[Air-L] Student Opportunity - Modeling and Mapping Election Interference

[Shulman, Stu]

I have a remote work project that requires two or more part time student
assistants. The key skill sets are using .graphml exports of Twitter data
to help build a prototype tool for tracking the evolution of networks over
time, and using a trust model to score accounts in the network based on
their historical footprint. The hourly wage is negotiable.

Job 1: If you are a network science student interested in the application
of Kineviz GraphXR or similar tools I would like to discuss how you might
help us transition from static pictures to dynamic historical mapping tools
with a lower barrier to entry for new users.

Job 2: If you have advanced python programming skills, the second
opportunity involves learning to operate Trust Defender:
https://github.com/texifter/trust-defender. This is a complex collection of
open source scripts combining an n-gram Bayes classifier model with a
neural network model which is used to classify Twitter users as potential
good or bad actors.

Ideally, the two positions will together help shape a new historical and
interactive visualization where we can filter for a variety of trust score
ranges in a dynamic network that allows real time drilling in and out of
hubs and nodes. Please send me a CV, two references, and a short cover
letter if you are interested.

Assuming Twitter survives (there is some debate about this) we are going to
need many new tools, teams, and methods to understand how U.S. election
interference will operate in the run-up to 2024.

-- 
Dr. Stuart W. Shulman
Founder and CEO, Texifter
Editor Emeritus, *Journal of Information Technology & Politics*