Name
Abstract | ||
|---|---|---|
We apply an ensemble pipeline composed of a character-level convolutional neural network (CNN) and a long short-term memory (LSTM) as a general tool for addressing a range of disinformation problems. We also demonstrate the ability to use this architecture to transfer knowledge from labeled data in one domain to related (supervised and unsupervised) tasks. Character-level neural networks and transfer learning are particularly valuable tools in the disinformation space because of the messy nature of social media, lack of labeled data, and the multi-channel tactics of influence campaigns. We demonstrate their effectiveness in several tasks relevant for detecting disinformation: spam emails, review bombing, political sentiment, and conversation clustering. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Computation and Language | Journal |
Volume | Citations | PageRank |
abs/1905.10412 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Numa Dhamani | 1 | 0 | 1.01 |
| Paul Azunre | 2 | 0 | 1.35 |
| Jeffrey L. Gleason | 3 | 0 | 0.68 |
| Craig Corcoran | 4 | 11 | 2.31 |
| Garrett Honke | 5 | 0 | 1.35 |
| Steve Kramer | 6 | 0 | 1.01 |
| Jonathon Morgan | 7 | 0 | 1.01 |