Two Roads to Middle-earth Converge & One Online Social Networks Analysis Tool to Study Them All: Introducing Netlytic

    This post is part of the series of posts designed to highlight some of the web tools that we are developing at the Social Media Lab and how they can be used for research.

    Jennifer Grek Martin, a recent grad of the School of Information Management, Dalhousie University

    Given the choice between watching a movie or reading the book on which it is based, which would you choose? Did the book inspire you to watch the film or was it the other way around? And if yes, did one experience influence the other and did it change your mental image or perception of places and characters in the story? These are just some of the questions that Jennifer Grek Martin, a recent grad of the School of Information Management at Dalhousie University had set out to answer in her Master’s thesis entitled “Two Roads to Middle-earth Converge: Observing Text-based and Film-based Mental Images from TheOneRing.net Fan Community”.

    To study how the mental images inspired by these two very different media can seamlessly merge, diverge or overlap, Jennifer collected and examined thousands of online comments posted by fans to the http://TheOneRing.net community using Netlytic (http://netlytic.org), a web-based system that our lab is developing. Netlytic is designed for automated discovery, analysis and visualization of social networks and other information about online communities. It does this by using various text analysis techniques to automatically reduce large volumes of conversational type texts into more useful visual representations such as communication networks that can be easily studied. Traditionally, the collection and analysis of this type of conversational data by social science researchers is usually done using labor intensive and time consuming surveys and interviews. With Netlytic, researchers can now automatically collect online conversational type data from any online community, create social networks visualization and explore their dataset with ease.

    Here is a brief description by Jennifer highlighting what she discovered in her thesis:

    Mental images are visualizations created in our mind’s eye that often occur when we read a text, imagine an object, person, or place, or when we try to remember something. In fact, they are a way for humans to process information; mental images, in particular spatially oriented images, have a great deal to say about how we understand the world around us.

    One way to look at mental images is to compare the types of images generated from the same story, but from two different media. I took this route and examined spatial mental images of The Lord of the Rings from both text and film, using public online posts.

    What I found was fascinating — those who read the book and watched the film often combined the mental images created from both media. Sometimes the process of combining images was fairly easy; sometimes it took quite a bit of work — meaning mental images are not only spontaneous, but can be shaped and modified consciously. A few described how the film’s imagery helped them better understand the text, while others preferred their own visions to Peter Jackson’s. As an added windfall, I found an intellectually rich, articulate, and devoted community of fans whose discussions focused on furthering understanding of Tolkien’s and Jackson’s Middle-earth through humour, debate, and careful reading of the text and viewing of the film.

    These findings suggest a few avenues for further discussion. For example, one implication is that while visual (filmic) images, once seen, are often difficult to “erase” from the mind’s eye, they can be modified, especially if the thing envisioned is large and complex (like a landscape or building.) My research also adds to the growing interest in the nature of online fan communities — not as “apersonal,” anti-social networks, but as thriving fora for shared experience, discussion, and debate. (The full text of Jennifer’s thesis is accessible at http://hdl.handle.net/10222/14242)

    This is just one example of how Netlytic can be used by social science researchers to explore and understand the vast and ever growing legacy of public texts found online. If you are interested in using Netlytic to study an existing or emerging online community or have any questions about the research at the Social Media Lab, please contact us.

    * Written by Anatoliy Gruzd, Jennifer Grek Martin and Philip Mai

     

    Reference:
    Grek Martin, Jennifer M. (2011). Two Roads to Middle-earth Converge: Observing Text-based and Film-based Mental Images from TheOneRing.net Online Fan Community Master’s thesis, Dalhousie University, Halifax, NS, Canada

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