Raz Schwartz

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Raz Schwartz is a postdoctoral researcher at Cornell Tech NYC and a Magic grant fellow at the Brown Institute for Media Innovation at Columbia Journalism School. Raz studies social media usage in urban settings and focuses on examining local social interactions by applying computational social science methods. Prior to joining Cornell Tech NYC, Raz was a(More)
With the increasing volume of location-annotated content from various social media platforms like Twitter, Instagram and Foursquare, we now have real-time access to people's daily documentation of local activities, interests and attention. In this demo paper, we present CityBeat, a real-time visualization of hyper-local social media content for cities. The(More)
An increasing number of location-annotated content available from social media channels like Twitter, Instagram, Foursquare and others are reflecting users' local activities and their attention like never before. In particular, we now have enough available data to start extracting real-time local information from social media. In this paper, we focus on the(More)
As more people tweet, check-in and share pictures and videos of their daily experiences in the city, new opportunities arise to understand urban activity. When aggregated, these data can uncover invaluable local insights for local stakeholders such as journalists, first responders and city officials. To better understand the needs and requirements for this(More)
The role of algorithms in the detection, curation and broadcast of news is becoming increasingly prevalent. To better understand this role we developed CityBeat, a system that implements what we call “editorial algorithms” to find possible news events. This fully functional system collects real time geo-tagged information from social media, finds key(More)
Challenges of the local context such as encouraging civic engagement and facilitating collaboration to address local issues have motivated researchers and practitioners to explore the role of technologies in supporting life in cities, neighborhoods, and local communities. The goal of this workshop is to open a discussion on how to design, build and study(More)
The Workshop on Computational Personality Recognition allowed participants to compare the results of their systems on a common benchmark. Unlike competitive shared tasks, the workshop did not focus just on performance, but rather on discovering which feature sets, resources, and learning techniques are useful in the extraction of personality from text.(More)