314 research outputs found
Causal Inference in Disease Spread across a Heterogeneous Social System
Diffusion processes are governed by external triggers and internal dynamics
in complex systems. Timely and cost-effective control of infectious disease
spread critically relies on uncovering the underlying diffusion mechanisms,
which is challenging due to invisible causality between events and their
time-evolving intensity. We infer causal relationships between infections and
quantify the reflexivity of a meta-population, the level of feedback on event
occurrences by its internal dynamics (likelihood of a regional outbreak
triggered by previous cases). These are enabled by our new proposed model, the
Latent Influence Point Process (LIPP) which models disease spread by
incorporating macro-level internal dynamics of meta-populations based on human
mobility. We analyse 15-year dengue cases in Queensland, Australia. From our
causal inference, outbreaks are more likely driven by statewide global
diffusion over time, leading to complex behavior of disease spread. In terms of
reflexivity, precursory growth and symmetric decline in populous regions is
attributed to slow but persistent feedback on preceding outbreaks via
inter-group dynamics, while abrupt growth but sharp decline in peripheral areas
is led by rapid but inconstant feedback via intra-group dynamics. Our proposed
model reveals probabilistic causal relationships between discrete events based
on intra- and inter-group dynamics and also covers direct and indirect
diffusion processes (contact-based and vector-borne disease transmissions).Comment: arXiv admin note: substantial text overlap with arXiv:1711.0635
Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems
Social media is being increasingly weaponized by state-backed actors to
elicit reactions, push narratives and sway public opinion. These are known as
Information Operations (IO). The covert nature of IO makes their detection
difficult. This is further amplified by missing data due to the user and
content removal and privacy requirements. This work advances the hypothesis
that the very reactions that Information Operations seek to elicit within the
target social systems can be used to detect them. We propose an
Interval-censored Transformer Hawkes (IC-TH) architecture and a novel data
encoding scheme to account for both observed and missing data. We derive a
novel log-likelihood function that we deploy together with a contrastive
learning procedure. We showcase the performance of IC-TH on three real-world
Twitter datasets and two learning tasks: future popularity prediction and item
category prediction. The latter is particularly significant. Using the
retweeting timing and patterns solely, we can predict the category of YouTube
videos, guess whether news publishers are reputable or controversial and, most
importantly, identify state-backed IO agent accounts. Additional qualitative
investigations uncover that the automatically discovered clusters of
Russian-backed agents appear to coordinate their behavior, activating
simultaneously to push specific narratives
Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes
An ability to predict the popularity dynamics of individual items within a
complex evolving system has important implications in an array of areas. Here
we propose a generative probabilistic framework using a reinforced Poisson
process to model explicitly the process through which individual items gain
their popularity. This model distinguishes itself from existing models via its
capability of modeling the arrival process of popularity and its remarkable
power at predicting the popularity of individual items. It possesses the
flexibility of applying Bayesian treatment to further improve the predictive
power using a conjugate prior. Extensive experiments on a longitudinal citation
dataset demonstrate that this model consistently outperforms existing
popularity prediction methods.Comment: 8 pages, 5 figure; 3 table
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