My research primarily focuses on leveraging human intelligence to solve problems that go beyond the capability of machines. You can find my publications here. I am broadly interested in:
Crowdsourcing
Human Computation
Information Retrieval
Web Mining
Social Computing
Brief CV
2013-Present
Ph.D Candidate L3S Research Center, Leibniz Universität Hannover, Germany.
2011-2012
Master of Science in Computer Science Delft University of Technology (TU Delft), Delft, The Netherlands.
2006-2010
Bachelor of Technology in Computer Science and Engineering Vellore Institute of Technology University, Vellore, Tamil Nadu, India.
Selected Works
CHI 2015
Understanding Malicious Behavior in Crowdsourcing Platforms: The Case of Online Surveys
Crowdsourcing is increasingly being used as a means to tackle problems requiring human intelligence. With the evergrowing worker base that aims to complete microtasks on crowdsourcing platforms in exchange for financial gains, there is a need for stringent mechanisms to prevent exploitation of deployed tasks. Quality control mechanisms need to accommodate a diverse pool of workers, exhibiting a wide
range of behavior. A pivotal step towards fraud-proof task design is understanding the behavioral patterns of microtask workers. In this paper, we analyze the prevalent malicious activity on crowdsourcing platforms and study the behavior exhibited by trustworthy and untrustworthy workers, particularly on crowdsourced surveys. Based on our analysis of the typical malicious activity, we define and identify different types of workers in the crowd, propose a method to measure malicious activity, and finally present guidelines for the efficient design of crowdsourced surveys.
WebSci 2015
Ranking Buildings and Mining the Web for Popular Architectural Patterns
Knowledge about the reception of architectural structures is crucial for architects and urban planners. Yet obtaining such information has been a challenging and costly activity. However, with the advent of the Web, a vast amount of structured and unstructured data describing architectural structures has become available publicly. This includes information about the perception and use of buildings (for instance, through social media), and structured information about the building’s features and characteristics (for instance, through public Linked Data). Hence, first mining (i) the popularity of buildings from the social Web and (ii) then correlating such rankings with certain features of buildings, can provide an efficient method to identify successful architectural patterns. In this paper we propose an approach to rank buildings through the automated mining of Flickr metadata. By further correlating such rankings with building properties described in Linked Data we are able to identify popular patterns for particular building types (airports, bridges, churches, halls, and skyscrapers). Our approach combines crowdsourcing with Web mining techniques to establish influential factors, as well as ground truth to evaluate our rankings. Our extensive experimental results depict that methods tailored to specific structure types allow an accurate measurement of their public perception.
HT 2014
A Taxonomy of Microtasks on the Web
Nowadays, a substantial number of people are turning to crowdsourcing, in order to solve tasks that require human intervention. Despite a considerable amount of research done in the field of crowdsourcing, existing works fall short when it comes to classifying typically crowdsourced tasks. Understanding the dynamics of the tasks that are crowdsourced and the behaviour of workers, plays a vital role in efficient task-design. In this paper, we propose a two-level categorization scheme for tasks, based on an extensive study of 1000 workers on CrowdFlower. In addition, we present insights into certain aspects of crowd behavior; the task affinity of workers, effort exerted by workers to complete tasks of various types, and their satisfaction with the monetary incentives.
Tutorials and Lectures
May 22nd, 2016
WebSci'16 - It's Getting Crowded! How to Use Crowdsourcing Effectively for Web Science Research Hannover, Germany. Tutorial Slides
May 17th, 2016
ICWSM'16 - How to Use Crowdsourcing Effectively for Social Media Research Cologne, Germany. Tutorial Slides
April 26th, 2016
Web Science Sommersemester'16 L3S Research Center, Leibniz Universität Hannover, Germany. Slide Deck
