[Bigbang-dev] Clarifying theoretical commitments going into IETF 116

[Sebastian Benthall]

Priyanka, Effy,

So, identifying whether the email address used even when slightly different
> refers to the exact same person, is something my algorithm can do which I
> have presented at the AID workshop.


Brilliant.

Within the email body, doing the entity recognition as well as perhaps
> coreference resolution (i.e., the name of the person or company is
> not present but is referred to with pronouns such as he/she/they) has
> varying accuracy. I was happy to know of Effy's work in this direction.
> Myself, I would try to use Effy's published work as well as try Lauren
> Berk's (now Lauren Wheelock) work https://github.com/lauren897
> https://dspace.mit.edu/handle/1721.1/127291?show=full which when I had
> attended worked well for cases with short context.
>

Of course, it would be ideal to work with Effy on this!


> This is an interesting question for me, since I haven't thought of the
> graph from the perspective of say measures like betweenness centrality,
> etc. I thought of it as a representation based on which we mine for
> insights, using new graph neural network algorithms.  For example, if we
> represent the discourses as a multi edged temporal graph, where the
> different types of edges represent different aspects of the communication
> that we take into account, then if we work on extracting say graphlets
> (which in my mind are homeomorphic subgraph patterns (say could have maybe
> 15 nodes which could be one set of folks that hold a particular view).
>

Wow, this is very cool! I think I am following.

Taking email communications as an example... I suppose this would mean
labeling the messages somehow?
For example, the label could include references to other entities?

One challenge that has always been a problem for me in representing these
discussions as a network is that while emails may have an "In-Reply-To"
header, which is useful for modeling turn-taking and social responsiveness,
in a 'mailing list' there is also the audience of lurkers, those on the
thread who may be indirectly part of the audience, etc. Not to mention
out-of-band communication. I suppose that at a large scale, one can chalk
this all up to measurement error.

But I bring it up because I'm wondering what concretely we might do with
respect to preparing the dataset.

(Ideally, our data preprocessing steps might support a number of different
downstream 'user stories', which then feed into the dashboard for the
'users'... but our own use case of this research project can also be a good
source of requirements.)

I'm also wondering how the significant graphlets are identified. Does that
involve labeling (i.e. supervised) of the graphlets?
Or do these new algorithms extract network motifs based on frequencies
alone?


> Then these graphlets we could label as different viewpoints in how they
> view privacy?? I apologize if it doesn't make sense, I haven't yet figured
> this out .
>

I appreciate you going out on a limb. I think this is very exciting!

It may be useful to distinguish analytically between:
 - behavioral regularities -- which we could identify from the graph data
 - *reasons for* those behavioral regularities, which could be:
   - endogenous, because of internal dynamics within the system of
communication (shades of Luhmann here...)
   - exogenous (due to external forces such as the corporate structure of
Cisco or the geographic distance between people)

I suppose I would argue that for something to be a "norm", there is
necessarily some endogenous dynamic that maintains it.
(I don't think that's a sufficient condition, but I do think it might be a
good necessary condition.)

For something to be a 'norm', the endogenous dynamic maybe needs to involve
the shared 'view' that the regularity is how things ought to be.
I think we could set aside the question of whether these are 'privacy
norms' until we have a firmer sense of how we are operationalizing things.

These are very deep questions but I am into them. I started BigBang to
study questions like this!
But one of the first things I learned with BigBang is that not all
behavioral regularities are due to endogenous factors, and that indeed
exogenous explanations are often precisely what is needed as a kind of
'null hypothesis'.

I mean we could take the direction where we are not doing this .. and we
> model the problem as a agent simulation where the goals are related to the
> CI .. and inside we represent the agents and their interaction in the graph
> structure and we create a learning model whose weights we are trying to
> learn by trying to reach the goals based on the existing dialogue traces
> (aka mailing list conversations) we have.
>

I love where you are going with this! You see this as distinct from what
you proposed previously?

This seems to be a good way of figuring out how, say, an endogenous dynamic
could be responsible for the behavioral regularities.

If it's based on multiple agents interacting with a learning dynamic, that
could be "normative" in a very rich sense, no?

Truly, you're setting up an awesome vision here, Priyanka.
It's of course much larger scope than a project for a single hackathon.
It reads to me more like something that would become a funding proposal.
I do very much like funding proposals though!

- S

>
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