User Activity Analytics on the Social Web of News

Sihem Amr Yahia
Sihem Amer-Yahia, QCRI, Qatar & LIG - CNRS Grenoble, France


The proliferation of social media is undoubtedly changing the way people produce and consume news online. Editors and publishers in newsrooms need to understand user engagement and audience sentiment evolution on various news topics. News consumers want to explore public reaction on articles relevant to a topic and refine their exploration via related entities, topics, articles and tweets. I will present MAQSA, a system for social analytics on news. MAQSA provides an interactive topic-centric dashboard that summarizes social activity around news articles. The dashboard contains an annotated comment timeline, a social graph of comments, and maps of comment sentiment and topics. MAQSA relies on scalable algorithms that enable an interactive specification of topics, actions, and dates and dynamically process large collections of relevant articles.


Sihem Amer-Yahia is Principal Research Scientist at Qatar Computing Research Center (QCRI) and DR1 CNRS at LIG in Grenoble. Sihem's interests are at the intersection of large-scale data management and analytics, and social content at large. Until May 2011, she was Senior Scientist at Yahoo! Research for 5 years and worked on revisiting relevance models and top-k processing algorithms on datasets from Delicious, Yahoo! Personals and Flickr. Before that, she spent 7 years at AT&T Labs in NJ, working on XML query optimization and XML full-text search. Sihem is editor of the W3C XML full-text standard. She is a member of the VLDB Endowment and the ACM SIGMOD executive committee. Sihem is track chair at PVLDB and SIGIR this year. She serves on the editorial boards of ACM TODS, the VLDB Journal and the Information Systems Journal. Sihem received her Ph.D. in Computer Science from Univ. Paris-Orsay and INRIA in 1999, and her Diplome d'Ingenieur from INI, Algeria in 1994.