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DESCRIPTION:Robust fake-post detection against real-coloring adversaries:
Branching process and Stochastic approximation
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/500/robust-fake-post-detection-against-real-coloring-adversariesbranching-process-and-stochastic-approximation/
SUMMARY:The viral propagation of fake posts on online social networks (OSNs) has
become an alarming concern. We design control mechanisms for fake post
detection while negligibly affecting the propagation of real posts.
Towards this, a warning mechanism based on crowd-signals was recently
proposed, where all users actively declare the post as real or fake. In
the talk, we will discuss a more realistic framework where users exhibit
different adversarial or non-cooperative behaviour: (i) they can
independently decide whether to provide their response, (ii) they can
choose not to consider the warning signal while providing the response,
and (iii) they can be real-coloring adversaries who deliberately declare
any post as real. To analyze the post-propagation process in this
complex system, we propose and study a new branching process, namely
total-current population-dependent branching process with multiple death
types.  For the branching process, under finite second-moment
conditions, using stochastic approximation technique, we show that the
time-asymptotic proportion of the populations either converges to the
equilibrium points or infinitely often enters every neighbourhood and
exits some neighbourhood of a saddle point of an appropriate ordinary
differential equation with a certain probability.

For the application at hand, at first, we compare and show that the
existing warning mechanism significantly under-performs in the presence
of adversaries. Then, we design new mechanisms which remarkably perform
better than the existing mechanism by cleverly eliminating the influence
of the responses of the adversaries. Towards the end, we propose an
algorithm which works the best, without assuming any prior knowledge
about user specific parameters. The theoretical results are validated
using Monte-Carlo simulations.
DTSTART:20230810T120000Z
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