those which can be relevant to detect malicious actions in Twitter communities. Relevant tweets are processed to raise an alert in case of anomaly within the collected set. © 2016 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
