FeedMe: Understanding and Supporting Social Link Sharing on the Web

Which approach do you take to managing information overload on the web?  Do you unleash the firehose on yourself, subscribing to RSS feeds or relying on content aggregators to keep up with the news?  Or do you take small sips from the stream of content, regularly checking a small set of websites to look for updates?  It’s a common problem: firehosers dedicate much of their time to finding the golden nugget in the stream, whereas the sippers have given up on hearing everything—they will settle for a subset of the news. In both cases, highly personalized information often misses the recipient, or arrives late.

We’ve been looking at ways to empower another source highly personalized content: our friends, family, and coworkers. They already share web pages with us by e-mail, in person, and on social networks.  Social link sharing is often high-quality and personalized: quality is vetted by people you trust, and personalization is implicit when your social network uses its notion of your interests and tastes to forward you links.  Social link sharing is not perfect either—we all have that friend that’s filling up our mailbox with e-mails that contain the subject line “Fwd: Fwd: Fwd: puppies,” and our considerate friends sometimes avoid sending us content to avoid being perceived as that person.

Michael and I have been working on a multi-stage project to understand the social processes behind web content sharing and to support those processes by introducing a novel tool called FeedMe to facilitate such sharing.  We’ve published our findings in this technical report, and have summarized the results below.  Today we’ll be sharing part 1, where we will discuss our initial exploration to understand social link sharing; in the next post, you’ll be hearing about the tool we built based on these findings, and you’ll get a chance to sign up for a public release of FeedMe!

Link-Sharing Surveys
We conducted two surveys encompassing 140 users of Amazon Mechanical Turk, one focusing on what it’s like to receive posts, and the other focusing on what people think about when sharing.

In our receiver surveys, we learned several things:

  • E-mail is the dominant link-sharing medium.  Receivers cited a lack of time as a reason for why they do not visit content aggregators to find the top web content.  Sharers share content through email over all other mechanisms, because it is ubiquitous on the internet, and is a consistent protocol for sending content with anyone.  Another interesting tidbit: in addition to being the dominant link-sharing mechanism, e-mail tied regularly visiting one’s favorite websites as the the dominant information-finding mechanism.  Few users utilized feed readers, social aggregators, or social networks for links.  It turns out that e-mail is not dying in favor of Facebook and Twitter, especially not for the average user.Table 1Table 2
  • Topic Interest Drives Enjoyment.  The biggest reasons receivers cited for liking shared content was the relevance and entertainment value of the content.  Off-topic shares were off-putting for them.  Sharers were conscious of this; relevance and timeliness were their biggest concerns.Table 3Table 4
  • Link Sharing is Burdensome when it is a Repetitive Firehose.  Receivers disliked it most when sharers could not rate-limit themselves.  One user complained about a sharer who blindly forwards 10-20 e-mails per day.
  • Small Audiences are Best.  A small recipient list is a good predictor of whether recipients will appreciate the content.
  • Friends are the Most Common Target.  Sharers share more content with friends than family or co-workers, and their set of receiving friends are a small group that they regularly communicate with.
  • Receivers Want Even More.  If guaranteed high-quality content, receivers claimed they would like to have more links shared with them.

From active sharers, we learned:

  • Sharing Correlates with Seeking.  Individuals that identify with spending a large amount of time seeking out web content are also those that identify with sharing a large amount of content, and having their contacts in mind as they read web content.Figure 2
  • Sharing does not Imply Sociality.  You might think that sharing activity is guided by how much of a social butterfly you are. Not so. We measured two types of social capital, and neither was able to explain sharing practice.

Next Up…
With this information in mind, we sought out to build a tool to help heavy information seekers share more content.  Next week we’ll be sharing FeedMe, the tool we built to address this issue.  Until then, feel free to look through our technical report for the detailed results.

Table 2

12 Responses to “FeedMe: Understanding and Supporting Social Link Sharing on the Web”

  • [...] Understanding and Supporting Social Link Sharing on the Web – Haystack Blog [...]

  • In Table 2, you split the RSS responses into two categories: Google Reader and Other RSS. If they had been combined, feed reading would have ranked as the third most popular option, after directly visiting sites and getting email from people you know. Was there a reason for splitting the two?

  • Adam Marcus says:

    @Dean—great point. While somewhat of a distant third place, feed readers would have come up past the other contenders. Keep in mind that it’s unclear what summing the “social networks” and “Twitter” responses would have resulted in as well since they might overlap, but that’s another contender for third place.

