The Barometer helps you to discover controversial topics, issues, people and more.
With the Event Blender you can investigate the controversy around events and topics in real time.
We measure controversy across media such as news articles, scientific papers and social media.
ControCurator aims to enable for modern information access systems to discover and understand controversial topics and events by bringing together different types of crowds (niches of experts, lay crowds and engaged social media contributors) and machines in a joint active learning workflow for the creation of adequate training
data both realtime and offline.
In ControCurator the Accurator.nl tools and algorithms will be validated in new domains with different niches of experts (media studies scholars and professionals from the media industry), and with different types of media (social media, videos and video transcripts/subtitles) and will be also combined with complementary paid crowdsourcing and machine processing of text.
Bob van de Velde
Our ControCurator paper abstract titled “ControCurator: Understanding Controversy Using Collective Intelligence” has been accepted at Collective Intelligence 2017. In this paper we describe the aspects of controversy: the time-persistence, emotion, multiple actors, polarity and openness. Using crowdsourcing, the ControCurator dataset of 31888 controversy annotations was obtained for the relevance of these aspects to 5048 Guardian[…]
Our demo of ControCurator titled “ControCurator: Human-Machine Framework For Identifying Controversy” will be shown at ICT Open 2017. In this demo the ControCurator human-machine framework for identifying controversy in multimodal data is shown. The goal of ControCurator is to enable modern information access systems to discover and understand controversial topics and events by bringing together[…]
The aim of the ControCurator project is for modern information access systems to discover and understand controversial topics and events. This is done by 1) bringing together different types of crowds: niches of experts, lay crowds and engaged social media contributors; and 2) using machines in a joint active learning workflow for real-time and offline[…]