• Stance Detection Algorithms
• • What is Stance Detection ?
  • Algorithms list

Stance Detection Algorithms

What is Stance Detection ?

Stance detection is usually considered as a subproblem of sentiment analysis and aims to identify the stance of the text author towards a target (an entity, concept, event, idea, opinion, claim, topic, etc.) either explicitly mentioned or implied within the text.

Definition 1 (Stance Detection). For an input in the form of a piece of text and a target pair, stance detection is a classification problem where the stance of the author of the text is sought in the form of a category label from this set: {Favor, Against, Neither}.

Definition 2 (Multi-Target Stance Detection). For an input in the form of a piece of text and a set of related targets, multi-target stance detection is a classification problem where the stance of the text author is sought as a category label from this set: {Favor, Against, Neither} for each target and each stance classification (for each target) might have an effect on the classifications for the remaining targets

Definition 3 (Cross-Target Stance Detection). Cross-target stance detection is a classification problem where the stance of the text author is sought for a specific target as a category label from this set: {Favor, Against, Neither}, in a settings where stance annotations are available for (though related but) different targets, i.e., there is not enough stance-annotated training data for the target under consideration.

Definition 4 (Rumour Stance Classification). For an input in the form of a piece of text and a rumour pair, rumour stance classification is a problem where the position of the text author towards the veracity of the rumour is sought for, in the form of a category label from this set: {Supporting, Denying, Querying, Commenting}.

Definition 5 (Fake News Stance Detection). For an input in the form of news headline and a news body pair (where the headline and body parts may belong to different news articles), this is a classification problem where the stance of the body towards the claim of the headline is sought for, in the form of a category label from this set: {Agrees, Disagrees, Discusses (the same topic), Unrelated}.

Algorithms list

This library mainly includes the following algorithms:

• Target-adaptive Graph for Cross-target Stance Detection TPDG
• 基于循环交互注意力网络的问答立场分析 AnswerStance
• 基于多任务对比学习的问答立场检测 Multi-task-Contrastive