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