How statistics can aid in the fight against misinformation
Machine learning model detects misinformation, is inexpensive and is transparent

Date:
December 2, 2021
Source:
American University
Summary:
Mathematicians created a statistical model that can be used to
detect misinformation in social posts.



FULL STORY ==========================================================================
An American University math professor and his team created a statistical
model that can be used to detect misinformation in social posts. The model
also avoids the problem of black boxes that occur in machine learning.


==========================================================================
With the use of algorithms and computer models, machine learning
is increasingly playing a role in helping to stop the spread of
misinformation, but a main challenge for scientists is the black box
of unknowability, where researchers don't understand how the machine
arrives at the same decision as human trainers.

Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models
can detect misinformation in social media during events like a pandemic
or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science
Prof. Nathalie Japkowicz, also show how the model's decisions align with
those made by humans.

"We would like to know what a machine is thinking when it makes decisions,
and how and why it agrees with the humans that trained it," Boukouvalas
said. "We don't want to block someone's social media account because
the model makes a biased decision." Boukouvalas' method is a type of
machine learning using statistics. It's not as popular a field of study
as deep learning, the complex, multi-layered type of machine learning
and artificial intelligence. Statistical models are effective and provide another, somewhat untapped, way to fight misinformation, Boukouvalas said.

For a testing set of 112 real and misinformation tweets, the model
achieved a high prediction performance and classified them correctly,
with an accuracy of nearly 90 percent. (Using such a compact dataset was
an efficient way for verifying how the method detected the misinformation tweets.) "What's significant about this finding is that our model
achieved accuracy while offering transparency about how it detected
the tweets that were misinformation," Boukouvalas added. "Deep learning
methods cannot achieve this kind of accuracy with transparency."


========================================================================== Before testing the model on the dataset, researchers first prepared
to train the model. Models are only as good as the information humans
provide. Human biases get introduced (one of the reasons behind bias in
facial recognition technology) and black boxes get created.

Researchers carefully labeled the tweets as either misinformation or
real, and they used a set of pre-defined rules about language used in misinformation to guide their choices. They also considered the nuances
in human language and linguistic features linked to misinformation,
such as a post that has a greater use of proper nouns, punctuation
and special characters. A socio-linguist, Prof. Christine Mallinson of
the University of Maryland Baltimore County, identified the tweets for
writing styles associated with misinformation, bias, and less reliable
sources in news media. Then it was time to train the model.

"Once we add those inputs into the model, it is trying to understand
the underlying factors that leads to the separation of good and bad information," Japkowicz said. "It's learning the context and how words interact." For example, two of the tweets in the dataset contain "bat
soup" and "covid" together. The tweets were labeled misinformation
by the researchers, and the model identified them as such. The model
identified the tweets as having hate speech, hyperbolic language,
and strongly emotional language, all of which are associated with misinformation. This suggests that the model distinguished in each of
these tweets the human decision behind the labeling, and that it abided
by the researchers' rules.

The next steps are to improve the user interface for the model, along
with improving the model so that it can detect misinformation social
posts that include images or other multimedia. The statistical model
will have to learn how a variety of elements in social posts interact
to create misinformation. In its current form, the model could best be
used by social scientists or others who are researching ways to detect misinformation.

In spite of the advances in machine learning to help fight misinformation, Boukouvalas and Japkowicz agreed that human intelligence and news literacy remain the first line of defense in stopping the spread of misinformation.

"Through our work, we design tools based on machine learning to alert
and educate the public in order to eliminate misinformation, but we
strongly believe that humans need to play an active role in not spreading misinformation in the first place," Boukouvalas said.

========================================================================== Story Source: Materials provided by American_University. Original written
by Rebecca Basu.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Caitlin Moroney, Evan Crothers, Sudip Mittal, Anupam Joshi, Tu"lay
Adalı, Christine Mallinson, Nathalie Japkowicz, Zois
Boukouvalas.

The Case for Latent Variable Vs Deep Learning Methods in
Misinformation Detection: An Application to COVID-19. Discovery
Science, 2021 DOI: 10.1007/978-3-030-88942-5_33 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/12/211202162151.htm

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