Social Media Writing Style Fingerprint
This work addresses authorship verification for social media posts, which is an incremental improvement in a domain-specific area.
The authors tackled the problem of social media text authorship attribution by developing a computer-aided system using word-level and character-level models in a neural network-like structure, achieving a precision of 0.82, recall of 0.926, and F-measure of 0.869.
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.