44 research outputs found
Oral tradition in a technologically advanced world
In the field of folklore, the study of "oral tradition" cannot be an either/or proposition. Rather, the responsible study of oral tradition recognizes the interdependence of both of these concepts: while "oral" clearly modifies "tradition," there is an equally important coloring of "oral" by "tradition."//Note: Quotation marks removed to ensure alphabetical order. Difference as follows; "Oral Tradition" in a Technologically Advanced World
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Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News
Abstract
Rumors and conspiracy theories thrive in environments of low confi- dence and low trust. Consequently, it is not surprising that ones related to the Covid-19 pandemic are proliferating given the lack of scientific consensus on the virus’s spread and containment, or on the long term social and economic ramifications of the pandemic. Among the stories currently circulating are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frame- works supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real-time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread
An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web
Although a great deal of attention has been paid to how conspiracy theories
circulate on social media and their factual counterpart conspiracies, there has
been little computational work done on describing their narrative structures.
We present an automated pipeline for the discovery and description of the
generative narrative frameworks of conspiracy theories on social media, and
actual conspiracies reported in the news media. We base this work on two
separate repositories of posts and news articles describing the well-known
conspiracy theory Pizzagate from 2016, and the New Jersey conspiracy Bridgegate
from 2013. We formulate a graphical generative machine learning model where
nodes represent actors/actants, and multi-edges and self-loops among nodes
capture context-specific relationships. Posts and news items are viewed as
samples of subgraphs of the hidden narrative network. The problem of
reconstructing the underlying structure is posed as a latent model estimation
problem. We automatically extract and aggregate the actants and their
relationships from the posts and articles. We capture context specific actants
and interactant relationships by developing a system of supernodes and
subnodes. We use these to construct a network, which constitutes the underlying
narrative framework. We show how the Pizzagate framework relies on the
conspiracy theorists' interpretation of "hidden knowledge" to link otherwise
unlinked domains of human interaction, and hypothesize that this multi-domain
focus is an important feature of conspiracy theories. While Pizzagate relies on
the alignment of multiple domains, Bridgegate remains firmly rooted in the
single domain of New Jersey politics. We hypothesize that the narrative
framework of a conspiracy theory might stabilize quickly in contrast to the
narrative framework of an actual one, which may develop more slowly as
revelations come to light.Comment: conspiracy theory, narrative structur
An Automated Pipeline for Character and Relationship Extraction from Readers' Literary Book Reviews on Goodreads.com
Reader reviews of literary fiction on social media, especially those in
persistent, dedicated forums, create and are in turn driven by underlying
narrative frameworks. In their comments about a novel, readers generally
include only a subset of characters and their relationships, thus offering a
limited perspective on that work. Yet in aggregate, these reviews capture an
underlying narrative framework comprised of different actants (people, places,
things), their roles, and interactions that we label the "consensus narrative
framework". We represent this framework in the form of an actant-relationship
story graph. Extracting this graph is a challenging computational problem,
which we pose as a latent graphical model estimation problem. Posts and reviews
are viewed as samples of sub graphs/networks of the hidden narrative framework.
Inspired by the qualitative narrative theory of Greimas, we formulate a
graphical generative Machine Learning (ML) model where nodes represent actants,
and multi-edges and self-loops among nodes capture context-specific
relationships. We develop a pipeline of interlocking automated methods to
extract key actants and their relationships, and apply it to thousands of
reviews and comments posted on Goodreads.com. We manually derive the ground
truth narrative framework from SparkNotes, and then use word embedding tools to
compare relationships in ground truth networks with our extracted networks. We
find that our automated methodology generates highly accurate consensus
narrative frameworks: for our four target novels, with approximately 2900
reviews per novel, we report average coverage/recall of important relationships
of > 80% and an average edge detection rate of >89\%. These extracted narrative
frameworks can generate insight into how people (or classes of people) read and
how they recount what they have read to others