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Predicting age groups of Twitter users based on language and metadata features.

Antonio A Morgan-Lopez¹, Annice E Kim², Robert F Chew³

¹Behavioral Health and Criminal Justice Research Division, RTI International, Research Triangle Park, North Carolina, United States of America.

|August 30, 2017

Summary

Predicting Twitter user age using language and metadata is key for health campaigns. Combining these features best identifies youth and young adults, aiding public health efforts.

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Area of Science:

Social Media Research
Public Health Informatics
Computational Social Science

Background:

Health organizations utilize social media platforms like Twitter for health communication.
Evaluating the reach of social media health campaigns requires understanding audience demographics, particularly age.
Accurate audience segmentation is crucial for effective public health interventions and message tailoring.

Purpose of the Study:

To assess the predictive power of linguistic and metadata features for determining Twitter user age groups.
To compare the effectiveness of models using language features alone, metadata features alone, and a combination of both.
To identify key linguistic and metadata indicators for predicting user age on Twitter.

Main Methods:

A labeled dataset of Twitter users (youth, young adults, adults) was created using birthday announcement tweets.
Publicly available tweets (200 most recent) and user metadata were collected for each labeled user.
L1-regularized logistic regression models were employed to predict age groups, evaluating accuracy, precision, recall, and F1 scores.

Main Results:

Models incorporating both linguistic and metadata features achieved the highest performance (74% precision, 74% recall, 74% F1).
Models using only Twitter metadata were least accurate (58% precision, 60% recall, 57% F1).
Keywords like "school" (youth) and "college" (young adults) were strong predictors; older adults were harder to classify accurately.

Conclusions:

Combining linguistic and metadata analysis offers a robust method for predicting the age of Twitter users, particularly for younger demographics.
This approach can enhance public health surveillance by enabling more precise targeting and evaluation of social media-based health education campaigns.
Future research should explore refining models for older adult classification and validating findings across different social media platforms.