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    FeedStrap is a web application that gathers links from collaborators and stores them in a searchable report-friendly database. Using FeedStrap, users can easily tag, search and export content related to web links.

    FeedStrap was developed organically at the Department of Veterans Affairs using open source tools. The current content pertains to strategic foresight and policy analysis. However, the same technology can be used by any organization or office that wants to better organize web links, spur collaboration and simplify reporting of web content.

    Problem Statement

    Currently, many analysts share internet research by copying and pasting web links into email. Using this method, content can be lost if the linked websites change. Also, links are not ultimately stored in a way that they can be easily retrieved by team members conducting research in associated areas.

    Analyst frequently try to resolve this problem by compiling information into shared Excel worksheets or Word documents. However static documents suffer from version inconsistencies, and lack of quick search-ability.

    The problem simply stated is that analysts need a better system by which they can share, store, tag, search and report out internet research.


    Rather than using static word documents or email as the primary system of organizing web links, analysts can contribute content to a centralized repository of information, accessible through a web application.

    Links are uploaded to the database using a “Post It” button on the browser. Once they are uploaded, the content is archived. The analyst can tag content, provide additional analysis, mark it under specific or topic area. Other analysts can search by tag, report, topic, individual user, office or even within the archived text via keyword. Also, reporting out the searched material is automated so that an analyst can quickly generate a Word document, properly formatted with relevant material. This report will include the additional follow on analysis as well as the link itself.


    The Strategic Studies Group (SSG) in VA's Office of Policy and Planning, regularly sends out a report to a large distribution, called the “Weekly Reads Report.” The report is meant to provide situational awareness on the areas SSG is researching and discussing. In the past, to generate this report, analysts would have to copy and paste links into an excel document. Each report was a snap shot in time, but not easily searchable based on a specific field of interest.

    Using FeedStrap, analysts store, tag and write summaries for the linked articles throughout the week. All the information is saved systematically. When an analyst wants to generate the Weekly Reads Report, he or she can export the content into the Weekly Reads format. The articles are already tagged and the relevant fields of information are completed.

    Additionally, when SSG is preparing to write an analysis paper on a specific topic, analysts can easily generated an entire history of all the internet research that was done on that topic instantaneously. In the past, this process was time-consuming and potentially provided an incomplete picture.

    The Weekly Reads Report is shared with strategic planners across the Federal Government and has received positive feedback on its usefulness in trend analysis, identification of emerging concept and corporate environmental scanning. The quality of the Weekly Reads is directly related to the utility of FeedStrap.

    Technical Approach

    FeedStrap is powered by many open-source technologies.

    The RSS 1.0, RSS 2.0 and Atom XML protocols are the primary means by which shared articles are made machine readable so that they can be inputted into a database. The “Post It” button is a tool that masks that process of taking a web link and converting it to an RSS feed.

    Once the link is machine readable, FeedStrap relies on a range of Python programs on the server side to perform tasks such as feed parsing (via feedparser), article extraction (via Python-Goose), PDF text extraction (via PDFMiner), Feed Generation (PyRSS2Gen) and full text indexing (via pysolr + Apache Solr).

    All the data is stored in a Postgresql 9.2 Database. The Django Web Framework provides for an easy to use Python API for accomplishing SQL commands and data queries.

    Additionally, Django provides the framework for creating front end “views” for the website. Django is a Model-View-Controller framework.

    The front end design and functionality is aided greatly by the Twitter Bootstrap design framework and the JQuery Javascript library.