    There were two reasons for splitting Google Reader from the rest of the feed readers. First, we wanted to make sure that the average person did not have to know that Google Reader is an RSS/Feed reader. Google has done a good job of branding Google Reader as a tool that helps you keep up with blogs without being too heavy on the technical jargon, and we wanted to make sure that mechanical turk users would recognize the tool even if only by brand. The second reason was for our own development purposes: we knew that if we developed a tool for a feed reader (we weren’t yet sure of the details of the tool yet), we would want to develop it for the most popular reader. We had a hunch that it was Google Reader, and so we put it in its own category. Initially, the list started out with a bunch of feed readers named explicitly, but we felt that the list was too long to get good responses, so we left it at “Google Reader” and “The others” mostly to verify our hunch.

    I hope this helps. Thanks for paying attention to the details!

  • [...] few weeks ago Adam and I blogged about some of our recent work investigating how link-sharing happens on the web. In contrast to [...]

  • Yuancheng says:

    “We measured two types of social capital, and neither was able to explain sharing practice.” — any detail? as far as I know(I read lots of publications concerning this topic), one big motivator for people’s knowledge sharing behavior is to socialize.

  • @Yuancheng: Sure, we used measures of social capital from Ellison et al’s study of Facebook use. One was bridging social capital, which investigates participants’ interest in meeting new people and being community-minded. The second was bonding social capital, which seeks to find out whether participants have strong ties, or individual friends who they interact with and trust a lot. There’s a lot of detail in the PDF that Adam linked to in the article if you want to read the specific questions that we asked.

  • It seems like the data you have found shows a great cross-section of Mechanical Turk users, but does that actually reflect internet users as a whole?

  • Adam Marcus says:

    @Chilledoutbeardedman: We cite a few sources that study that question: how good or bad an approximation of the real internet population distribution is MTurk? I’ve included those and a few more blog entries on this topic below. Most find that the population demographics are close to those of the US (not the internet, per se). In our case, this is fine—our goal was not to perfectly explore the sharing world, but just to explore the design space we were jumping in to so that we could garner ideas. Our final submission to CHI contains language that we think accurately frames the Mechanical Turk surveys, if you want to take a look at that. Thanks for your great question!

    1. Kittur, A., Chi, E., Suh, B., et al. Crowdsourcing user studies with Mechanical Turk. Proc. CHI ‘08, ACM Press, 453-456, 2008.
    2. Mason, W. and Watts, D. Financial Incentives and the “Performance of Crowds”. Invited Talk An Overview of Human Computation, 77, 2009.
    3. Mason, W. and Watts, D. Financial Incentives and the “Performance of Crowds”. Proc. HCOMP ‘09, 77, 2009.
    4. http://behind-the-enemy-lines.blogspot.com/2008/09/why-people-participate-on-mechanical.html
    5. http://behind-the-enemy-lines.blogspot.com/2008/03/why-people-participate-on-mechanical.html
    6. http://behind-the-enemy-lines.blogspot.com/2008/03/mechanical-turk-demographics.html
    7. http://blog.doloreslabs.com/2008/11/emnlp-slides/
    8. http://asc-parc.blogspot.com/2008/07/mechanical-turk-demographics.html
    9. http://behind-the-enemy-lines.blogspot.com/2008/03/mechanical-turk-demographics.html

  • @Chilledoutbeardedman: I agree with Adam. The answer is, “more than you might think.” The existing research shows that Mechanical Turkers in the US generally reflect a good cross-section of the US income, education, and gender distributions. But, “internet users as a whole” is a kind of dangerous statement. People rarely start by designing technology for internet users as a whole — e.g., everyone on the internet. More likely we are getting at United States internet users, or Internet users worldwide, or adult internet users, or Internet users with bank accounts (as Mechanical Turkers are).

  • The references are very interesting. I agree that “internet users as a whole” is a gross generalization. The opposite applies, as well, such that without Adam’s references justifying Mechanical Turk’s value, your case would lack grounding. Thanks for sharing this extremely informative subject matter. Good luck with your project.

  • Just to give another reference that compares the demographics of US Mechanical Turk users, with the demographics of US Internet users:


  • [...] will affect news coverage, and about the perceived incentives to produce high-trafficking junk news.Haystack Blog » FeedMe: Understanding and Supporting Social Link Sharing on the WebWhich approach do you take to managing information overload on the web? Do you unleash the firehose [...]