From Stances' Imbalance to Their HierarchicalRepresentation and Detection

Abstract

Stance detection has gained increasing interest from the research community due to its importance for fake news detection. The goal of stance detection is to categorize an overall position of a subject towards an object into one of the four classes: agree, disagree, discuss, and unrelated. One of the major problems faced by current machine learning models used for stance detection is caused by a severe class imbalance among these classes. Hence, most models fail to correctly classify instances that fall into minority classes. In this paper, we address this problem by proposing a hierarchical representation of these classes, which combines the agree, disagree, and discuss classes under a new related class. Further, we propose a two-layer neural network that learns from this hierarchical representation and controls the error propagation between the two layers using the Maximum Mean Discrepancy regularizer. Compared with conventional four-way classifiers, this model has two advantages: (1) the hierarchical architecture mitigates the class imbalance problem; (2) the regularization makes the model to better discern between the related and unrelated stances. An extensive experimentation demonstrates state-of-the-art accuracy performance of the proposed model for stance detection.

“Here Are the Rules: Ignore All Rules”: Automatic Contradiction Detection in Spanish

“…Table 3 presents the results. The results obtained in the Unrelated class indicate that the system is capable of detecting with excellent F 1 m these types of examples, corroborating the results obtained in the literature on this type of semantic relation between texts [30]. The other two classes have room for improvement, by using, for instance, external knowledge.…”

Beyond facts – a survey and conceptualisation of claims in online discourse analysis

“…In the semantic web community and the fields of knowledge representation and knowledge base construction/augmentation, facts are seen as the knowledge that is represented in KGs or KBs [6,9,12,28,31,46,47,50,110,110,115,131,153,165,189,193,196,200,216,223]. More precisely, items in KGs or KBs are coined statements of facts or assertions or triples encoding/representing facts [28,31,115,165,193], with the facts being assumed to be true, can be proven to be true or are likely to hold [31,131,142].…”

“…Viewpoint extraction is closely connected to the stance detection problem, a supervised classification problem in NLP where the stance of a piece of text towards a particular target is explored. Stance detection has been applied in different contexts, including social media (stance of a tweet towards an entity or topic) [10,38,41,93,116,174,210], online debates (stance of a user post or argument/claim towards a controversial topic or statement) [13,67,167,198], and news media (stance of an article towards a claim) [20,70,141,203,216]. A recent work by Schiller et al [158] details the different and varying task definitions found in previous works, diverging not only with regard to domains, but also classes and number and type of inputs, and introduce a benchmark for stance detection that allows the comparison of models against a variety of heterogeneous datasets.…”

A Framework to Understand Attitudes towards Immigration through Twitter

“…In this context, we develop a mixed approach grounded on the theories about attitude formation instead of following a hate speech approach, which is limited to hate but not necessarily opposition/approval or feelings of threat/empathy toward migration. Each formation theory defines an attitude, and, in cases where the classifier confidence is low, we define an undisclosed stance to account for participation in the debate without disclosing attitude [51]. Particularly, we build upon our previous work to classify users into attitudes as political stances using a tree-based classifier [17].…”