Social Media Writing Style Fingerprint

2017/12/11 by Himank Yadav, Yadav, Himank, Juliang Li +1
Computer Science · #Authorship Attribution and Profiling #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1712.04762

openalex publication_date 2017/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present our approach for computer-aided social media text authorship attribution based on recent advances in short text authorship verification. We use various natural language techniques to create word-level and character-level models that act as hidden layers to simulate a simple neural network. The choice of word-level and character-level models in each layer was informed through validation performance. The output layer of our system uses an unweighted majority vote vector to arrive at a conclusion. We also considered writing bias in social media posts while collecting our training dataset to increase system robustness. Our system achieved a precision, recall, and F-measure of 0.82, 0.926 and 0.869 respectively.

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