This model is a multi-class classifier, model fine-tuned using the model 'bert-base-uncased'.
It is built around a large corpus of Twitter users' metadata.
It filters the data into 3 main categories - (1) Non-ExpertUser (2) ExpertUser (3) Other. The aim of this project was to find out whether a tweet belongs to an individual or not. And if it is, whether the person is an expert in the field of Security and Privacy.
Originally, the Model had 4 classes - where the 'Other' field was classified into 'Non-Person' (denoting accounts such as organizations)and 'Unknown'.
Since the main aim was to find out about whether a user is a non-expert user or not, the classes were reduced to 3 classes in this version 2.
The validation scores for the module were as follows
Accuracy = 0.93
| Class | Precision | Recall | F1-Score |
|---|---|---|---|
| ExpertUser (0) | 0.88 | 0.90 | 0.89 |
| Non-ExpertUser (1) | 0.95 | 0.97 | 0.96 |
| Other (2) | 0.85 | 0.78 | 0.81 |
Paper: The paper detailing how it was designed can be found here Perspectives of non-expert users on cyber security and privacy: An analysis of online discussions on twitter
Please cite the paper if you use this model :
Nandita Pattnaik, Shujun Li, and Jason R.C. Nurse. 2023.
Perspectives of non-expert users on cyber security and
privacy: An analysis of online discussions on Twitter.
Computers & Security 125 (2023), 103008. https://doi.org/10.1016/j.cose.2022.103008
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