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Collaborating Authors

Mathematical Foundations for Social Computing

Communications of the ACM

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.


Apple to pay $24.9 million to settle Siri patent lawsuit

AITopics Original Links

Apple has agreed to pay $24.9 million to a patent holding company to resolve a 5-year-old lawsuit accusing Siri of infringing one of its patents. Apple will pay the money to Marathon Patent Group, the parent company of Texas firm Dynamic Advances, which held an exclusive license to a 2007 patent covering natural language user interfaces for enterprise databases. Marathon reported the settlement in a filing with the U.S. Securities and Exchange Commission Tuesday. On Wednesday, in response to the settlement, Magistrate Judge David Peebles of U.S. District Court for the Northern District of New York dismissed a lawsuit against Apple filed by Dynamic Advances and Rensselaer Polytechnic Institute in Troy, New York, where the natural language technology was created. A trial had been scheduled to begin early next month in Syracuse, New York.


Mathematical Foundations for Social Computing

#artificialintelligence

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.


Facebook data could predict spread of disease outbreaks says new research on 'social-connectedness'

Daily Mail - Science & tech

Researchers say evaluating the'social-connectedness' of regions using Facebook data could give epidemiologists another tool in judging the spread of infectious disease outside of geographic proximity and population density. The study, which appears in the preprint journal ArXiv and is authored by researchers from New York University, found links between two hotspots of the ongoing COVID-19 pandemic - Westchester County, New York and Lodi province in Italy - to areas with correlating connections on the social media platform, Facebook. Using an equation developed by the same researchers in 2017 called the'Social Connectedness Index' the study was able to make correlations between the spread of COVID-19 from Westchester County and Lodi to geographically disparate locations like ski resorts on Florida and vacation spots in Rimini, Italy near the Adriatic sea. Those correlations remained even after controlling for wealth, population density, and geographic proximity according to researchers. Levels of social connectedness didn't always correlate to the disproportionate spread of the virus, however.


Multi-channel discourse as an indicator for Bitcoin price and volume movements

arXiv.org Machine Learning

This research aims to identify how Bitcoin-related news publications and online discourse are expressed in Bitcoin exchange movements of price and volume. Being inherently digital, all Bitcoin-related fundamental data (from exchanges, as well as transactional data directly from the blockchain) is available online, something that is not true for traditional businesses or currencies traded on exchanges. This makes Bitcoin an interesting subject for such research, as it enables the mapping of sentiment to fundamental events that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely takes place on online forums and chat channels. In stock trading, the value of sentiment data in trading decisions has been demonstrated numerous times [1] [2] [3], and this research aims to determine whether there is value in such data for Bitcoin trading models. To achieve this, data over the year 2015 has been collected from Bitcointalk.org, (the biggest Bitcoin forum in post volume), established news sources such as Bloomberg and the Wall Street Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and bitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we find weak to moderate correlations between forum, news, and Reddit sentiment and movements in price and volume from 1 to 5 days after the sentiment was expressed. A Granger causality test confirms the predictive causality of the sentiment on the daily percentage price and volume movements, and at the same time underscores the predictive causality of market movements on sentiment expressions in online communities