Guiding Computational Stance Detection with Expanded Stance Triangle Framework (2023)

Abstract: Stance detection determines whether the author of a piece of text is in favor of, against, or neutral towards a specified target, and can be used to gain valuable insights into social media. The ubiquitous indirect referral of targets makes this task challenging, as it requires computational solutions to model semantic features and infer the corresponding implications from a literal statement. Moreover, the limited amount of available training data leads to subpar performance in out-of-domain and cross-target scenarios, as data-driven approaches are prone to rely on superficial and domain-specific features. In this work, we decompose the stance detection task from a linguistic perspective, and investigate key components and inference paths in this task. The stance triangle is a generic linguistic framework previously proposed to describe the fundamental ways people express their stance. We further expand it by characterizing the relationship between explicit and implicit objects. We then use the framework to extend one single training corpus with additional annotation. Experimental results show that strategically-enriched data can significantly improve the performance on out-of-domain and cross-target evaluation.

## introduction stance (and its variant stancetaking) is a concept defined as a linguistically articulated form of social action whose meaning is construed within language, interaction, and sociocultural value @xcite @xcite . its subject can be the speaker in a conversation or the author of a social media post, and its object can be in the form of an entity, concept, idea, event, or claim. 1 the stance detection task in natural language processing aims to predict the stance of a piece 1 there are various definitions of stance and stancetaking in pragmatics and sociolinguistics field. in this article, we follow the generic "stance act" defined by @xcite . text: service was slow, but the people were friendly. aspect: "service" sentiment: negative aspect: "people" ## the stance triangle framework in linguistics, stance is originally defined as "the overt expression of an author's or speaker's attitudes, feelings, judgments, or commitment concerning the message" @xcite . sociolinguists further emphasized inter-subjective relations, which refers to how speakers position themselves with their own or other people's views, attitudes, and claims toward particular objects or ideas in ongoing communicative interaction @xcite . in this way, the core concept of stance is embedded with inherently dialogic and inter-subjective characteristics. when other interlocutors in the context indicate a stance on objects, the current speaker may take the same polarity or against their stance in various ways @xcite . given that stance is firmly grounded in the communicative interaction and stancetaking is crucial for the social construction of meaning in different discourses, @xcite proposed a holistic analytic framework named "stance triangle", which describes the communication situation in which two speakers create intersubjectivity through their evaluation of an object. as shown in figure 1 , in the stance triangle, the three key components regarding stancetaking are located at the vertices, namely the current stance holder (subject 1), what the stance is about (object), and the other stance holders in context (subject 2). the edges are then categorized into three types to reveal expression acts among interlocutors and objects. the evaluation and positioning are both affective expression @xcite . evaluation is tied to affect @xcite . it refers to a person expressing an overt emotional reaction to or displaying an affective orientation toward the object. positioning often occurs as a consequence of evaluation. the positioned subject is the one who makes the evaluation toward a specific object. by invoking an evaluation, the subject presents himself/herself as taking a particular affective orientation toward an object via the act of stancetaking. alignment is used to highlight the similarities and differences of interlocutors' stance. in communicative interaction, one could find his/her own stance is compared and contrasted with others. as communication goes on, interlocutors may line up affective stances on the shared object. alternatively, they negotiate their stance and stay differential. alignment thus becomes an essential part of intersubjectivity in dynamic dialogical interaction. the stance triangle provides insights into the dialogic nature of stance and serves as an analytical framework to understand how stance is taken in interactions. there are many qualitative studies that are inspired by this framework and applied the fundamental theory to analyze various resources, such as story narration (bohmann and ahlers, 2022), and social media text @xcite . while previous work demonstrates the success of the stance triangle in linguistics and social constructionism, to the best of our knowledge, few works use the linguistic framework to facilitate computational stance detection. example a: i believe in science. i wear a mask for