Talks & Seminars
Can you believe what you read online? Modeling and Predicting Trustworthiness of Online Textual Information
Mr. V. G. Vinod Vydiswaran, University of IIlinois at Urbana Champaign
Date & Time: January 18, 2012 14:15
Venue: SIC 201, 02nd Floor, C Block, Dept. of Computer Science & Engg., Kanwal Rekhi Building
With the advent of Web 2.0, more and more data is being produced and consumed online. Individuals, especially from the younger generation, are getting informed and building their opinions based on what they watch, read, and discuss on the Web. At the same time, it is also easy for anyone, even nefarious agents, to generate (dis)information and make it widely available. In this scheme of things, can you trust all the content available online In this talk, I will present some of the challenges in computing trustworthiness of free text claims and argue the need to go beyond structured, extraction-centric approaches to unstructured, textual evidence-driven trust models. Building trustworthiness models for textual claims involves understanding the different ways in which claims can be expressed in free text, aggregating weak signals based on the quality of evidence, addressing differential trust levels of sources (experts vs. laymen, commercial vs. governmental websites, etc.), and exploring the use of community knowledge (expressed in forums) to enable Web users decide credibility of sources and claims. I will present some of my work in this direction.
Speaker Profile:
Vinod is a doctoral candidate at the University of Illinois at Urbana-Champaign working with Prof.ChengXiang Zhai and Prof.Dan Roth. His research interests include text informatics, natural language processing, machine learning, and information extraction. Vinod has a Masters degree in Technology from the Indian Institute of Technology Bombay, where he worked on Conditional models for Information Extraction from websites with Prof. Sunita Sarawagi. He also worked as a Research Engineer at Yahoo! R&D center at Bangalore and was involved in building Information Extraction technologies for large scale extraction from the Web.
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