StorySourcing: Telling Stories with Humans & Machines

Speaker:  Lora Aroyo – New York City, NY, United States
Topic(s):  Human Computer Interaction


Stories connect us with other people, guide us in exploring unfamiliar places, bring us to discover new things. They are all around us - in our daily lives, in museum exhibitions, in movies and songs. Everybody has them, but only few used to tell them. The Web has changed this. Now, we co-create and share stories everywhere - blurring the lines between physical and online worlds with our social media timelines and video streaming. It is a symbiotic cooperation between humans and machines. However, we still do not understand the implicit and creative aspects of narration. In this talk I connect serendipitous discovery, creative thinking and human computation in the context of narrative building. This is illustrated with examples from smart culture, such as DIVE+ (, where humanities scholars explore and discover stories with cultural heritage objects from media collections online. DIVE+ is the result of a interdisciplinary collaboration between computer scientists, humanities scholars, cultural heritage professionals currently integrated in the Dutch national CLARIAH (Common Lab Research Infrastructure for the Arts and Humanities) research infrastructure. In this talk, I also show how human computation can help acquire, capture and harness diversity in human interpretation to make such explorative journeys creative and serendipitous. The experience with the crowd leaves one thing absolutely clear - there is no single notion of truth, but rather a spectrum that has to account for context, opinions, perspectives and shades of grey. CrowdTruth ( is a widely used crowdsourcing methodology adopted by industrial partners and public organizations, e.g. Google, IBM, New York Times, Crowdynews, The Netherlands Institute for Sound and Vision, in a multitude of domains, e.g. AI, news, medicine, social media, cultural heritage, social sciences. The central characteristic of CrowdTruth is harnessing the diversity in human interpretation to capture the wide range of opinions and perspectives, and thus, provide more reliable and realistic real-world annotated data for training and evaluating machine learning components. Unlike other methods, we do not discard dissenting votes, but incorporate them into a richer and more continuous representation of truth. Creating this more complex notion of truth contributes directly to the larger discussion on how to how to distinguish facts from opinions, perspectives and ultimately to make the Web more reliable, diverse and inclusive. 

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About this Lecture

Number of Slides:  83
Duration:  60 minutes
Languages Available:  English
Last Updated: 

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