University of Kwazulu-Natal

Fake News Political Science Spring 2025 Prof Blanchard Research

Bonisiwe Shabane
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fake news political science spring 2025 prof blanchard research

Where do you get your news? Traditional news sources like newspapers and news organizations? Social media like Facebook? A recent survey from the Pew Research Center (Links to an external site.) showed that about 48% of American adults get news from social media. With so many outlets for news these days we must be very critical about where our news comes from. A 2016 study at Stanford University (Links to an external site.) showed that students that are digital savvy were showing "a dismaying inability to reason about information that they had found on the Internet"...

It is important to be aware of what the news source's intentions are. Why are they publishing this news? Are they a legitimate news source? Is this a political organization? A special interest group? Is this trying to influence me in some way?

Is it propaganda (Links to an external site.)? Are they trying to get an emotional response? Is this clickbait (Links to an external site.)? Sometimes it is hard to tell because "fake news is a made-up story with an intention to deceive, often geared toward getting clicks." (Links to an external site.) We have all seen those headlines... “It’s natural to think of scientists as truth seekers, people driven by an intense curiosity to understand the natural world. Yet this picture of scientists and scientific inquiry sits uncomfortably with the reality and prevalence of scientific fraud.

If one wants to get at the truth about nature, why lie?” Bright’s question points to the internal temptations within science. Still, it applies equally when political administrations, corporate boards, and advocacy groups become influential players in the credit race, deciding which findings are amplified, suppressed, or rewarded, and sometimes bending science toward their preferred... Most of the time, the scientific enterprise works remarkably well, with prestige and its accoutrements motivating researchers to test, publish, and share their ideas. But when the golden ring of credit seems just out of reach, scientists may try a shortcut, scientific fraud. Scientific fraud results in:

Because of these harms, it is crucial not only to identify fraud but also to discover and mitigate its underlying causes. Before jumping in, a shoutout to Liam Kofi Bright of the London School of Economics for providing the broad outlines of my thinking. Consider your stereotypical scientist: a blend of brilliance, focus, and eccentricity, prone to absent-mindedness, yet relentlessly curious. Most importantly, driven by the “scientific method’s” search for the truth. Home > CONFERENCES > URS > URS 2025 > 120 Akshaj Singh, Boise State UniversityFollow Francesca Spezzano, Boise State UniversityFollow Andrew CoonFollow

The rapid spread of fake news and overt political bias in the media has prompted a need for robust detection systems. This study explores the impact of political bias on fake news detection by developing three identical machine learning models. A control model was trained on 40,000 pieces of non-biased news data, while two additional models were trained on 40,000 heavily biased news articles each—one with a left-wing (Democratic) bias and the other with... To assess cross-political performance, 20% of the left-wing data was used to test the right-wing model and vice versa, with each biased model also evaluated on a separate 20% non-biased dataset. Results indicate that the left-wing biased model achieved an accuracy of 73.0% on its primary test set but dropped to 58.0% when evaluated on right-wing data. Conversely, the right-wing biased model recorded 74.7% accuracy on its primary test set and 80.2% on left-wing data.

In contrast, the control model attained a consistently high accuracy of 89.0% across both politically opposed and unbiased test sets. Precision and recall metrics further support these findings: the control model achieved a precision of 90.1% and a recall of 88.5%, outperforming the left-wing model (precision 87.2%, recall 85.0%) and the right-wing model (precision... These outcomes suggest that while politically biased models perform well on data aligned with their training bias, their effectiveness diminishes with opposing data, underscoring the importance of unbiased training data for accurate fake news... However, the study was limited by constraints in computing power and the challenges associated with cleaning and sourcing high-quality biased news data, which may impact the overall scalability and generalizability of the approach. Singh, Akshaj; Spezzano, Francesca; and Coon, Andrew, "Tri Polar Analysis: Comparative Evaluation of Political Bias in Fake News Detection Using Three Identically Structured Machine Learning Models" (2025). 2025 Undergraduate Research Showcase.

120. https://scholarworks.boisestate.edu/under_showcase_2025/120 This document is currently not available here. Competing Interests: The authors have declared that no competing interests exist. Received 2021 Mar 24; Accepted 2021 Nov 2; Collection date 2021. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Although fake news creation and consumption are mutually related and can be changed to one another, our review indicates that a significant amount of research has primarily focused on news creation. To mitigate this research gap, we present a comprehensive survey of fake news research, conducted in the fields of computer and social sciences, through the lens of news creation and consumption with internal and... We collect 2,277 fake news-related literature searching six primary publishers (ACM, IEEE, arXiv, APA, ELSEVIER, and Wiley) from July to September 2020. These articles are screened according to specific inclusion criteria (see Fig 1). Eligible literature are categorized, and temporal trends of fake news research are examined. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved.

Use of this web site signifies your agreement to the terms and conditions. Be aware of the media sources you use when doing research. They all have some level of bias so it is always good to be aware of it. Below are a media bias charts (the first one created by Ad Fontes Media and the bottom one by "All Sides". They give a good representation (not necessarily absolute) of where various media sources lie in the grand scheme of things. Look at news from different sides and make up your own mind about an issue by putting aside pre-conceived notions and emotionalism and look at it from different angles.

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