Inferring Gender from Names on the Web: A Comparative Evaluation of Gender Detection Methods (1603.04322v1)

Abstract: Computational social scientists often harness the Web as a "societal observatory" where data about human social behavior is collected. This data enables novel investigations of psychological, anthropological and sociological research questions. However, in the absence of demographic information, such as gender, many relevant research questions cannot be addressed. To tackle this problem, researchers often rely on automated methods to infer gender from name information provided on the web. However, little is known about the accuracy of existing gender-detection methods and how biased they are against certain sub-populations. In this paper, we address this question by systematically comparing several gender detection methods on a random sample of scientists for whom we know their full name, their gender and the country of their workplace. We further suggest a novel method that employs web-based image retrieval and gender recognition in facial images in order to augment name-based approaches. Our findings show that the performance of name-based gender detection approaches can be biased towards countries of origin and such biases can be reduced by combining name-based an image-based gender detection methods.

Inferring Gender from Names on the Web: A Comparative Evaluation of Gender Detection Methods

This paper presents a systematic analysis of gender detection methods that utilize the web to infer demographic information essential for research in computational social sciences. The challenge is to accurately derive gender from individuals' names, a task particularly complex due to biases against various sub-populations based on geographical and cultural contexts.

Methodology and Approaches

The authors undertake a comprehensive evaluation using a diverse dataset consisting of scientists’ names, gender, and country of origin. They focus on several unsupervised methods that infer gender from names and images without the necessity for training:

1. US Social Security Administration (SSA) Data: Employs historical baby names from the United States for gender inference.
2. IPUMS Census Data: Utilizes American demographic samples, although limitations exist in international applicability.
3. Sexmachine Database: Contains 40,000 names with associated popularity and gender information across various countries.
4. Genderize API: Leverages social network data to provide probabilistic gender estimates.
5. Face++ Algorithm: Utilizes facial recognition technology for gender detection, requiring image inputs.

Additionally, the paper introduces mixed methods combining both name-based and image-based approaches, enhancing accuracy especially for non-Western names.

Results and Observations

The comparative evaluation reveals several critical insights:

• Performance Metrics: Name-based methods show varying precision and recall, impacted by factors like country-specific name characteristics. Genderize and Face++ offer relatively high accuracy compared to other standalone methods.
• Mixed Methods Superiority: Mixed approaches exhibit a marked improvement in accuracy, outperforming individual techniques by at least 8% overall. They address biases associated with specific regions, notably improving results for nations such as South Korea and China where name-centric methods fall short.
• Country-Specific Bias: There is a substantial variance in method performance based on geographic location. Western countries benefit more from conventional name databases, whereas emerging nations present challenges due to less representation in such datasets.

Implications and Future Directions

The paper underscores the importance of integrating diverse data sources to enhance demographic inference accuracy. The methodological advancements proposed hold significant potential for reducing biases and improving scalability in analyzing online behavior across different cultural contexts.

The authors envision the application of machine learning techniques could further refine detection methods, considering the complexities revealed in the paper. As mixed methods demonstrate superiority over traditional approaches, the integration of name and image data offers a promising avenue to address demographic inference challenges comprehensively.

In summary, this research contributes crucial insights into gender detection strategies on the web, highlighting methodological enhancements necessary for unbiased and accurate data interpretation in computational social science fields.