A Survey on Stance Detection for Mis- and Disinformation Identification (2022)

Abstract: Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a component of fact-checking, rumour detection, and detecting previously fact-checked claims, or (b) as a task in its own right. While there have been prior efforts to contrast stance detection with other related tasks such as argumentation mining and sentiment analysis, there is no existing survey on examining the relationship between stance detection and mis- and disinformation detection. Here, we aim to bridge this gap by reviewing and analysing existing work in this area, with mis- and disinformation in focus, and discussing lessons learnt and future challenges.

## introduction the past decade is characterized by a rapid growth in popularity of social media platforms such as facebook, twitter, reddit, and more recently, parler. this, in turn, has led to a flood of dubious content, especially during controversial events such as brexit and the us presidential election. more recently, with the emergence of the covid-19 pandemic, social media were at the center of the first global infodemic @xcite , thus raising yet another red flag and a reminder of the need for effective mis-and disinformation detection online. in this survey, we examine the relationship between automatically detecting false information online -including fact-checking, and detecting fake news, rumors, and hoaxes -and the core underlying natural language processing (nlp) task needed to achieve this, namely stance detection. therein, we consider mis-and disinformation, which both refer to false information, though disinformation has an additional intention to harm. detecting and aggregating the expressed stances towards a piece of information can be a powerful tool for a variety of tasks including understanding ideological debates @xcite , gathering different frames of a particular issue @xcite or determining the leanings of media outlets @xcite . the task of stance detection has been studied from different angles, e.g., in political debates @xcite , for fact-checking @xcite , or regarding new products @xcite . moreover, different types of text have been studied, including social media posts @xcite and news articles @xcite . finally, stances expressed by different actors have been considered, such as politicians @xcite , journalists @xcite , and users on the web @xcite . there are some recent surveys related to stance detection. @xcite discuss the role of stance in rumour verification, @xcite survey stance detection for social media, and @xcite survey stance detection holistically, without a specific focus on veracity. there are also surveys on fact-checking @xcite , which mention, though do not exhaustively survey, stance. however, there is no existing overview of the role that different formulations of stance detection play in the detection of false content. in that respect, stance detection could be modelled as factchecking -to gather the stances of users or texts towards a claim or a headline (and support factchecking or studying misinformation) -, or as a component of a system that uses stance as part of its process of judging the veracity of an input claim. here, we aim to bridge this gap by surveying the research on stance for mis-and disinformation detection, including task formulations, datasets, and methods, from which we draw conclusions and lessons, and we forecast future research trends. dataset source(s) target context evidence #instances task english datasets rumour has it @xcite topic tweet 10k rumours pheme @xcite claim tweet 4.5k rumours emergent @xcite ǌ headline article * 2.6k rumours fnc-1 @xcite ǌ headline article 75k fake news rumoureval '17 @xcite implicit foot_0 tweet 7.1k rumours fever @xcite ɀ claim facts 185k fact-checking snopes @xcite snopes claim snippets 19.5k fact-checking rumoureval '19 @xcite implicit 1 post 8.5k rumours covidlies @xcite claim tweet 6.8k misconceptions tabfact @xcite ɀ statement wiki in order to understand the task of stance detection, we first provide definitions of stance and the stance-taking process. @xcite define stance as the expression of a speaker's standpoint and judgement towards a given proposition. further, du bois (2007)) define stance as "a public act by a social actor, achieved dialogically through overt communicative means, of simultaneously evaluating objects, positioning subjects (self and others), and aligning with other subjects, with respect to any salient dimension of the sociocultural field", showing that the stance-taking process is affected not only by personal opinions, but also by other external factors such as cultural norms, roles in the institution of the family, etc. here, we adopt the general definition of stance detection by @xcite : "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. occasionally, the category label of neutral is also added to the set of stance categories @xcite , and the target may or may not be explicitly mentioned in the text" @xcite . note that the stance detection definitions and the label inventories vary somewhat, depending on the target application (see section 3). finally, stance detection can be distinguished from several other closely related nlp tasks: (i) biased language detection, where the existence of an inclination or tendency towards a particular perspective within a text is explored, (ii) emotion recognition, where the goal is to recognise emotions such as love, anger, etc. in the text, (iii) perspective identification, which aims to find