34 research outputs found
Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions
Online extremism has severe societal consequences, including normalizing hate
speech, user radicalization, and increased social divisions. Various mitigation
strategies have been explored to address these consequences. One such strategy
uses positive interventions: controlled signals that add attention to the
opinion ecosystem to boost certain opinions. To evaluate the effectiveness of
positive interventions, we introduce the Opinion Market Model (OMM), a two-tier
online opinion ecosystem model that considers both inter-opinion interactions
and the role of positive interventions. The size of the opinion attention
market is modeled in the first tier using the multivariate discrete-time Hawkes
process; in the second tier, opinions cooperate and compete for market share,
given limited attention using the market share attraction model. We demonstrate
the convergence of our proposed estimation scheme on a synthetic dataset. Next,
we test OMM on two learning tasks, applying to two real-world datasets to
predict attention market shares and uncover latent relationships between online
items. The first dataset comprises Facebook and Twitter discussions containing
moderate and far-right opinions about bushfires and climate change. The second
dataset captures popular VEVO artists' YouTube and Twitter attention volumes.
OMM outperforms the state-of-the-art predictive models on both datasets and
captures latent cooperation-competition relations. We uncover (1) self- and
cross-reinforcement between far-right and moderate opinions on the bushfires
and (2) pairwise artist relations that correlate with real-world interactions
such as collaborations and long-lasting feuds. Lastly, we use OMM as a testbed
for positive interventions and show how media coverage modulates the spread of
far-right opinions.Comment: accepted in the 18th AAAI International Conference on Web and Social
Media (ICWSM'24
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
Interval-censored Hawkes processes
Interval-censored data solely records the aggregated counts of events during
specific time intervals - such as the number of patients admitted to the
hospital or the volume of vehicles passing traffic loop detectors - and not the
exact occurrence time of the events. It is currently not understood how to fit
the Hawkes point processes to this kind of data. Its typical loss function (the
point process log-likelihood) cannot be computed without exact event times.
Furthermore, it does not have the independent increments property to use the
Poisson likelihood. This work builds a novel point process, a set of tools, and
approximations for fitting Hawkes processes within interval-censored data
scenarios. First, we define the Mean Behavior Poisson process (MBPP), a novel
Poisson process with a direct parameter correspondence to the popular
self-exciting Hawkes process. We fit MBPP in the interval-censored setting
using an interval-censored Poisson log-likelihood (IC-LL). We use the parameter
equivalence to uncover the parameters of the associated Hawkes process. Second,
we introduce two novel exogenous functions to distinguish the exogenous from
the endogenous events. We propose the multi-impulse exogenous function - for
when the exogenous events are observed as event time - and the latent
homogeneous Poisson process exogenous function - for when the exogenous events
are presented as interval-censored volumes. Third, we provide several
approximation methods to estimate the intensity and compensator function of
MBPP when no analytical solution exists. Fourth and finally, we connect the
interval-censored loss of MBPP to a broader class of Bregman divergence-based
functions. Using the connection, we show that the popularity estimation
algorithm Hawkes Intensity Process (HIP) is a particular case of the MBPP. We
verify our models through empirical testing on synthetic data and real-world
data
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Curso de especialidad en la carrera de Administración y Negocios Internacionales, de carácter teórico-práctico,
dirigido a los estudiantes del 1er ciclo, que aborda los principios básicos de la teoría administrativa global y su
evolución, naturaleza y funcionamiento, desde una perspectiva del libre comercio en el contexto de la
globalización del Siglo XXI. Los contenidos teóricos, además, están asociados al estudio de factores internos y
externos para la definición de la estrategia de negocios, midiendo y analizando variables vinculadas a la cultura,
las relaciones laborales, los entornos empresariales, el mercado y las actividades de los competidores nacionales
e internacionales.
1La globalización de los negocios significa que los decisores y el personal gerencial administrativo de una
organización trabajen e interactúen con miembros de otras culturas, valorando los distintos entornos y siendo
capaces de gestionarlo de manera eficiente para lograr los resultados de una organización empresarial
The effect of 3β, 6β, 16β-trihydroxylup-20(29)-ene lupane compound isolated from Combretum leprosum Mart. on peripheral blood mononuclear cells
Chemistry and Biological Activities of Terpenoids from Copaiba (Copaifera spp.) Oleoresins
Evolving Trends in the Management of Acute Appendicitis During COVID-19 Waves: The ACIE Appy II Study
Evolving Trends in the Management of Acute Appendicitis During COVID-19 Waves: The ACIE Appy II Study
Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide
Correction: Evolving Trends in the Management of Acute Appendicitis During COVID-19 Waves: The ACIE Appy II Study (World Journal of Surgery, (2022), 46, 9, (2021-2035), 10.1007/s00268-022-06649-z)
In the original online version of this article Oreste Claudio Buonomo’s family name was misspelled. The original article was corrected