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Article summary:

1. Stance detection is the task of automatically determining whether an author is in favor or against a given target.

2. Traditionally, this task is approached by learning a target-specific classifier that is trained for detecting a stance on the same target of interest.

3. This article proposes an adversarial attention network to bridge the gap between posts from different targets and determine both the topic and sentiment of each post.

Article analysis:

The article provides a comprehensive overview of the current state of stance detection and its challenges, as well as proposing an innovative solution to address these issues. The authors provide evidence for their claims, such as citing relevant literature and providing examples to illustrate their points. The article also presents both sides of the argument equally, noting potential risks associated with using multi-target data for stance detection. However, there are some areas where more detail could be provided, such as how exactly the proposed adversarial attention network works and what datasets were used to evaluate it. Additionally, while the authors note potential risks associated with using multi-target data for stance detection, they do not provide any concrete solutions or strategies for mitigating these risks.