Congratulations, Aneesha on building a great tool!
I came across an article recently which might interest you titled
"Measuring Behavioral Trust in Social Networks" (http://www.cs.rpi.edu/
research/pdf/10-03.pdf). The authors state:
"We will focus on two particular behavior patterns as an expression of
trust: conversation and propagation. Specifically, if two nodes
converse, then they are more likely to trust each
other; and a prolong conversation reinforces this conclusion. If one
node propagates information from another then it suggests that the
propagator trusts the information. Similarly, a repeated propagation
makes the conclusion stronger."
"The measure of conversational trust will be based on the
conversations in C, obeying the following postulates:
• Longer conversations imply more trust.
• More conversations imply more trust.
• Balanced participation by A and B implies more trust.
Note that one could add other requirements, for example, if people who
did trust each other stop keeping in touch, their trust will likely
deteriorate over time - i.e. more spaced apart conversations implies
less trust. However, the above three properties are a good starting
This is just the only type of value judgement that SNA can make -
based on quantitative data that can be captured about activities in
the network. It's akin to treating each conversation as an atomic
transaction, indifferent to it's actual properties.
Or look at HP LABs' recent research around Influence and Passivity in
online Social Media (http://www.scribd.com/doc/35401457/Influence-and-
Passivity-in-Social-Media-HP-Labs-Research) in which they looked at
measures like retweeting rates to study popularity vs. influence.
Of course, Valdis Krebs has a wealth of information and analysis on
SNA at http://www.orgnet.com/sna.html
To understand if these measures could be an indication of quality
would be to ask - have we captured enough sources of data to determine
trust or influence or are we happy doing trending and graph analysis?,
do our data sources accurately define the subject of analytics (is
influence = number of times my followers retweet me? or trust = longer
conversations?), does our model, whatever that is, give us sufficient
basis to make predictions (my followers will on average retweet any of
my posts irrespective...), and does our analysis capture the longer
term changes in user behavior rather than analyzing static snapshots?
As George says, these could be a good starting point. I agree with
that. But at the same time I want to focus on building tools that act
as sources of data for qualitative analysis. Apologise I don't know of
any research that took a hypothesis from SNA and proved it to be wrong
on the qualitative dimension, but I am not sure if I want to search
for one in the light of the questions above,
On Sep 5, 6:14 am, Aneesha Bakharia <aneesha.bakha...@gmail.com
> > George- Hide quoted text -
> - Show quoted text -