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Browsing by Author "Kohler, Rachel"

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    CS 5604 INFORMATION STORAGE AND RETRIEVAL Front-End Team Fall 2016 Final Report
    Kohler, Rachel; Tasooji, Reza; Sullivan, Patrick (Virginia Tech, 2016-12-08)
    Information Retrieval systems are a common tool for building research and disseminating knowledge. For this to be possible, these systems must be able to effectively show varying amounts of relevant information to the user. The information retrieval system is in constant interaction with the user, who can modify the direction of their search as they gain more information. The front-end of the information retrieval system is where this important communication happens. As members of Dr. Fox's class on Information Storage and Retrieval, we are tasked with understanding and making progress toward answering the question: how can we best build a state-of-the-art information retrieval and analysis system in support of the IDEAL (Integrated Digital Event Archiving and Library) and GETAR (Global Event and Trend Archive Research) projects? As the front-end design and development team, our responsibility to this project is in creating an interface for users to explore large collections of tweet and webpage data. Our goal in this research effort is to understand how users search for information and to support these efforts with an accurate and usable interface. We support various methods of searching, such as query driven searches, faceted search and browsing, and filtering of information by topic. We implemented user management and logging to support future work in recommendations. Additionally, we integrated a framework for future efforts in providing users with insightful visualizations which will allow them to explore social network and document interrelation data.
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    Supporting Open Source Investigative Journalism with Crowdsourced Image Geolocation
    Kohler, Rachel (Virginia Tech, 2017-08-10)
    Journalists rely on image and video verification to support their investigations and often utilize open source tools to verify user generated content, but current practice requires experts be involved in every step of the process. Additionally, lacking custom tools to support verification efforts, experts are often limited to the utility of existing, openly available tools, which may or may not support the interactions and information gathering they require. We aim to support the process of geolocating images and videos through crowdsourcing. By enabling crowd workers to participate in the geolocation process, we can provide investigative journalists with efficient and complete verification of image locations. Parallelizing searching speeds up the verification process as well as provides a more extensive search, all while allowing the expert to follow up on other leads or investigative work. We produced a software prototype called GroundTruth which enables crowd workers to support investigative journalists in the geolocation of visual media quickly and accurately. Additionally, this work contributes experimental results demonstrating how the crowd can be utilized to support complex sensemaking tasks.