TextAge: A Curated and Diverse Text Dataset for Age Classification
Shravan Cheekati, Mridul Gupta, Vibha Raghu, Pranav Raj

TL;DR
TextAge is a comprehensive, curated dataset of text samples linked to specific age groups, enabling improved age classification and detection models for diverse applications like moderation and targeted communication.
Contribution
We introduce TextAge, a new diverse dataset mapping text to age groups, and demonstrate its utility through age detection and classification tasks with promising results.
Findings
Models perform well on classifying children but struggle with older age groups.
The dataset enables effective age-related language analysis and model training.
Future work will expand dataset size and improve older age group classification.
Abstract
Age-related language patterns play a crucial role in understanding linguistic differences and developing age-appropriate communication strategies. However, the lack of comprehensive and diverse datasets has hindered the progress of research in this area. To address this issue, we present TextAge, a curated text dataset that maps sentences to the age and age group of the producer, as well as an underage (under 13) label. TextAge covers a wide range of ages and includes both spoken and written data from various sources such as CHILDES, Meta, Poki Poems-by-kids, JUSThink, and the TV show "Survivor." The dataset undergoes extensive cleaning and preprocessing to ensure data quality and consistency. We demonstrate the utility of TextAge through two applications: Underage Detection and Generational Classification. For Underage Detection, we train a Naive Bayes classifier, fine-tuned RoBERTa,…
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Taxonomy
TopicsTechnology Use by Older Adults
