A New Direction in Stance Detection: Target-Stance Extraction in the Wild (2023)

Abstract: Stance detection aims to detect the stance toward a corresponding target. Existing works use the assumption that the target is known in advance, which is often not the case in the wild. Given a text from social media platforms, the target information is often unknown due to implicit mentions in the source text and it is infeasible to have manual target annotations at a large scale. Therefore, in this paper, we propose a new task Target-Stance Extraction (TSE) that aims to extract the (target, stance) pair from the text. We benchmark the task by proposing a two-stage framework that first identifies the relevant target in the text and then detects the stance given the predicted target and text. Specifically, we first propose two different settings: Target Classification and Target Generation, to identify the potential target from a given text. Then we propose a multi-task approach that takes target prediction as the auxiliary task to detect the stance toward the predicted target. We evaluate the proposed framework on both in-target stance detection in which the test target is always seen in the training stage and zero-shot stance detection that needs to detect the stance for the targets that are unseen during the training phase. The new TSE task can facilitate future research in the field of stance detection.

## introduction stance detection aims to automatically identify people's attitude/viewpoint (e.g., favor or against) expressed in texts toward a target that is generally a controversial topic or political figure @xcite @xcite . for example, the tweet in figure expresses a stance of "against" toward the target "atheism." social media platforms like twitter, facebook and other debate forums have become an integral way of opinion dissemination these days @xcite . the peculiar characteristics of these platforms are that the information is usually scattered across texts and the opinionated text could be expressed toward target entities in an implicit way. existing methods have achieved promising performance on in-target stance detection in which same targets are seen in both train and test sets @xcite @xcite and cross-target stance detection that aims to transfer the knowledge from a source target to a destination target @xcite @xcite . however, almost all previous methods work under the assumption that the target is known or manually identified, which is often not the case in the wild. in practice, the target is unknown given a text and it is usually implicitly mentioned in the text, as can be seen from the example shown in figure 1 . therefore, instead of detecting the stance given both the target and text, we propose a more challenging task target-stance extraction (tse) in the context of stance detection that aims to extract the (target, stance) pair from the text. the new tse task is more challenging because it includes both target identification and stance detection. to tackle this task, we propose a two-step framework that first identifies the relevant target in the text and then detects the stance given the predicted target and the text, as shown in figure 1 . in the first stage, we propose two different settings to identify the target discussed in a text: (1) target classification, where we train a text classifier @xcite @xcite to predict the target as one of the pre-defined targets, and (2) target generation, where we leverage bart @xcite model that is pretrained on a keyphrase generation dataset @xcite @xcite to generate keyphrases (e.g., "christianity" in figure 1 ), and then map them to one of the pre-defined targets (e.g., "atheism"). in the second stage, we propose a multi-task framework that takes the target prediction as the auxiliary task for stance detection. we expect the stance detection model to better capture the target-related features and to develop a better understanding of the text itself with the auxiliary task. our proposed two-step framework can not only be applied to in-target stance detection, but also zero-shot stance detection in which targets of test examples are not seen in the train set. we evaluate the proposed framework on the combined set of four stance datasets @xcite @xcite for in-target stance detection. further, we extend our framework to zero-shot stance detection and test it on six targets of diverse domains @xcite @xcite @xcite . it is worth noting that our primary goal is not to present a new state-of-the-art model, but to deliver a new and more challenging task to stimulate research on stance detection. we summarize our contributions as follows: stance detection the stance detection task aims to detect the stance toward a specific target @xcite @xcite . the target could be defined in a variety of forms: a controversial figure @xcite @xcite , a hot topic such as gun control @xcite @xcite @xcite or a claim @xcite @xcite . in previous works, the target is usually manually provided along with the input sentence to a stance classifier. however, given a post on social media, we may not have a direct clue about the target information due to their implicit mentions, and it is infeasible to do large-scale target annotations by humans. motivated by this observation, we propose a new task named target-stance extraction (tse) that aims to extract both the target and the corresponding stance from a given text. besides the in-target stance detection @xcite in which the test target is seen in the training stage, crosstarget stance detection @xcite @xcite and zero-shot stance detection @xcite @xcite have also drawn a lot of attention recently. in crosstarget stance detection, a classifier is adapted from a different but related target to a destination target in a one-to-one way, whereas in zero-shot stance detection we need to detect the stance for a variety of unseen targets at the inference stage. in this paper, we evaluate our proposed framework in both in-target and zero-shot settings. keyphrase generation / extraction keyphrase generation or extraction is the task where given a source document (e.g., a scientific article, newspaper article, or webpage), we predict the keyphrases that best describe or summarize that document @xcite @xcite @xcite @xcite @xcite @xcite . in the context of stance detection, we can use keyphrase generation models to generate keyphrases that are target-related words give an input text. to our knowledge, target-related keyphrase generation task has not been explored before for stance detection. the most popular paradigm for the keyphrase generation task is the one2seq encoder-decoder framework @xcite where given a document, we generate a sequence of @xcite separated keyphrases in an auto-regressive way. we use the pre-trained bart model @xcite finetuned separately on three keyphrase generation datasets, i.e., openkp @xcite , kp-times @xcite , and fulltextkp @xcite and generate keyphrases using the one2seq model. ## approach as discussed in the previous section, tse is a challenging task that involves both target identification and stance detection given a text. to tackle this task, we propose a two-stage framework, in which we first identify the target from a given text using either a target classification or target generation approach and then detect the stance toward the predicted target with a stance classifier in the second stage. the overall framework of our proposed approach is shown in figure 2 . ## results and analysis in this section, we first present the results for target classification and target generation in §6.1. we then present the set of experiments performed on the intarget tse task and show the results obtained by using the aforementioned baselines in §6.2. in the next section §6.3, we report the results for the zeroshot tse task where targets of test set are not seen in the train set. finally, we study the performance of multi-task models in §6.4. each result is the average of three runs with different initializations. ## conclusion in this paper, we introduce a new target-stance extraction (tse) task to identify both target and corresponding stance in the wild. different from original stance detection task that aims to only detect the stance given the target and text, our proposed task includes both target identification and stance de-tection, which makes it a more challenging task. we benchmark the task by proposing a two-stage framework that first identifies the target from a text and then detects the stance toward the predicted target. our two-stage framework can not only be applied to in-target stance detection but also zeroshot stance detection. in addition, we propose a multi-task approach that takes target prediction as an auxiliary task to improve the task performance of stance detection. it is worth noting that the primary goal of this paper is the introduction of new stance detection task. the proposed framework provides a good starting point and leaves much room for further improvements. future work includes improving the target identification task, e.g., with a better mapping strategy.