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

<table>
  <tr>
    <th>Class</th>
    <th>Precision</th>
    <th>Recall</th>
    <th>F1-Score</th>
  </tr>
  <tr>
    <td>ExpertUser (0)</td>
    <td>0.88</td>
    <td>0.90</td>
    <td>0.89</td>
  </tr>
  <tr>
    <td><b>Non-ExpertUser (1)</b></td>
    <td><b>0.95</b></td>
    <td><b>0.97</b></td>
    <td><b>0.96</b></td>
  </tr>
   <tr>
    <td>Other (2)</td>
    <td>0.85</td>
    <td>0.78</td>
    <td>0.81</td>
  </tr>
</table>

<b>Paper:</b> The paper detailing how it was designed can be found here <a href="https://www.sciencedirect.com/science/article/pii/S016740482200400X">Perspectives of non-expert users on cyber security and privacy: An analysis of online discussions on twitter</a>

<b>Please cite the paper if you use this model </b>: 

Nandita Pattnaik, Shujun Li, and Jason R.C. Nurse. 2023.<br> Perspectives of non-expert users on cyber security and
privacy: An analysis of online discussions on Twitter.<br> Computers & Security 125 (2023), 103008. https://doi.org/10.1016/j.cose.2022.103008

{"license": "cc-by-nc-nd-4.0"}
transformers tf bert text-classification license:cc-by-nc-nd-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #text-classification #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #region-us 

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



**Paper:** The paper detailing how it was designed can be found here <a href="URL 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. URL



TAGS
#transformers #tf #bert #text-classification #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #tf #bert #text-classification #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #region-us 
