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Related Concept Videos

Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Stereotype Content Model02:16

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Skewness01:06

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The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
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Bias in Epidemiological Studies01:29

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Stereotypes, Prejudice, and Discrimination02:55

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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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The Representativeness Heuristic02:13

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Gender Bias in the News: A Scalable Topic Modelling and Visualization Framework.

Prashanth Rao1, Maite Taboada1

  • 1Discourse Processing Lab, Department of Linguistics, Simon Fraser University, Burnaby, BC, Canada.

Frontiers in Artificial Intelligence
|July 5, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing news topics and gender representation. It reveals how certain topics disproportionately feature men or women, reinforcing societal gender roles.

Keywords:
corpus linguisticsgender biasmachine learningnatural language processingnews mediatopic modelling

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Area of Science:

  • Computational Linguistics
  • Media Studies
  • Sociology

Background:

  • Existing topic modeling research often uses fixed text collections (closed corpora).
  • News corpora are dynamic and continuously growing (open corpora).
  • Understanding gender representation in media is crucial for societal equity.

Purpose of the Study:

  • To develop and present a topic modeling and data visualization methodology.
  • To examine gender-based disparities in news articles across different topics.
  • To analyze representation in an open, continuously growing news corpus.

Main Methods:

  • Utilized Latent Dirichlet Allocation (LDA) for topic discovery.
  • Applied the methodology to a 2-year corpus of mainstream Canadian news articles.
  • Generated monthly topics (keyword distributions) to track trends.

Main Results:

  • Identified distinct topics with prominent representation of either women or men.
  • Topics like lifestyle and healthcare featured more women; sports, politics, and business featured more men.
  • Observed a reinforcement of gendered societal roles: women in caregiving, men in leadership/business.

Conclusions:

  • The findings highlight a self-reinforcing gendered division in news representation.
  • Unequal representation can entrench societal perceptions of women as caregivers and men as leaders.
  • The methodology is robust, scalable, and applicable to future studies on media representation and language analysis.