Risultati di ricerca

con K2 Items e Tipo , e con Elenco Pubblicazioni - Publications e K2 Category , e con STEFANO CRESCI e Author sono stati trovati i seguenti risultati:

Marco Avvenuti, Stefano Cresci, Andrea Marchetti, Carlo Meletti, Maurizio Tesconi, "Predictability or Early Warning: Using Social Media in Modern Emergency Response", IEEE Internet Computing 20(6): 4-6, 2016
Avvenuti, M., Cimino, M. G., Cresci, S., Marchetti, A., & Tesconi, M. (2016). A framework for detecting unfolding emergencies using humans as sensors. SpringerPlus, 5(1), 1, Springer
The advent of online social networks (OSNs) paired with the ubiquitous proliferation of smartphones have enabled social sensing systems. In the last few years, the aptitude of humans to spontaneously collect and timely share context information has...
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2016). DNA-inspired online behavioral modeling and its application to spambot detection. IEEE Intelligent Systems, 31(5), 58-64, IEEE
A novel approach to modeling online user behavior extracts and analyzes digital DNA-inspired sequences from users’ online actions. Standard DNA analysis techniques can then discriminate between genuine and spambot accounts.
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2016). Social fingerprinting - or the truth About you. ERCIM News, 106, 26-27, ERCIM
Inspired by biological DNA, we model the behaviour of online users as “Digital DNA” sequences, introducing a strikingly novel, simple, and effective approach to discriminate between genuine and spambot online accounts.
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015). Fame for sale: efficient detection of fake Twitter followers. Decision Support Systems, 80, 56-71, Elsevier
Fake followers are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for the social platform and beyond, since they may alter concepts like popularity and influence in the...
Avvenuti, M., Cresci, S., Del Vigna, F., & Tesconi, M. (2016). Impromptu Crisis Mapping to Prioritize Emergency Response. Computer, 49(5), 28-37.
To visualize post-emergency damage, a crisis-mapping system uses readily available semantic annotators, a machine-learning classifier to analyze relevant tweets, and interactive maps to rank extracted situational information. The system was validated...
Avvenuti, Del Vigna, Cresci, Marchetti, Tesconi, "Pulling Information from Social Media in the Aftermath of Unpredictable Disasters", 2015 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)
Social media have become a primary communication channel among people and are continuously overwhelmed by huge volumes of User Generated Content. This is especially true in the aftermath of unpredictable disasters, when users report facts, descriptions...
Cresci, S., Cimino, A., Dell’Orletta, F., & Tesconi, M. (2015, November). Crisis mapping during natural disasters via text analysis of social media messages. In International Conference on Web Information Systems Engineering (pp. 250-258), Springer
Recent disasters demonstrated the central role of social media during emergencies thus motivating the exploitation of such data for crisis mapping. We propose a crisis mapping system that addresses limitations of current state-of-the-art approaches by...
Cresci, S., Tesconi, M., Cimino, A., & Dell'Orletta, F. (2015, May). A linguistically-driven approach to cross-event damage assessment of natural disasters from social media messages. In Proceedings of the 24th WWW, ACM
This work focuses on the analysis of Italian social media messages for disaster management and aims at the detection of messages carrying critical information for the damage assessment task. A main novelty of this study consists in the focus on...
Cimino, A., Cresci, S., Dell’Orletta, F., & Tesconi, M. (2014). Linguistically-motivated and lexicon features for sentiment analysis of italian tweets. 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2014)
In this paper we describe our approach to EVALITA 2014 SENTIment POLarity Classification (SENTIPOLC) task. We participated only in the Polarity Classification subtask. By resorting to a wide set of general–purpose features qualifying the lexical and...

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