your protection. explicit object: "wear a mask" / "science" stance label: favor specified target: "dr. fauci" explicit mention: no label alignment: 1 stance label: favor specified target: "covid is a hoax" explicit mention: no label alignment: -1 stance label: against specified target: "cd disk" explicit mention: no label alignment: 0 stance label: none example b: so can unborn children have rights now? explicit object: "unborn children" stance label: favor specified target: "fetus" explicit mention: no label alignment: 1 stance label: favor specified target: "abortion" explicit mention: no label alignment: -1 stance label: against specified target: "trump" explicit mention: no label alignment: 0 stance label: none specified object denotes the target for predicting the stance label. how the specified object links to an explicit object determines the alignment or dis-alignment of their stance labels. explicit mention denotes whether the specified target is an explicit object in the context. object relation indicates whether the stance label of a specified target can be inferred from the explicit object. for instance, regarding the irrelevant target "cd disk", its object relation to the explicit objects "wear a mask" and "science" is no, and that of "dr. fauci" is yes. label alignment indicates the relationship between a specified target and the explicit object in stance labeling. a value of 1 means they share the same polarity (e.g., favor vs. favor), and -1 means the opposite polarity (e.g., favor vs. against). moreover, we add the categorical value 0 when they do not have any stance label correlation (i.e., object relation value is no). therefore, the label alignment can also be used to describe the object relation. we then outline the two inference paths of the stancetaking flow: (1) as path-a shown in figure 2 , the speaker poses an affective expression on the object from a first-person's perspective. for instance, as example a shown in the expanded linguistic framework describes the key components, expression acts, and inference flows of stance detection, and it is helpful for qualitative and quantitative analysis, especially for the implicitly-mentioned stance objects. more importantly, the expanded framework sheds light on the challenging parts of computational stance detection. for instance, some domain knowledge is necessary for reasoning the label alignment between explicit objects and specified targets, and current tweetrelated datasets do not provide particular labeling of re-tweet and quotations. this framework paves the way for further research extension. ## theory-inspired practice: strategic annotation enrichment various corpora with target-aware stance annotation are constructed to facilitate computational solutions for stance detection @xcite @xcite . however, most of them only adopt a simple annotation scheme, where a single target and its corresponding label are provided. some recent datasets adopt a multi-target annotation @xcite , but the paired target number is limited. according to our linguistic framework, modeling the implication of a specified object is important. therefore, we enrich the annotation of one corpus from a single domain by adding multi-target stance labeling on explicit and implicit objects. we select the data from semeval2016 task-6 a "tweet stance detection" @xcite as it serves as a benchmark in many previous works @xcite . as shown in we first obtain a sample subset where the specified target is not explicitly mentioned in the text. next, to obtain explicit objects in an extractive manner, we apply an off-the-shelf constituency parsing model, 3 and collect all noun phrases in the constituency tree. to reduce extraction noise, we filter out the noun-phrase candidates with some criteria (e.g., being not near the verbs in the sentence, being shorter than 4 characters, and being started with hashtag token and "@user"). then linguistic annotators are invited to label the stance polarity on the explicit objects. to reduce superficial target-related patterns and biases from single target labeling, and emphasize object-object relationship, here we propose and adopt an adversarial multi-target strategy, namely selecting the explicit object that shows a stance dis-alignment to the specified target (e.g., "unborn children" and "abortion" of example b in ## conclusions in this work, we revisited the challenges of computational stance detection from a linguistic perspective. we expanded a generic linguistic framework the "stance triangle", with the relationship and labels of explicit and implicit objects, and characterized various fashions of how humans express their stances. we then followed the framework to strategically enrich the annotation of one benchmarked single-domain corpus. experimental results showed that the enriched data significantly improve the performance on out-of-domain and crosstarget evaluation, and guiding computational stance detection with expanded stance triangle framework can encourage models to learn more general and domain-invariant features. moreover, our framework paves the way for future research such as assessing the explainability of data-driven models.