Description:
This model is based on the hierarchical transformer architecture described in the associated paper.
Paper:
Interpretable rumor detection in microblogs by attending to user interactions. Proceedings of the AAAI Conference on Artificial Intelligence, April 2020 (Vol. 34, No. 05, pp. 8783-8790).
Author(s):
Khoo, L. M. S., Chieu, H. L., Qian, Z., & Jiang, J. (2020).
SGnlp is an initiative by AI Singapore's NLP Hub. They aim to bridge the gap between research and industry, promote translational research, and encourage adoption of NLP techniques in the industry.
Various NLP models, other than aspect sentiment analysis are available in the python package. You can try them out at
SGNLP-Demo
|
SGNLP-Github
.
The train and evaluation datasets were derived from the Twitter15, Twitter16 and PHEME datasets. The full dataset can be downloaded from the author's
Dropbox
.
Model Inputs:
Thread of tweets. The first tweet should be the target tweet.
Model Outputs:
Array of logits for each class (True, False, Unverified, Non-Rumour). This can be converted into probabilities using the softmax function.
Model Size:
~60mb
Model Inference Info:
Not available.
Usage Scenarios:
Rumour detection / fake news detection on Twitter
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