Outliers and Anomaly Detection in Online Social Networks
External reference persons Francesca Soro (DET)
Thesis type DATA ANALYSIS, MODELLING
Description Online social networks, such as Instagram and Facebook, allow users to interact and debate with each other. In this reserarch, the candidate will analyze large quantities of data collected from social networks, possibily using big data techniques (such as Pyspark). Then, the student will model the phenomena that characterize the usage of the social network. In particular, the student will propose a methodology to find outliers and anomaly detection. Finally, these models will be validated and will help recognizing the differences in usage and forecast possible future scenarios.
Required skills Modellazione, analisi dati, python programing, preferentially big data (pyspark)
Notes Contact firstname.lastname@example.org
Deadline 16/07/2023 PROPONI LA TUA CANDIDATURA