Technical Program

09:00 – 09:10 Welcome
09:10 – 10:20 Keynote Talk
Aristides Gionis, Aalto University, Finland
Mining temporal networks
11:20 – 11:00 Coffee Break
11:00 – 12:20 Session I: OSNED and information sharing
Super-blockers and locality: The effect of network structure on information cascades.
Caitlin Gray, Lewis Mitchell and Matthew Roughan
Buzz in Social Media: Detection of Short-lived Viral Phenomena.
Clemens Deuaer, Nora Jansen, Jan Reubold, Oliver Hinz and Thorsten Strufe
(Disruptive ideas) From Reaction to Proaction: Unexplored Ways to the Detection of Evolving Spambots.
Stefano Cresci, Marinella Petrocchi, Angelo Spognardi and Stefano Tognazzi
Twitter and the press: an ego-centred analysis.
Chiara Boldrini, Mustafa Toprak, Marco Conti and Andrea Passarella
12:20 – 13:50 Lunch Break
13:50 – 15:00 Keynote Talk
Yelena Mejova, Qatar Computing Research Institute, Qatar
Capturing Digital Signals for Lifestyle Health Research
15:00 – 15:40 Coffee Break
15:40 – 17:00 Session II: OSNED and social behaviour
Analyzing a User’s Contributive Social Capital Based on Activities in Online Social Networks and Media.
Sebastian Schams, Jan Hauffa and Georg Groh
Predicting Argumentative Influence Probabilities in Large-Scale Online Civic Engagement.
Gaku Morio and Katsuhide Fujita
(Concise paper) Violent terms in Social Media: how do aggressive users and cyberbullies look like?
Mauro Coletto, Claudio Lucchese and Salvatore Orlando
Profiling OSN Users Based on Posting Patterns
Qiu Fang Ying, Dah Ming Chiu, Srinivasan Venkatramanan and Xiaopeng Zhang
17:00 – 17:10 Closing remarks

Keynotes


Name: Aristides Gionis
Title: Tracking node importance in temporal networks

Abstract
Large networks are being generated by applications that keep track of relationships between different data entities. Examples include online social networks recording interactions between individuals, sensor networks logging information exchanges between sensors, and more. There is a large body of literature on mining large networks, but most existing methods assume either static networks, or dynamic networks where the network topology is changing. On the other hand, in many real-world applications a continuous stream of interactions takes place on top of a relatively stable network topology, giving rise to different semantics than those of dynamic networks. In this talk we discuss a few different problems that consider networks as a stream of interactions (edges) over time. In particular, we consider the problems of maintaining neighborhood profiles and tracking important nodes. For the studied problems we present new algorithms, and discuss our analytical results. We also present experimental evaluation on real-world datasets and case studies on different application scenarios.

Bio
Aristides Gionis is a professor in the department of Computer Science in Aalto University. His previous appointments include being a visiting professor in the University of Rome and a senior research scientist in Yahoo! Research. He is currently serving as an action editor in the Data Management and Knowledge Discovery journal (DMKD), an associate editor in the ACM Transactions on Knowledge Discovery from Data (TKDD), and a managing editor in Internet Mathematics. He has contributed in several areas of data science, such as algorithmic data analysis, web mining, social-media analysis, data clustering, and privacy-preserving data mining. His current research is funded by the Academy of Finland (projects Nestor, Agra, AIDA) and the European Commission (project SoBigData).


Name: Yelena Mejova
Website: https://sites.google.com/site/yelenamejova/
Title:  Capturing Digital Signals for Lifestyle Health Research

Abstract
The scale and complexity of the traces of human behavior captured on the web has been a useful resource for tracking disease, pushing the lag of conventional health tracking to more real-time “now-casting”. These signals provide a rich source of information about the context of people’s health conditions, revealing their cultural, social, and personal attitudes and behaviors, making social media data especially useful for understanding lifestyle diseases, such as obesity and diabetes — conditions that claim more lives than infectious diseases worldwide. This talk will discuss the latest findings in lifestyle disease tracking via large social media collections encompassing population and individual scales.

Bio
Yelena Mejova (@yelenamejova) is a Scientist in the Social Computing Group at the Qatar Computing Research Institute, HBKU. Specializing in social media analysis and mining, her work concerns the quantification of health and wellbeing signals in social media, as well as tracking of social phenomena, including politics and news consumption. Recently, she co-edited a volume on the use of Twitter for social science in Twitter: A Digital Socioscope, and a special issue of Social Science Computer Review on Quantifying Politics Using Online Data. Previously, as a postdoctoral member of the Web Mining team at Yahoo! Research Barcelona, Yelena participated in the Linguistically Motivated Semantic Aggregation Engines (LiMoSINe) EU project. The design and evaluation of the resulting entity-based exploratory search system has been awarded the Best Paper Award at the ACM Conference on Information and Knowledge Management (CIKM) in 2013. Also, Yelena has published widely on sentiment analysis and its application to social media and political speech. She has given talks on the subject internationally, including Yandex’s Yet Another Conference (Moscow) and American University of Beirut.