the pointof-view of the author (e.g., democrat vs. republican) and the target is always explicit, (iv) sarcasm detection, where the interest is in satirical or ironic pieces of text, often written with the intent of ridicule or mockery, and (v) sentiment analysis, which checks the polarity of a piece of text. ## stance and factuality here, we offer an overview of the settings for misand disinformation identification to which stance detection has been successfully applied. as shown in figure 1 , stance can be used (a) as a way to perform fact-checking, or more typically, (b) as a component of a fact-checking pipeline. ## approaches in this section, we discuss various ways to use stance detection for mis-and disinformation detection, and list the state-of-the-art results in fact-checking as stance detection here, we discuss approaches for stance detection in the context of mis-and disinformation detection, where veracity is modelled as stance detection as outlined in section 3.1. one such line of research is the fake news challenge, which used weighted accuracy as an evaluation measure (fnc score), to mitigate the impact of class imbalance. subsequently, @xcite criticized the fnc score and f1-micro, and argued in favour of f1-macro (f1) instead. in the competition, most teams used hand-crafted features such as words, word embeddings, and sentiment lexica @xcite . @xcite showed that the most important group of features were the lexical ones, followed by features from topic models, while sentiment analysis did not help. @xcite investigated the importance of lexical cues, and found that report and negation are most beneficial, while knowledge and denial are least useful. all these models struggle to learn the disagree class, achieving up to 18 f1 due to major class imbalance. in contrast, unrelated is detected almost perfectly by all models (over 99 f1). @xcite showed that these models exploit the lexical overlap between the headline and the document, but fail when there is a need to model semantic relations or complex negation, or to understand propositional content in general. this can be attributed to the use of n-grams, topic models, and lexica. @xcite investigated memory networks, aiming to mitigate the impact of irrelevant and noisy information by learning a similarity matrix and a stance filtering component, and taking a step towards explaining the stance of a given claim by extracting meaningful snippets from evidence documents. like previous work, their model performs poorly on the agree/disagree classes, due to the unsupervised way of training the memory networks, i.e., there are no gold snippets justifying the document's stance w.r.t. the target claim. more recently, transfer learning with pre-trained transformers has been explored @xcite , significantly improving the performance of previous state-of-the-art approaches. guderlei and aßenmacher (2020) showed the most important hyper-parameter to be learning rate, while freezing layers did not help. in particular, using the pre-trained transformer roberta improved f1 from 18 to 58 for disagree, and from 50 to 70 for agree. the success of these models is also seen in cross-lingual settings. for arabic, khouja (2020) achieved 76.7 f1 for stance detection on the ans dataset using mbert. similarly, @xcite applied pattern-exploiting training (pet) with sentiment pre-training in a cross-lingual setting showing sizeable improvements on 15 datasets. @xcite showed that language-specific pre-training was pivotal, outperforming the state of the art on arastance (52 f1) and arabic fc (78 f1). some formulations include an extra step for evidence retrieval, e.g., retrieving wikipedia snippets for fever @xcite . to evaluate the whole fact-checking pipeline, they introduced the fever score -the proportion of claims for which both correct evidence is returned and a correct label is predicted. the top systems that participated in the fever competition 2019 ) proposed a neural semantic matching network, which ranked first in the competition, achieving 64.2 fever score. they used page view frequency and wordnet features in addition to pre-trained contextualized embeddings @xcite . more recent approaches used bi-directional attention @xcite , a gpt language model @xcite , and graph neural networks @xcite @xcite @xcite @xcite . @xcite showed that adding graph networks on top of bert can improve performance, reaching 67.1 fever score. yet, the retrieval model is also important, e.g., using the gold evidence set adds 1.4 points. @xcite al. (2020b); zhong et al. (2020 ) replaced the retrieval model with a bert-based one, in addition to using an improved mechanism to propagate the information between nodes in the graph, boosting the score to 70. recently, @xcite experimented with a retriever that incorporates co-reference in distantsupervised pre-training, namely, corefroberta. wang et al. (2020) added external knowledge to build a contextualized semantic graph, setting a new sota on snopes. si et al. (2021) and ostrowski et al. (2021) improved multi-hop reasoning using a model with extra hop attention (zhao et al., 2020), a capsule network aggregation layer, and lda topic information. atanasova et al. (2022) introduced the task of evidence sufficiency prediction to more reliably predict the not enough info class. another notable idea is to use pre-trained language models as fact-checkers based on a masked language modelling objective @xcite , or to use the perplexity of the entire claim with respect to the target document @xcite . such