Discover Your Social Identity from What You Tweet:a Content Based Approach
Table 5: The confusion matrix generated by our best trained model from the test set of identity dataset
['TruthPrediction', 'Regular', 'Media', 'Celebrity', 'Sport', 'Company', 'Government', 'Reporter']
We perform an error analysis to investigate why our model fails in certain cases. As shown in this table, it is relatively harder for our model to distinguish between celebrities and regular users. We further looked at such errors with high confidences and found that some celebrities just have not posted any indicating words in their tweets or descriptions. For example, one celebrity account only use “A Virgo” in the description without any other words, which makes this account predicted as a regular user. Including other features like number of followers or network connections may overcome this issue, and we leave it for future work. Another common error happens when dealing with non-English tweets. Even enhanced with transferred knowledge from the large-scale verify dataset, our model still cannot handle some rare languages in the data.