About me.

I am a Ph.D. student in Engineering in Computer Science, working at Sapienza University of Rome, under the supervision of Prof. Aris Anagnostopoulos.
Previously, I received my M.Sc. in Engineering in Computer Science from Sapienza University of Rome, working under the supervision of Prof. Roberto Baldoni.

As a member of the Web Algorithmics and Data Mining Lab (WADAM), my research evolves around modeling and mining dynamic processes on large-scale social and information networks. My investigation lies on the interface of computational methods and social theories, combining them to analyze effectively social media data, enabling to study human behavioural processes that have an impact on the structure, evolution and lifecycle of social networks.

Main topics
information diffusion, influence analysis, link formation, event detection, network inference
user modeling, spatio/temporal urban mining, advertising, dynamic graph visualization, recommender systems and distributed computing

Contact information

Mara Sorella
Department of Computer, Control
and Management Engineering
Sapienza University of Rome
Via Ariosto, 25
00185, Rome (Italy)
Room: A220
Phone: +39 06.77274036


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Here you can find a list of my recent projects:

Cultural dynamics

A fundamental open question is understanding the role that influence and selection play in shaping the evolution of socio-cultural systems. Quantifying these forces in real settings is still a big challenge, especially in the large-scale case in which the entire social network between the users may not be known, and only longitudinal data in terms of sizes of cultural groups (e.g., political affiliation, market share, cultural tastes) may be available. We propose an influence and selection model encompassing an explicit characterization of the cultural feature space in the form of a natural equation-based macroscopic model, following an approach by Kempe et al. Our main goal is to estimate edge influence strengths and selection parameters from an observed time series. We perform learning and prediction on real datasets from Last.FM and Wikipedia, achieving good prediction accuracy.

Interest Driven Targeted Advertising in Cities using Twitter

Applying to the context of advertising of events and products in cities, use the Twitter social network to gather information about users' degree of interest in given advertising categories and about the common routes that they follow, characterizing in this way each zone in a given city. Given an advertisement belonging to one of the examined categories, we estimate the most promising areas to be selected for the placement of an ad that can maximize its targeted effectiveness; we show that this approach behaves better with respect to a currently used approach based on rough crowd estimates. To the best of our knowledge this is the first work on offline advertising in urban areas making use of (publicly available) data from social networks.

Entweety Graph

Dynamic visualization of a time-evolving co-mentioning graph of entities in a Twitter stream using Python and d3.js.


This project aims at developing a full stack application for tracking a (possibly event-related) query on Twitter in real time, mining the stream of matched tweets for top keywords live, thus allowing the end user to have a grasp of the evolution of interest in time related to the query. The implementation is based on a Storm Cluster for stream processing equipped with a Redis queuing system (+ other technologies, like Node.js web server with RESTful interface for dynamically building a web page the end user, to display the aggregated rankings provided by the cluster in real time)

Wadam Dataset Repository

An online search engine for the WADAM dataset repository, curated by the Web Algorithmics and Data mining research group. Based on the awesome Node.js and MongoDB.


A Java based application to deploy, run, monitor and gather results of remote tasks (experiments!) executing on cloud or privately-owned machines.


  • A. Anagnostopoulos, F. Petroni and M. Sorella
    "Targeted Interest-Driven Advertising in Cities using Twitter"
    Proc. of the 10th International conference on Web and Social Media (ICWSM 2016), (pdf), Cologne, Germany. May 2016.

  • A. Anagnostopoulos and M. Sorella
    "Learning a Macroscopic Model of Cultural Dynamics"
    Proc. of the 2015 IEEE International Conference on Data Mining (ICDM 2015) (pdf, extended version), Atlantic City, NJ, USA. November 2015.

  • R. Baldoni, S. Bonomi, G.A. Di Luna, L. Montanari, and M. Sorella
    " Understanding (Mis)Information Spreading for Improving Corporate Network Trustworthiness"
    Proc. of the 14th European Workshop on Distributed Computing, 2013 (EWDC 2013), Coimbra, Portugal. May 2013.


  • Learning a Macroscopic Model of Cultural Dynamics (slides)
    2015 IEEE International Conference on Data Mining (ICDM 2015), Atlantic City, NJ, USA. November 2015.

    COLITA: Collaborative Interest-driven targeted advertising (slides)
    International Conference on Computational Social Science (ICCSS 2015), Helsinki, Finland. June 2015.

  • Understanding (Mis)Information Spreading for Improving Corporate Network Trustworthiness (slides)
    M.Sc. dissertation @ Faculty of Engineering (Sapienza University of Rome), Rome, Italy. July 2013.


While offline, I enjoy playing piano and guitar.
I also like home-making 100% organic ecological detergents and make-up and maintaining my freshwater aquarium (and the wild beasts living therein).
I am a proud member of the Ninux Wireless Community Network.
At Ninux, we enjoy building a large wireless network putting directional antennas like this on our rooftops.

Social outlets:

You can download an updated cv here (April, 2016).

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