Twitter User Classification using Ambient Metadata
This work addresses the need for user classification on social media platforms like Twitter, but it is incremental as it applies existing methods to a new type of data.
The study tackled the problem of classifying Twitter users by using ambient metadata, specifically profile descriptions, and found that this metadata is an effective feature for classification tasks.
Microblogging websites, especially Twitter have become an important means of communication, in today's time. Often these services have been found to be faster than conventional news services. With millions of users, a need was felt to classify users based on ambient metadata associated with their user accounts. We particularly look at the effectiveness of the profile description field in order to carry out the task of user classification. Our results show that such metadata can be an effective feature for any classification task.