Anonymous Persuader: the Diverse Faces of Personalized Technology

This post was written by a student in the Stanford’s Winter 2011 Networked Rhetoric class; it was designed to focus in on a particular source or research experience related to his/her project on social media and digital culture .  See a more detailed overview of this assignment.

Data lies at the heart of nearly every scientific endeavor. With solid numbers, scientists gain the ability to draw accurate connections, and oftentimes these conclusions are willfully accepted by a broad base of uncritical consumers.  In a similar data-mining spirit, personalization overlays the most used websites, ranging from Google to Pandora Radio. Based upon algorithms that constantly analyze web histories, including click-throughs, likes, and even physical location, numerous data are collected, sorted, and used to allow websites dynamic adaptations of many aspects of their content delivery systems.  This data allows for advertisement personalization based on one’s recently searched items, video recommendations in response to a user’s previous views, and aggregators that sort information to best match perceived needs.

Intrigued by this new era of personalization on the web, I am currently researching the mechanisms and rules that inform specific personalization platforms on the web, and the persuasive effects that these dynamic changes have on a user. Probably one of the most used website personalization aggregators comes in the form of the Facebook news feed. One of the hallmarks of the social network, the news feed takes front and center on each user’s homepage, delivering up to the minute information about those people Facebook thinks should be most visible to a user based on his/her behavior. During my research, a video post (link to video) by researcher Lily Cheng in Stanford’s Persuasive Technology Laboratory introduced a vastly useful resource into my repertoire for analyzing this personalized aggregator: the Fogg Behavior Model.

In the video, Cheng presents the Fogg Behavior Model and uses it to analyze the news feed. The Fogg Behavior Model is rather simple. As Cheng explains, the psychological model brilliantly theorizes that those users with a combination of high motivation and high ability create a situation that needs to be triggered. Upon the deployment of this trigger, all elements converge, influencing a user to take action. In the context of the news feed, Cheng argues that Facebook achieves its influence by “put[ting] hot triggers in the path of motivated people.” She claims that users, by the very nature of visiting the site, already exhibit motivation. Because all of the content in the news feed springs in some way from a connection that has already been willingly accepted (say from friends, or friends’ friends), the links, pictures, status updates that appear all take on the form of hot triggers. In this way, through personalized content, users are influenced to follow the updates that constantly inhabit their news feeds.

Besides this useful starting point for further research into the reasons why Facebook’s personalized news feed is able to persuasively direct users in certain directions, I believe that the Behavior Model will become a useful tool as I expand my research. Beyond Facebook, I plan to use the Model to break down personalized web platforms such as Google’s use of search engine optimization, or even recommendation systems. I will categorize motivation, ability, and triggers in all of these contexts. While it will not be the only model I use, at this point it remains the most accessible way in which I can analyze persuasive web personalization.

– Coulton Bunney, Stanford University, Class of 2013

This entry was posted in CCR exchange: Student Research, Networked Rhetoric: Section 2, Stanford Networked Rhetorics class. Bookmark the permalink.

3 Responses to Anonymous Persuader: the Diverse Faces of Personalized Technology

  1. Jamie says:

    You did a reasonable job of explaining the Fogg Behavior model in the little space that you had, and I definitely see how it could factor into your research. But I don’t really understand how you are going to use this model as anything but qualitative data; and if that’s the case, why use a model at all? Data like that is very hard to get, although you might be able to figure out some way to track yourself, which would be a very interesting field experiment.

    I look forward to seeing what you can conclude from your research, especially what you end up using the Fogg model for.

  2. hspinks says:

    This is very interesting to me because I have very mixed feelings about personalized ads. A lot of the time, I will be very annoyed at the invasion of privacy that comes with personalized technology, and sometimes I just don’t want to know what I’ve been searching. With all my frustration at these ads though, occasionally I will find something that really interests me, like a concert by my favorite artist – I would probably have never known about that Murs concert last year without that ad. Similarly, I know a lot of people who found out about a free Chiddy Bang concert in a couple days from an ad on the sidebar of Facebook. I look forward to hearing about what you find with this model – was I “motivated” and is it worth the invasion of privacy for the few useful triggers that lead to action?

  3. Carlos Fierro says:

    It is pretty interesting that you bring in Facebook as being personalized as we read an article earlier about MySpace being personalized and Facebook being more controlled. I recall when ads were always irrelevant just a few years ago on Facebook. Now they are sometimes helpful. It really makes me curious as to how much data Facebook and other sites are collecting to make those ads more relevant to the user. I really thought personalization would be taken out in future technology, but your blog is a perfect contradiction to that. Perhaps users can’t personalize the ads, but at least there is some variance in the interface. I look forward to seeing where your research goes and what other things are being heavily personalized.

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