  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, and XLNet models to
differentiate between language patterns of minors and young-adults and over.
For Generational Classification, the models classify language patterns into
different age groups (kids, teens, twenties, etc.). The models excel at
classifying the "kids" group but struggle with older age groups, particularly
"fifties," "sixties," and "seventies," likely due to limited data samples and
less pronounced linguistic differences. TextAge offers a valuable resource for
studying age-related language patterns and developing age-sensitive language
models. The dataset's diverse composition and the promising results of the
classification tasks highlight its potential for various applications, such as
content moderation, targeted advertising, and age-appropriate communication.
Future work aims to expand the dataset further and explore advanced modeling
techniques to improve performance on older age groups.
