Text-Based Age and Gender Prediction for Online Safety Monitoring

Loo, Janneke van de; Pauw, Guy De; Daelemans, Walter

doi:10.17781/p002012

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…Moreover, it could be useful to validate previously provided demographic user data. For example, this approach could be used in anonymous digital communities (e.g., only for people aged under 18) to validate user profiles and to flag suspicious profiles containing potentially false information (van de Loo et al, 2016). In order to protect users' privacy, the feature extraction and potentially model predictions should happen on the user's end, for example on the smartphone.…”

Section: Discussionmentioning

confidence: 99%

Age and gender in language, emoji, and emoticon usage in instant messages

Koch

Romero

Stachl

2022

Computers in Human Behavior

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Text is one of the most prevalent types of digital data that people create as they go about their lives. Digital footprints of people's language usage in social media posts were found to allow for inferences of their age and gender. However, the even more prevalent and potentially more sensitive text from instant messaging services has remained largely uninvestigated. We analyze language variations in instant messages with regard to individual differences in age and gender by replicating and extending the methods used in prior research on social media posts. Using a dataset of 309,229 WhatsApp messages from 226 volunteers, we identify unique age-and gender-linked language variations. We use cross-validated machine learning algorithms to predict volunteers' age (MAEMd = 3.95, rMd = 0.81, R 2 Md = 0.49) and gender (AccuracyMd = 85.7%, F1Md = 0.67, AUCMd = .82) significantly above baseline-levels and identify the most predictive language features. We discuss implications for psycholinguistic theory, present opportunities for application in author profiling, and suggest methodological approaches for making predictions from small text data sets. Given the recent trend towards the dominant use of private messaging and increasingly weaker user data protection, we highlight rising threats to individual privacy rights in instant messaging.

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Section: Discussionmentioning

confidence: 99%

Age and gender in language, emoji, and emoticon usage in instant messages

Koch

Romero

Stachl

2022

Computers in Human Behavior

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“…Moreover, it could be useful to validate previously provided demographic user data. For example, our approach could be used in anonymous digital communities (e.g., only for people aged under 18) to validate user profiles and file suspicious profiles containing potentially false information for monitoring, as previous work has pointed out (van de Loo et al, 2016). In order to protect users' privacy, the feature extraction and potentially the modeling should happen on the user's end (i.e., on the phone) and only the extracted features or prediction results are transmitted to the provider.…”

Section: Discussionmentioning

confidence: 99%

Age and Gender in Language, Emoji, and Emoticon Usage in Instant Messages

Koch¹,

Peter²,

Stachl³

2020

Preprint

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Text is one of the most prevalent types of digital data that is constantly created as people go about their lives. The digital footprints of people’s language use in social media posts were found to allow for inferences of their age and gender. However, the even more prevalent and sensitive text data from instant messaging services has remained largely uninvestigated. Here we analyze language variations in WhatsApp instant messages with regard to individual differences in age and gender. Based on our dataset of 308,632 WhatsApp messages from 199 participants, we computed general message characteristics, linguistic features using the Linguistic Inquiry and Word Count (LIWC), and features regarding participants’ preferences for emoji and emoticons. We use cross-validated machine learning algorithms to predict participants’ age (MAE = 5.14, MAEsd = 0.71) and gender (AUC = 0.84, AUCsd = 0.06) and identify the most predictive features. We discuss implications for psycholinguistic theory, present opportunities for the application of author profiling techniques, and raise issues regarding the protection of individual privacy rights in chat environments.

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“…Janneke van de Loo et al, explored [21] the abilities of text based gender and age detection by analysing social media harmful content. Particularly, the authors concentrated on the use-case of determining sexual predators who are trying to groom children in online by providing false gender and age information in the profiles of users.…”

Section: Literature Survey Of Work Related To Age and Gender Predictionmentioning

confidence: 99%

A New Term Representation Method for Gender and Age Prediction

Para¹,

Patel

2023

IJRITCC

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Author Profiling is a kind of text classification method that is used for detecting the personality profiles such as age, gender, educational background, place of origin, personality traits, native language, etc., of authors by processing their written texts. Several applications like forensic analysis, security and marking are used the techniques of author profiling for finding the basic details of authors. The main problem in the domain of author profiling is preparation of suitable dataset for predicting the characteristics of authors. PAN is one organization conducting competitions on various types of shared tasks. In 2013, PAN organizers presented the task of author profiling in their series of competitions and continued this task in further years. They arranged different kinds of datasets in different varieties of languages. From 2013 onwards several researchers proposed solutions for author profiling to predict different personality features of authors by utilizing the datasets provided in PAN competitions. Researchers used different kinds of features like character based, lexical or word based, structural features, syntactic, content based, style based features for distinguishing the author’s writing styles in their texts. Most of the researchers observed that the content based features like words or phrases those are used in the text are most useful for detecting the personality features of authors. In this work, the experiment conducted with the content based features like most important words or terms for predicting age group and gender from the PAN competition datasets. Two datasets such as PAN 2014 and 2016 author profiling datasets are used in this experiment. The documents of dataset are converted in to a vector representation which is a suitable format for giving training to machine learning algorithms. The term representation in a document vector plays a crucial role to improve the performance of gender and age group prediction.The Term Weight Measures (TWMs) are such techniques used for this purpose to represent the significance of a term value in document vector representation. In this work, we developed a new TWM for representing the term value in document vector representation. The proposed TWM’s efficiency is compared with the efficiency of other existing TWMs. Two Machine Learning (ML) algorithms like SVM (Support Vector Machine) and RF (Random Forest) are considered in this experiment for estimating the accuracy of proposed approach. We recognized that the proposed TWM accomplished best accuracies for gender and age prediction in two PAN Datasets.

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Text-Based Age and Gender Prediction for Online Safety Monitoring

Cited by 12 publications

References 17 publications

Age and gender in language, emoji, and emoticon usage in instant messages

Age and gender in language, emoji, and emoticon usage in instant messages

Age and Gender in Language, Emoji, and Emoticon Usage in Instant Messages

A New Term Representation Method for Gender and Age Prediction

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