Whose ideas are worth spreading? The representation of women and ethnic groups in TED talks

Schwemmer, Carsten; Jungkunz, Sebastian

doi:10.1080/2474736x.2019.1646102

Cited by 10 publications

(8 citation statements)

References 74 publications

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“…For example, Schwemmer and Jungkunz (2019) investigate descriptive representation in digital platforms by annotating the gender and ethnorace of TED Talk speakers via Kairos diversity recognition software, and Lyu et al (2021) use DeepFace API to detect ethnorace of Twitter users in examining their opinion towards racially motivated hate crimes in the U.S. However, one shortcoming of the image-based method when classifying ethnoracial identity is that it is unable to distinguish between Hispanics/Latinos and non-Hispanic whites, presumably because it relies on skin-tone as a feature to predict ethnoracial identity, and there is a considerable amount of skin color variation among Latinos.…”

Section: Alternative Methodsmentioning

confidence: 99%

Measuring Descriptive Representation at Scale: Methods for Predicting the Race and Ethnicity of Public Officials

Lee¹,

Velez²

2023

Preprint

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Political scientists have increasingly relied on Bayesian Improved Surname Geocoding (BISG) to predict individual-level race and ethnicity using surnames and geographic information. However, these predictions are prone to systematic measurement error when geographic variables are included as independent variables, leading to bias and an increased likelihood of false positives. We compare five estimation methods, including approaches that use names, geography, and images. We find that a hybrid approach that combines surname-based Bayesian estimation with the use of publicly available images in a convolutional neural network not only reduces bias in downstream analyses, but also improves predictive accuracy in a sample of over 16,000 local elected officials. We conclude with a discussion of ethical considerations and describe domains where the hybrid approach might be especially suitable.

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Section: Alternative Methodsmentioning

confidence: 99%

Measuring Descriptive Representation at Scale: Methods for Predicting the Race and Ethnicity of Public Officials

Lee¹,

Velez²

2023

Preprint

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“…If we focus on dissemination over the Internet, although there has been an increase in the number of female TED Talk speakers (Schwemmer and Jungkunz 2019), we likewise find that women are less represented as producers of explanatory content via channels such as YouTube (Welbourne and Grant 2016). This not only entails reduced participation and visibility in scientific debate (Cambronero-Saiz et al 2021), but also a reduced opportunity to underpin their personal brand, which is very closely tied to the expectations, images, and perceptions created about an individual (Kalia et al 2017).…”

Section: Introductionmentioning

confidence: 95%

Fewer and Later: Women as Experts in TED Talks about COVID-19

Eizmendi

Peña-Fernández

2021

Journalism and Media

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The dissemination of science has undergone a major upsurge in recent years thanks to the Internet, which has served to overcome many of the technical and economic barriers. Among the many initiatives, one of the most successful tools has been the TED Talks website. At the same time, various studies indicate that women produce less science dissemination content and that they are also more likely to receive hostile comments. The objective of this research is to analyze from a gender perspective the content published on the TED Talk website on COVID-19 from 1 March 2020 to 31 March 2021 (n = 62) to determine its characteristics and the topics addressed, as well as the resulting engagement and the predominant feelings of the comments received. To do this, a content analysis of the publications has been carried out, as well as a qualitative analysis of the comments. The results indicate that the percentage of women experts on this platform is slightly lower than that of men (42.19%), and they have published content in later phases of the pandemic. From a qualitative point of view there are also differences, since women mainly opt for a social, political and social approach to the issue. In addition, the engagement of their publications is lower than that of men, although a higher prevalence of negative feelings is perceived in the comments they receive.

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“…Jürgens et al used facial recognition models to estimate age and gender compositions on German TV over time (Jürgens et al, 2022). Similarly, others combined facial recognition with automated text analysis to study the representation of women and ethnic groups in the digital sphere (Schwemmer & Jungkunz, 2019).…”

Section: Automated Image Analysis: An Overviewmentioning

confidence: 99%

Automated image analysis for studying online behaviour

Schwemmer¹,

Unger²,

Heiberger³

2023

Research Handbook on Digital Sociology

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Digitization led to an enormous increase in the availability of visual data. As images are an important aspect of human communication, decades of social science research have analysed images, yet in mostly manual fashion with limited scaling capacities. In this work, we outline how recent advances in computer vision enable automated image analysis, allowing researchers to further unlock the potential of digital behavioural data. We introduce the field of computational social science and conduct a literature review of early studies using image recognition. We also highlight important aspects to be considered, such as computational demands and biases of computer vision models. Furthermore, in a case study, we examine the online behaviour of U.S. Members of Congress during the early COVID-19 pandemic in 2020. In particular, we focus on sharing images showing face masks as they are a crucial aspect of health and safety measures during the pandemic. Using Instagram data and models for detecting face masks, we find that temporal dynamics and party affiliation play a substantial role in the likelihood of sharing images of people wearing face masks: images with masks are more often posted after the introduction of mask mandates and Democratic party members are more likely to share images with masks. In addition, we find somewhat weaker to no differences regarding the age and gender of politicians.

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Whose ideas are worth spreading? The representation of women and ethnic groups in TED talks

Cited by 10 publications

References 74 publications

Measuring Descriptive Representation at Scale: Methods for Predicting the Race and Ethnicity of Public Officials

Measuring Descriptive Representation at Scale: Methods for Predicting the Race and Ethnicity of Public Officials

Fewer and Later: Women as Experts in TED Talks about COVID-19

Automated image analysis for studying online behaviour

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