models do not require a retrieval step, as they use the knowledge stored in language models. however, they are prone to biases in the patterns used, e.g., they can predict date instead of city/country and vice-versa when using "born in/on". moreover, the insufficient context can seriously confuse them, e.g., for short claims with uncommon words such as "sarawak is a ...", where it is hard to detect the entity type. finally, the performance of such models remains well below supervised approaches; even though recent work shows that few-shot training can improve results @xcite . error analysis suggests that the main challenges are (i) confusing semantics at the sentence level, e.g., "andrea pirlo is an american professional footballer." vs. "andrea pirlo is an italian professional footballer who plays for an american club.", (ii) sensitivity to spelling errors, (iii) lack of relation between the article and the entities in the claim, (vi) dependence on syntactic overlaps, e.g., "terry crews played on the los angeles chargers." (note-noughinfo) is classified as refuted, given the sentence "in football, crews played ... for the los angeles rams, san diego chargers and washington redskins, ...", (v) embedding-level confusion, e.g., numbers tend to have similar embeddings, "the heart beats at a resting rate close to 22 bpm." is not classified as refuted based on the evidence sentence "the heart beats at a resting rate close to 72 bpm.", and similarly for months. threaded stance in the setting of conversational threads @xcite @xcite , in contrast to the single-task setup, which ignores or does not provide further context, important knowledge can be gained from the structure of user interactions. these approaches are mostly applied as part of a larger system, e.g., for detecting and debunking rumours (see section 3.2, rumours). a common pattern is to use tree-like structured models, fed with lexicon-based content formatting @xcite or dictionary-based token scores @xcite . @xcite replaced crfs with binarised constituency tree lstms, and used pre-trained embeddings to encode the tweets. more recently, tree @xcite and hierarchical @xcite transformers were proposed, which combine post-and threadlevel representations for rumour debunking, improving previous results on rumoureval '17 @xcite . @xcite split conversations into branches, modelling each branch with branched-lstm and hand-crafted features, outperforming other systems at rumoureval '17 on stance detection (43.4 f1). @xcite deviated from this structure and modelled the conversations as a graph. @xcite showed that pre-training on stance data yielded better representations for threaded tweets for downstream rumour detection. @xcite took a step further and curated per-class pre-training data by adapting examples, not only from stance datasets, but also from tasks such as question answering, achieving the highest f1 (57.9) on the rumoureval '19 stance detection task. @xcite additionally incorporated user credibility information, conversation structure, and other content-related features to predict the rumour veracity, ranking 3rd on stance detection and 1st on veracity classification (ru-moureval '19). finally, the stance of a post might not be expressed directly towards the root of the thread, thus the preceding posts must be also taken into account @xcite . a major challenge for all rumour detection datasets is the class distribution @xcite @xcite , e.g., the minority class denying is extremely hard for models to learn, as even for strong systems such as @xcite the f1 for it is 0. label semantics also appears to play a role as the querying label has a similar distribution, but much higher f1. yet another factor is thread depth, as performance drops significant at higher depth, especially for the supporting class. on the positive side, using multitask learning and incorporating stance detection labels into veracity detection yields a huge boost in performance @xcite . another factor, which goes hand in hand with the threaded structure, is the temporal dimension of posts in a thread @xcite @xcite . in-depth data analysis @xcite ; @xcite ; @xcite ; ma and gao (2020); @xcite ; among others) shows interesting patterns along the temporal dimension: (i) source tweets (at zero depth) usually support the rumour and models often learn to detect that, (ii) it takes time for denying tweets to emerge, afterwards for false rumors their number increases quite substantially, (iii) the proportion of querying tweets towards unverified rumors also shows an upward trend over time, but their overall number decreases. multi-dataset learning (mdl) mixing data from different domains and sources can improve robustness. however, setups that combine mis-and disinformation identification with stance detection, outlined in section 3, vary in their annotation and labelling schemes, which poses many challenges. earlier approaches focused on pre-training models on multiple tasks, e.g., @xcite achieved state-of-the-art results on fnc-1 by finetuning on multiple tasks such as question answering, natural language inference, etc., which are weakly related to stance. recently, @xcite proposed a benchmark to evaluate the robustness of stance detection models. they leveraged a pre-trained multi-task deep neural network, mt-dnn @xcite , and continued its training on all datasets simultaneously using multitask learning, showing sizeable improvements over models trained on individual datasets. @xcite experimented with cross-domain learning from 16 stance detection