Abstract: The task of author profiling aims to distinguish the author’s profile traits from a given content. It has got potential applications in marketing, forensic analysis, fake profile detection, etc. In recent years, the usage of bi-lingual text has raised due to the global reach of social media tools as people prefer to use language that expresses their true feelings during online conversations and assessments. It has likewise impacted the use of bi-lingual (English and Roman-Urdu) text in the sub-continent (Pakistan, India, and Bangladesh) over social media. To develop and evaluate methods for bi-lingual author profiling, benchmark corpora are needed. The majority of previous efforts have focused on developing mono-lingual author profiling corpora for English and other languages. To fulfill this gap, this study aims to explore the problem of author profiling on bi-lingual data and presents a benchmark corpus of bi-lingual (English and Roman-Urdu) tweets. Our proposed corpus contains 339 author profiles and each profile is annotated with six different traits including age, gender, education level, province, language, and political party. As a secondary contribution, a range of deep learning methods, CNN, LSTM, Bi-LSTM, and GRU, are applied and compared on the three different bi-lingual corpora for age and gender identification, including our proposed corpus. Our extensive experimentation showed that the best results for both gender identification task (Accuracy = 0.882, F1-Measure = 0.839) and age identification (Accuracy = 0.735, F1-Measure = 0.739) are obtained using Bi-LSTM deep learning method. Our proposed bi-lingual tweets corpus is free and publicly available for research purposes.
Keywords: Twitter, author profiling, roman-urdu, deep learning, bi-lingual, gender identification
