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Article summary:

1. This paper presents a new dataset for multi-target stance detection.

2. Experiments with several neural models on the dataset show that they are more effective in jointly modeling the overall position towards two related targets compared to independent predictions and other models of joint learning.

3. The dataset is publicly available to facilitate further research in multi-target stance classification.

Article analysis:

The article is generally trustworthy and reliable, as it provides a detailed description of the new dataset for multi-target stance detection, along with experiments conducted using several neural models on the dataset. The results of these experiments demonstrate that the neural models are more effective in jointly modeling the overall position towards two related targets compared to independent predictions and other models of joint learning. Furthermore, the authors make the new dataset publicly available, which allows for further research into this area.

The article does not appear to have any biases or one-sided reporting, as it provides an objective overview of the new dataset and its potential applications. Additionally, all claims made by the authors are supported by evidence from their experiments and results. There do not appear to be any missing points of consideration or unexplored counterarguments in this article either.

The article does not contain any promotional content or partiality; instead, it provides an unbiased overview of the new dataset and its potential applications in multi-target stance classification. Furthermore, possible risks associated with using this dataset are noted throughout the article, such as potential errors due to incorrect annotations or data imbalance issues. Finally, both sides of this issue are presented equally throughout the article, making it a balanced and reliable source of information on this topic.