The deluge of online and offline misinformation is overloading the exchange of ideas upon which democracies depend. Many have argued that echo chambers are increasingly constricting the ability of alternative perspectives to provide a check on one’s viewpoints. Suffering fragmentation and declining public trust, the Fourth Estate struggles to carry out its traditional editorial role distinguishing facts from fiction. Without those safeguards, fake news, conspiracy theories, and deceptive social bots proliferate, facilitating the manipulation of public opinion. Countering misinformation while protecting freedom of speech will require collaboration between stakeholders across industry, journalism, and academia. To foster such collaboration, the Workshop on Digital Misinformation will be held in conjunction with the 2017 International Conference on Web and Social Media (ICWSM) in Montreal, on May 15, 2017. Continue reading ICWSM 2017 Workshop on Digital Misinformation
Predicting popularity and success in cultural markets is hard due to strong inequalities and inherent unpredictability. A good example comes from the world of fashion, where industry professionals face every season the difficult challenge of guessing who will be the next seasons’ top models. A recent study by CNetS graduate student Jaehyuk Park, research scientist Giovanni Luca Ciampaglia (also at the IU Network Science Institute), and research scientist Emilio Ferrara (now at the University of Southern California) is now showing that early success in modeling can be predicted from the digital traces left by the buzz on social media such as Instagram. The study has been accepted for presentation at the 19th ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW’16). The work has been covered in the media by the MIT Technology Review, Die Welt, Fusion, and iTNews.
Speaker: Mike Conover (LinkedIn)
Title: Building Machine Learning Systems at LinkedIn
Room: Informatics East 122
Abstract: This talk details patterns and machine learning systems to provide our members with actionable, relevant opportunities to nurture their professional networks. Featuring the Connected mobile app as an in-depth case study of how to combine compute-intensive features describing billions of relationships with information that isn’t known until the moment a user opens the app, in this talk we’ll discuss the architectural, modeling, and experimentation patterns leveraged by the Connected relevance team to rank and serve mobile content. Additionally, this session will touch on the human element of machine learning product development, outlining collaboration and communication patterns for working effectively across the organization – from reporting and documentation to evangelism, skills transfer and user experience research. Taken together, these insights will provide a detailed picture of some of LinkedIn’s best practices for building data products at a global scale.
Biography: A graduate of the Indiana University School of Informatics and Computing, Mike Conover builds machine learning technologies that leverage the behavior and relationships of hundreds of millions of people. A senior data scientist at LinkedIn, Mike has a Ph.D. in complex systems analysis with a focus on information propagation in large-scale social networks. His work has appeared in the New York Times, the Wall Street Journal, Forbes, Science, MIT Technology Review and on National Public Radio.