TwiRole: A Hybrid Model for Role-related User Classification on Twitter

We publish a pre-trained version of TwiRole for role-related user classification on Twitter. The model can automatically crawl a user's profile, profile image and recent tweets, and classify a Twitter user into 📣 Brand, 👚 Female or 👔 Male, which is an aid to user-related research on Twitter. If TwiRole is helpful for your study or research, please ⭐ it!

Here is a live demo and we also provide a CodeOcean Capsule for reproducibility.

Getting Started

Prerequisites

• Python 2 or Python 3 (Anaconda recommended)

Installation and Configuration

• Clone this repo on your local machine

git clone https://github.com/liuqingli/TwiRole.git

• Install essential libraries

cd TwiRole
pip install -r requirements.txt

• Create or use your Twitter account and request your Twitter credentials from the Twitter Developer website.

• Replace the placeholders (line 53-56) in code/user_classifier.py with your Twitter credentials.

First Classification Task

TwiRole can detect a single user or multiple users (The screen names of users should be saved in a CSV file line by line). The output contains the final label and the probability of each role.

In the first run, some nltk libraries will be automatically downloaded and installed. User files will be saved in the user folder.

A Single User

python user_classifier.py -u [screen_name]

Example:

python user_classifier.py -u CNN
Task 1: CNN  =>  Brand   [Brand: 100.00%, Female: 0.00%, Male: 0.00%]

Multiple Users

python user_classifier.py -f [CSV File]

Citation

If you apply our model in a scientific publication, we would appreciate citations to the following paper on Arxiv:

@misc{Li2018TwiRole,
Author = {Liuqing Li, Ziqian Song, Xuan Zhang and Edward A. Fox},
Title = {A Hybrid Model for Role-related User Classification on Twitter},
Year = {2018},
Eprint = {arXiv:1811.10202},
}

Or you can also use the bibtex below to cite this repository:

@misc{Li2018PreTwiRole,
title={Pre-trained TwiRole for User Classification on Twitter},
author={Liuqing Li, Ziqian Song, Xuan Zhang and Edward A. Fox},
year={2018},
publisher={Github},
journal={GitHub repository},
howpublished={\url{https://github.com/liuqingli/TwiRole}},
}

Details

Model Architecture

Evaluation

First, we compare TwiRole with Ferrari et al.'s work on the same Kaggle dataset, since they also categorized Twitter users into Brand, Female, and Male. The overall accuracy (Acc = 0.899) of TwiRole is higher than with Ferrari et al.'s approach (Acc = 0.865), and our results are more balanced across different roles.

Second, we evaluated TwiRole in the real Twitter environment by applying it to detect a set of Twitter users extracted from event-related tweet collections (e.g., hurricanes, earthquakes, and shootings). We randomly chose 100 predicted users in each class, and manually checked their roles by browsing their Twitter pages. The precision results of TwiRole are shown below:

Role	Brand	Female	Male
Precision	0.84	0.86	0.81

Notes

TwiRole focuses on users' rich information during prediction but might not best describe the role of a user. As a result, it can effectively support role-related research at the group level, but research on specific users is not recommended.

Acknowledgments

• GETAR project supported by the National Science Foundation under Grant No. IIS-1619028
• Twitter user classification dataset on Kaggle for training and testing
• Thanks to the developers of Twarc