#Privacy #Social Networks #Prediction algorithms

How to Hide One's Relationships from Link Prediction Algorithms

Authors: Marcin Waniek, Kai Zhou, Yevgeniy Vorobeychik, Esteban Moro, Tomasz P Michalak, Talal Rahwan Journal: Scientific Reports volume 9, Article number: 12208 (2019) Link Abstract: Our private connections can be exposed by link prediction algorithms. To date, this threat has only been addressed from the perspective of a central authority, completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by studying how an individual can rewire her own network neighborhood to hide her sensitive relationships. ...

#Artificial Intelligence #Labour Markets

Towards understanding the impact of artificial intelligence on labor

Authors: Frank, Morgan R and Autor, David and Bessen, James E and Brynjolfsson, Erik and Cebrian, Manuel and Deming, David J and Feldman, Maryann and Groh, Matthew and Lobo, José and Moro, Esteban and Wang, Dashun and Youn, Hyejin and Rahwan, Iyad Journal: PNAS (2019). JOURNAL | PDF Abstract: Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. ...

#Deep Learning #Networks

How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology

Authors: Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Peter Krafft, Esteban Moro, Alex `Sandy’ Pentland Journal: Preprint (2018). arXiv Abstract: In this empirical paper, we investigate how learning agents can be arranged in more efficient communication topologies for improved learning. This is an important problem because a common technique to improve speed and robustness of learning in deep reinforcement learning and many other machine learning algorithms is to run multiple learning agents in parallel. ...

#Cryptocurrency #Collective Learning #Bitcoin

ScamCoins, S*** Posters, and the Search for the Next Bitcoin(TM): Collective Sensemaking in Cryptocurrency Discussions

Authors: Eaman Jahani, Peter M. Krafft, Yoshihiko Suhara, Esteban Moro, Alex “Sandy” Pentland Journal: Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 79 (November 2018), 28 pages. LINK Abstract: Participants in cryptocurrency markets are in constant communication with each other about the latest coins and news releases. Do these conversations build hype through the contagiousness of excitement, help the community process information, or play some other role? Using a novel dataset from a major cryptocurrency forum, we conduct an exploratory study of the characteristics of online discussion around cryptocurrencies. ...

#Big Data #Data Science #Gender Gap #Social Media

Analyzing gender inequality through large-scale Facebook advertising data

Authors: David Garcia, Yonas Mitike Kassa, Angel Cuevas, Manuel Cebrian, Esteban Moro, Iyad Rahwan, and Ruben Cuevas Journal: PNAS June 19, 2018. 201717781. LINK Abstract: Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media, in particular, are prone to gen- der inequality, an important issue given the link between social media use and employment. ...

#Social Media #Sentiment #Weather

Weather impacts expressed sentiment

Authors: Patrick Baylis, Nick Obradovich, Yury Kryvasheyeu, Haohui Chen, Lorenzo Coviello, Esteban Moro, Manuel Cebrian, James H. Fowler Journal: PLoS ONE 13(4): e0195750 (2018) LINK Abstract: We conduct the largest ever investigation into the relationship between meteorological con- ditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016. ...