News Not Noise: Socially Aware Information Filtering
itself, it is the shared interpretation of these stories that makes
individuals feel closer to one another, and that this process of
developing a common framework is critical for collaboration
around shared information. Although they discuss this in the
context of a team of work colleagues, it is easy to see parallels
with many social situations, particularly where some group
activity is being performed with some shared goal. Similarity,
therefore, has implications both for the estimation of social
proximity, and hence level of interest in news about that person,
and how the information should be presented. Stories from
those within a social group will make more sense when told in
the context of stories from other members of that group.
Perhaps by representing these ideas of social context and social
proximity within the user interface, we can help users to filter
social information better. Several designs were iterated through
and one developed into a digital prototype. This design
represents the user's friends as profile photographs in concentric
circles, with their closest friends (i.e. most likely to meet
offline) shown towards the centre and similar friends shown
adjacent to each other, such that the angle represents social
context. Users can hover over the profile photograph in order
to reveal what story types are available, and click in order to
reveal the stories.
Figure 4: Screenshot of prototype friend and news viewer
A screencast video was prepared showing the key elements of
the prototype interface in action and explaining the significance
of the layout (online at http://youtube.com/watch?v=4AbWpW-
wuuk). The same audience as before were surveyed again
regarding this new potential interface. Out of the 148 people
invited to participate in the survey, 11 responded (7.4%
response rate). Three respondents expressed reservations about
the amount of interaction required:
“If I had to deliberately choose specific people to click on to
see any news, I probably wouldn't bother”
“I find it quite boring to have to click on photos of everybody
you're interested in to see their news.”
“I think it could be quicker and easier to scan through a list of
news feeds on a page.”
The concept of organising friends by estimated social proximity
was widely welcomed:
“Many of the people on my list are friends from school who I
don't need to know about day to day”
“...it would make navigating through news stories a bit easier. ”
“the fact that the computer could choose to show you news feed
from your most clicked profiles is definitely a good idea”
One user disliked the idea of removing some of the serendipity
of the News Feed:
“narrowing down the news feed could take away some of the
surprising to find out something for someone that you don't
expect.”
There was significant disagreement about how the design would
function with different numbers of friends displayed, with some
respondents believing that it would be of most benefit to those
with large numbers of friends but others feeling that the display
would become ineffective when large numbers of friends
needed to be displayed. It is possible that this confusion was
caused by the use of too few friends in the prototype, which
meant that all of them could be displayed quite comfortably on
the display: conceptually, large numbers can be accommodated
in ever-expanding circles, closest friends nearer the centre.
4. CONCLUSIONS
The fundamental advantages of social networking services stem
from their geographically agnostic, asynchronous connection of
people, and the electronic representation of this social structure.
For example, the problem of displaying socially-relevant digital
media is effectively achieved by displaying to the user content
that is either published by or concerns their friends. This and
related research has shown that people are using social
networking services to support a wide variety of relationships.
However, the ability of the social networking service to display
interesting news is being undermined by the over-simplistic
underlying social model.
The answer does not seem to be to ask users to manually
classify their friends. Although respondents considered that the
proposed categories described the most interesting individuals
in their online social network, on average they only managed to
classify a minority of their friends, giving little clue as to their
relationship with the remaining others. Additionally, this task
was found to be demanding and time consuming.
Instead, we propose that user interfaces may be devised that
draw on machine learning techniques to assist the user in
filtering and prioritizing social media. These techniques allow
a far more complex and descriptive social model to be
constructed, potentially with little extra effort on the user's part.
Several interface designs were iterated through, one of which
was developed into a digital prototype which elicited positive
initial reactions from social network users. This prioritized
people based on the likelihood you would meet them offline as
well as those whose profiles were clicked the most.
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