datasets. they proposed a novel architecture (mole) that applies domain adaptation at different stages of the modelling process @xcite : feature-level @xcite ) and decision-level @xcite . they further integrated label embeddings @xcite , and eventually developed an end-toend unsupervised framework for predicting stance from a set of unseen target labels. @xcite explored pet @xcite in a cross-lingual setting, combining datasets with different label inventories by modelling the task as a cloze question answering one. they showed that mdl helps for low-resource and substantively for full-resource scenarios. moreover, transferring knowledge from english stance datasets and noisily generated sentiment-based stance data can further boost performance. ## lessons learned and future trends dataset size a major limitation holding back the performance of machine learning for stance detection is the size of the existing stance datasets, the vast majority of which contain at most a few thousand examples. contrasted with the related task of natural language inference, where datasets such as snli @xcite of more than half a million samples have been collected, this is far from optimal. moreover, the small dataset sizes are often accompanied with skewed class distribution with very few examples from the minority classes, including many of the datasets in this study @xcite @xcite @xcite @xcite . this can lead to a significant disparity for label performance (see section 4). several techniques have been proposed to mitigate this, such as sampling strategies @xcite , weighting classes (veyseh et al., 2017), foot_3 crafting artificial examples from auxiliary tasks @xcite , or training on multiple datasets @xcite @xcite . data mixing a potential way of overcoming limitations in terms of dataset size and focus is to combine multiple datasets. yet, as we previously discussed (see section 3), task definitions and label inventories vary across stance datasets. further, large-scale studies of approaches that leverage the relationships between label inventories, or the similarity between datasets are still largely lacking. one promising direction is the use of label embeddings @xcite , as they offer a convenient way to learn interactions between disjoint label sets that carry semantic relations. one such first study was recently presented by @xcite , which explored different strategies for leveraging inter-dataset signals and label interactions in both in-(seen targets) and out-of-domain (unseen targets) settings. this could help to overcome challenges faced by models trained on smallsize datasets, and even for smaller minority classes. multilinguality multi-linguality is important for several reasons: (i) the content may originate in various languages, (ii) the evidence or the stance may not be expressed in the same language, thus (iii) posing a challenge for fact-checkers, who might not be speakers of the language the claim was originally made in, and (iv) it adds more data that can be leveraged for modelling stance. currently, only a handful of datasets for factuality and stance cover languages other than english (see modelling the context modelling the context is a particularly important, yet challenging task. in many cases, there is a need to consider the background of the stance-taker as well as the characteristics of the targeted object. in particular, in the context of social media, one can provide information about the users such as their previous activity, other users they interact most with, the threads they participate in, or even their interests @xcite @xcite . the context of the stance expressed in news articles is related to the features of the media outlets, such as source of funding, previously known biases, or credibility @xcite @xcite . when using contextual information about the object, factual information about the real world, and the time of posting are all important. incorporating these into a stance detection pipeline, while challenging, paves the way towards a robust detection process. multimodal content spreading mis-and disinformation through multiple modalities is becoming increasingly popular. one such example are deepfakes, i.e., synthetically created images or videos, in which (usually) the face of one person is replaced with another person's face. another example are information propagation techniques such as memetic warfare. hence, it is increasingly important to combine different modalities to understand the full context stance is being expressed in. some work in this area is on fake news detection for images @xcite , claim verification for images @xcite , or searching for fact-checked information to alleviate the spread of fake news @xcite . there has been work on meme analysis for related tasks: detecting hateful @xcite , harmful @xcite , and propagandistic memes @xcite ; see also a recent survey of harmful memes @xcite . this line of research is especially relevant for mis-and disinformation tasks that depend on the wisdom of the crowd in social media as it adds additional information sources @xcite @xcite ; see section 5. ## conclusion we surveyed the current state-of-the-art in stance detection for mis-and disinformation detection. we explored applications of stance for detecting fake news, verifying rumours, identifying misconceptions, and fact-checking. we also discussed existing approaches used in different aspects of the aforementioned tasks, and we outlined several interesting phenomena, which we summarised as lessons learned and promising future trends.