598 research outputs found
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
It is widely believed that one's peers influence product adoption behaviors.
This relationship has been linked to the number of signals a decision-maker
receives in a social network. But it is unclear if these same principles hold
when the pattern by which it receives these signals vary and when peer
influence is directed towards choices which are not optimal. To investigate
that, we manipulate social signal exposure in an online controlled experiment
using a game with human participants. Each participant in the game makes a
decision among choices with differing utilities. We observe the following: (1)
even in the presence of monetary risks and previously acquired knowledge of the
choices, decision-makers tend to deviate from the obvious optimal decision when
their peers make similar decision which we call the influence decision, (2)
when the quantity of social signals vary over time, the forwarding probability
of the influence decision and therefore being responsive to social influence
does not necessarily correlate proportionally to the absolute quantity of
signals. To better understand how these rules of peer influence could be used
in modeling applications of real world diffusion and in networked environments,
we use our behavioral findings to simulate spreading dynamics in real world
case studies. We specifically try to see how cumulative influence plays out in
the presence of user uncertainty and measure its outcome on rumor diffusion,
which we model as an example of sub-optimal choice diffusion. Together, our
simulation results indicate that sequential peer effects from the influence
decision overcomes individual uncertainty to guide faster rumor diffusion over
time. However, when the rate of diffusion is slow in the beginning, user
uncertainty can have a substantial role compared to peer influence in deciding
the adoption trajectory of a piece of questionable information
Pulse of the Pandemic: Iterative Topic Filtering for Clinical Information Extraction from Social Media
The rapid evolution of the COVID-19 pandemic has underscored the need to
quickly disseminate the latest clinical knowledge during a public-health
emergency. One surprisingly effective platform for healthcare professionals
(HCPs) to share knowledge and experiences from the front lines has been social
media (for example, the "#medtwitter" community on Twitter). However,
identifying clinically-relevant content in social media without manual labeling
is a challenge because of the sheer volume of irrelevant data. We present an
unsupervised, iterative approach to mine clinically relevant information from
social media data, which begins by heuristically filtering for HCP-authored
texts and incorporates topic modeling and concept extraction with MetaMap. This
approach identifies granular topics and tweets with high clinical relevance
from a set of about 52 million COVID-19-related tweets from January to mid-June
2020. We also show that because the technique does not require manual labeling,
it can be used to identify emerging topics on a week-to-week basis. Our method
can aid in future public-health emergencies by facilitating knowledge transfer
among healthcare workers in a rapidly-changing information environment, and by
providing an efficient and unsupervised way of highlighting potential areas for
clinical research.Comment: 24 pages, 5 figures. To be published in the Journal of Biomedical
Informatic
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