Boosting gender identification using author preference

Küçükyılmaz, Tayfun; Deniz, Ayça; Kızılöz, Hakan Ezgi

doi:10.1016/j.patrec.2020.10.002

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Still, no prior work has been conducted on gender distribution in user collectives. In cases with gender labels available, small and large data, where gender information could be verified and collected, were used; users without gender information were to be excluded from the initially collected data [10,13,16].…”

Section: Reviews On Related Workmentioning

confidence: 99%

“…However, while it is inevitably difficult to obtain individual gender data [8], the anonymity and privacy policy of social media have made it difficult or impossible to acquire gender information from social media [9]. As a result, most prior gender research with social media has been made by using small-size or large-size data, where gender information could be collected [10,11]. If unavailable, gender information had to be manually annotated by researchers [12], or simple estimation approaches were adopted based on relevant cues such as names [13,14].…”

Section: Introduction 1background and Purposementioning

confidence: 99%

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Machine-Learning-Based Gender Distribution Prediction from Anonymous News Comments: The Case of Korean News Portal

Suh

2022

Sustainability

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Anonymous news comment data from a news portal in South Korea, naver.com, can help conduct gender research and resolve related issues for sustainable societies. Nevertheless, only a small portion of gender information (i.e., gender distribution) is open to the public, and therefore, it has rarely been considered for gender research. Hence, this paper aims to resolve the matter of incomplete gender information and make the anonymous news comment data usable for gender research as new social media big data. This paper proposes a machine-learning-based approach for predicting the gender distribution (i.e., male and female rates) of anonymous news commenters for a news article. Initially, the big data of news articles and their anonymous news comments were collected and divided into labeled and unlabeled datasets (i.e., with and without gender information). The word2vec approach was employed to represent a news article by the characteristics of the news comments. Then, using the labeled dataset, various prediction techniques were evaluated for predicting the gender distribution of anonymous news commenters for a labeled news article. As a result, the neural network was selected as the best prediction technique, and it could accurately predict the gender distribution of anonymous news commenters of the labeled news article. Thus, this study showed that a machine-learning-based approach can overcome the incomplete gender information problem of anonymous social media users. Moreover, when the gender distributions of the unlabeled news articles were predicted using the best neural network model, trained with the labeled dataset, their distribution turned out different from the labeled news articles. The result indicates that using only the labeled dataset for gender research can result in misleading findings and distorted conclusions. The predicted gender distributions for the unlabeled news articles can help to better understand anonymous news commenters as humans for sustainable societies. Eventually, this study provides a new way for data-driven computational social science with incomplete and anonymous social media big data.

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Section: Reviews On Related Workmentioning

confidence: 99%

Section: Introduction 1background and Purposementioning

confidence: 99%

Machine-Learning-Based Gender Distribution Prediction from Anonymous News Comments: The Case of Korean News Portal

Suh

2022

Sustainability

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“…It employs sociolinguistic-inspired text features to enhance the performance of text mining methods. Moreover, the authors of [29] have proposed integrating the text mining approach to examine author preferences for gender classification tasks. Searching for linguistic information in the author's preferences could improve the performance of the gender classification.…”

Section: Gender Identification Techniquesmentioning

confidence: 99%

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

et al. 2022

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Organizations analyze customers’ personal data to understand and model their behavior. Identifying customers’ gender is a significant factor in analyzing markets that help plan the promotional campaigns, determine target customers and provide relevant offers. Several techniques were developed to analyze different types of data, including text, image, speech, and biometrics, to identify the gender of the user. The method of synthesis of the profile name differs from one customer to another. Using numerical substitutions of specific letters, known as Leet language, impedes the gender identification task. Moreover, using acronyms, misspellings, and adjacent names impose additional challenges. Towards this goal, this work uses the customers’ profile names associated with submitted reviews to recognize the customers’ gender. First, we create datasets of profile names extracted from the customers’ reviews. Secondly, we introduce a dynamic pruned n-gram model for identifying the gender of the user. It starts with data segmentation to handle adjacent parts, followed by data conversion and cleaning to fix the use of Leet language. Feature selection through a dynamic pruned n-gram model is the next step with the recurrent misspelling correction using fuzzy matching. We evaluate the proposed approach on the real data collected from active web resources. The obtained results demonstrate its validity and reliability.

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“…In the literature, it is common to filter out the words that occur only once as they do not provide any predictive power [58]. By building on this idea, we filter out the words whose Information Gain value is below a certain threshold.…”

Section: ) Filter-based Feature Selectionmentioning

confidence: 99%