Distinguished Speaker Series
Jennifer Chayes
Technical Fellow and Managing Director of Microsoft Research New England, New York City, and Montreal
Talk title TBA
Monday
Nov 4, 2019
Watch video
1:30 pm
177 Huntington Ave
11th floor
About the speaker
Jennifer Chayes is Technical Fellow and Managing Director of Microsoft Research New England, New York City, and Montreal. Before joining Microsoft, she was Professor of Mathematics at UCLA. She has received numerous awards for both leadership and scientific contributions, including the Anita Borg Institute Women of Vision Leadership Award, the John von Neumann Lecture Award of the Society for Industrial and Applied Mathematics, and election to the American Academy of Arts and Sciences and the National Academy of Sciences. Her research areas include phase transitions in computer science, structural and dynamical properties of networks including graph algorithms, and computational biology. Chayes is one of the inventors of the field of graphons, which are widely used for the machine learning of large-scale networks. Her recent work focuses on machine learning, including applications in cancer immunotherapy, ethical decision making, and climate change.
Distinguished Speaker Series
Jennifer Chayes
Technical Fellow and Managing Director of Microsoft Research New England, New York City, and Montreal
Talk title TBA
Mon
Nov 4, 2019
1:30 pm
177 Huntington Ave
11th floor
ADD to calendar

The interplay between structural segregation and opinion polarization has motivated the work of scientists, writers, artists, and activists during the years. Segregation can be also a facilitator for the diffusion of misinformation, and debates that take place on social media have also a strong impact on the evolution of the network itself. Moreover, the consolidation of echo-chambers and the emergence of bots are rapidly changing the way we interact with others, and they are forcing our societies to rethink themselves, the way we vote, the freedom of speech, censorship policies, and so on. Nevertheless, these phenomena need more investigations because their dynamics and the always changing surrounding context are difficult to be framed within a unique framework. We need a multidisciplinary approach, and scientists need many several computational tools from traditionally different areas of computer science. Complex network analysis, computational linguistics, machine learning provide many methdologies and techniques, and it is not always trivial to use them adequately. This talk presents a very subjective review of what we have done in the last few years in collaboration with many other colleagues to model the spread of misinformation in segregated networks, to analyse how users’ stance in polarized political debates may be tighltly connected to the underlying structure of relationships, and how algorithms can be used to provide more efficient tools to test structural balance in signed networks that perfectly describe polarized communities.

References

[1] LM Aiello, A Barrat, C Cattuto, G Ruffo, R Schifanella, Link creation and profile alignment in the aNobii social network, 2010 IEEE 2nd Int.. Conf. on Social Computing, 249-256

[2] LM Aiello, A Barrat, C Cattuto, G Ruffo, R Schifanella, Link creation and information spreading over social and communication ties in interest based online social network, EPJ Data Science 1 (1), 12

[3] LM Aiello, M. Deplano, R Schifanella, G Ruffo, People are Strange when you’re a Stranger: Impact and Influence of Bots on Social Networks, in Proc. of the 6th Intern. AAAI Conf. on Weblogs and Social Media (ICWSM’12), Dublin, Ireland, 2012

[4] M Tambuscio, G Ruffo, A Flammini, and F Menczer. 2015. Fact-checking Effect on Viral Hoaxes: A Model of Misinformation Spread in Social Networks. In Proc. of the 24th Int. Conf. on World Wide Web (WWW '15 Companion)

[5] M Tambuscio, D F M Oliveira, G L Ciampaglia, G Ruffo, Network segregation in a model of misinformation and fact-checking, Journal of Computational Social Science (2018) 1: 261

[6] M Tambuscio, G. Ruffo, Fact-checking strategies to limit urban legends spreading in a segregated society, to appear in Applied Network Science Journal, Springer

[7] M Lai, M Tambuscio, V Patti, P Rosso, G. Ruffo, Stance Polarity in Political Debates: a Diachronic Perspective of Network Homophily and Conversations on Twitter, submitted

[8] A T E Capozzi, V Patti, G Ruffo, and C Bosco. 2018. A Data Viz Platform as a Support to Study, Analyze and Understand the Hate Speech Phenomenon. In Proceedings of the 2nd International Conference on Web Studies (WS.2 2018), ACM

about the speaker
Jennifer Chayes is Technical Fellow and Managing Director of Microsoft Research New England, New York City, and Montreal. Before joining Microsoft, she was Professor of Mathematics at UCLA. She has received numerous awards for both leadership and scientific contributions, including the Anita Borg Institute Women of Vision Leadership Award, the John von Neumann Lecture Award of the Society for Industrial and Applied Mathematics, and election to the American Academy of Arts and Sciences and the National Academy of Sciences. Her research areas include phase transitions in computer science, structural and dynamical properties of networks including graph algorithms, and computational biology. Chayes is one of the inventors of the field of graphons, which are widely used for the machine learning of large-scale networks. Her recent work focuses on machine learning, including applications in cancer immunotherapy, ethical decision making, and climate change.