A Unified Deep Learning Architecture for Abuse Detection
Table 3: Metadata Importance. The values are obtained using the Cyberbullying dataset.
['Metadata Features', 'Acronym', 'AUC']
Metadata importance: As described earlier, we use a number of metadata features extracted from the tweets and their authors, namely tweet-based, user-based, and network-based. These metadata play an important role on the improvement of the performance. When combined with the raw text, they substantially increase all the metrics. However, not all of them have the same impact on the performance (some are more essential than others). In order to determine how each one of these metadata affects our model, we experiment with the cyberbullying dataset and calculate their importance. We chose this dataset as we have all groups of metadata available (user, tweet, network, text) and, therefore, it is possible to examine how each of them contributes to the model. The results for other datasets are following similar trends and are omitted due to space limitations.