Related papers: Gender Bias in Fake News: An Analysis

Gender Bias in Fake News: An Analysis

URL: http://arxiv.org/abs/2209.11984v3
Date: Sat, 4 Feb 2023 09:34:24 GMT
Title: Gender Bias in Fake News: An Analysis
Authors: Navya Sahadevan, Deepak P
Abstract summary: We provide the first empirical analysis of gender bias vis-a-vis fake news. Our analysis establishes the increased prevalance of gender bias in fake news across three facets. Gender bias needs to be an important consideration in research into fake news.
Score: 3.4925763160992402
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Data science research into fake news has gathered much momentum in recent years, arguably facilitated by the emergence of large public benchmark datasets. While it has been well-established within media studies that gender bias is an issue that pervades news media, there has been very little exploration into the relationship between gender bias and fake news. In this work, we provide the first empirical analysis of gender bias vis-a-vis fake news, leveraging simple and transparent lexicon-based methods over public benchmark datasets. Our analysis establishes the increased prevalance of gender bias in fake news across three facets viz., abundance, affect and proximal words. The insights from our analysis provide a strong argument that gender bias needs to be an important consideration in research into fake news.

Related papers

A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT [8.363702038073814]
Existing fake news detection datasets are sourced from human journalists. In this paper, we revisit the existing fake news dataset verified by human journalists with augmented fact-checking by large language models.
arXiv Detail & Related papers (2023-12-19T05:46:11Z)
Adapting Fake News Detection to the Era of Large Language Models [48.5847914481222]
We study the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news. Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa.
arXiv Detail & Related papers (2023-11-02T08:39:45Z)
Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines [63.52264764099532]
We use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022. We quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs. Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias.
arXiv Detail & Related papers (2023-03-28T03:31:37Z)
Gender and Prestige Bias in Coronavirus News Reporting [6.646098685534984]
We identify when experts are quoted in news and extract their names and institutional affiliations. We find a substantial gender gap, where men are quoted three times more than women. We also identify academic prestige bias, where journalists turn to experts from highly-ranked academic institutions more than experts from less prestigious institutions.
arXiv Detail & Related papers (2023-01-27T21:18:09Z)
Predicting Sentence-Level Factuality of News and Bias of Media Outlets [10.925648034990306]
This paper introduces a large sentence-level dataset, titled "FactNews", composed of 6,191 sentences expertly annotated according to factuality and media bias definitions proposed by AllSides. We use FactNews to assess the overall reliability of news sources, by formulating two text classification problems for predicting sentence-level factuality of news reporting and bias of media outlets.
arXiv Detail & Related papers (2023-01-27T16:56:24Z)
Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
arXiv Detail & Related papers (2023-01-14T18:58:42Z)
Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks [49.29141811578359]
We propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.
arXiv Detail & Related papers (2022-12-24T00:19:32Z)
User Preference-aware Fake News Detection [61.86175081368782]
Existing fake news detection algorithms focus on mining news content for deceptive signals. We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling.
arXiv Detail & Related papers (2021-04-25T21:19:24Z)
A Survey on Predicting the Factuality and the Bias of News Media [29.032850263311342]
"The state of the art on media profiling for factuality and bias" "Political bias detection, which in the Western political landscape is about predicting left-center-right bias" "Recent advances in using different information sources and modalities"
arXiv Detail & Related papers (2021-03-16T11:11:54Z)
Multi-Dimensional Gender Bias Classification [67.65551687580552]
Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
arXiv Detail & Related papers (2020-05-01T21:23:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.