SlowGuess/NAACL_2022_OCR huggingface dataset:
Threaded Stance In the setting of conversational threads (Zubiaga et al., 2016b; Derczynski et al., 2017; Gorrell et al., 2019), 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 (Zubiaga et al., 2016a) or dictionary-based token scores (Aker et al., 2017). Kumar and Carley (2019) replaced CRFs with Binarised Constituency Tree LSTMs, and used pre-trained embeddings to encode the tweets. More recently, Tree (Ma and Gao, 2020) and Hierarchical (Yu et al., 2020) Transformers were proposed, which combine post- and thread-level representations for rumour debunking, improving previous results on RumourEval '17 (Yu et al., 2020). Kochkina et al. (2017, 2018) 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). Li et al. (2020) deviated from this structure and modelled the conversations as a graph. Tian et al. (2020) showed that pre-training on stance data yielded better representations for threaded tweets for downstream rumour detection. Yang et al. (2019) 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. Li et al. (2019a,b) 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 (RumourEval '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 (Gorrell et al., 2019).
A major challenge for all rumour detection datasets is the class distribution (Zubiaga et al., 2016b; Derczynski et al., 2017; Gorrell et al., 2019), e.g., the minority class denying is extremely hard for models to learn, as even for strong systems such as Kochkina et al. (2017) 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 (Gorrell et al., 2019; Yu et al., 2020).
Another factor, which goes hand in hand with the threaded structure, is the temporal dimension of posts in a thread (Lukasik et al., 2016; Veyseh et al., 2017; Dungs et al., 2018; Wei et al., 2019). In-depth data analysis (Zubiaga et al. (2016a,b); Kochkina et al. (2017); Wei et al. (2019); Ma and Gao (2020); Li et al. (2020); 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., Fang et al. (2019) achieved state-of-the-art results on FNC-1 by fine-tuning on multiple tasks such as question answering, natural language inference, etc., which are weakly related to stance. Recently, Schiller et al. (2021) proposed a benchmark to evaluate the robustness of stance detection models. They leveraged a pre-trained multi-task deep neural network, MT-DNN (Liu et al., 2019), and continued its training on all datasets simultaneously using multitask learning, showing sizeable improvements over models trained on individual datasets. Hardalov et al. (2021) 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 (Luo et al., 2002): feature-level (Guo et al., 2018; Wright and Augenstein, 2020) and decision-level (Ganin and Lempitsky, 2015). They further integrated label embeddings (Augenstein et al., 2018), and eventually developed an end-to-end unsupervised framework for predicting stance from a set of unseen target labels. Hardalov et al. (2022) explored PET (Schick and Schütze, 2021) in a cross-lingual setting, combining datasets with different label inventories by modelling the task as a cloze question answering one.
Paper	Dataset	Score	Metric
Hardalov et al. (2021)	Rumour Has It	71.2	F1macro
Kumar et al. (2019)	PHEME	53.2	F1macro
Hardalov et al. (2021)	Emergent	86.2	F1macro
Guderlei et al. (2020)	FNC-1	78.2	F1macro
Yu et al. (2020)	RumourEval '17	50.9	F1macro
Dominiks (2021)*	FEVER	76.8	FEVER
Wang et al. (2020)	Snopes	78.3	F1macro
Yang et al. (2019)	RumourEval '19	61.9	F1macro
Weinzierl et al. (2021)	COVIDLies	74.3	F1macro
Liu et al. (2020a)	TabFact	84.2	Accuracy
Alhindi et al. (2021)	Arabic FC	52.?	F1macro
Lillie et al. (2019)	DAST	42.1	F1macro
Bošnjak and Karan (2019)	Croatian	25.8	F1macro
Alhindi et al. (2021)	ANS	90.?	F1macro
Alhindi et al. (2021)	AraStance	78.?	F1macro
Table 2: State-of-the-art results on the stance detection datasets. Note that some papers round their results to integers, and thus we put ‘?’ for them. *Extracted from the FEVER leaderboard. $^{2}$
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.
State of the Art Table 2 shows the state-of-the-art (SOTA) results for each dataset discussed in Section 3 and Table 1. The datasets vary in their task formulation and composition in terms of size, number of classes, class imbalance, topics, evaluation measures, etc. Each of these factors impacts the performance, leading to sizable differences in the final score, as discussed in Section 4, and hence rendering the reported results hard to compare directly across these datasets.
5 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 (Bowman et al., 2015) 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 (Zubiaga et al., 2016b; Derczynski et al., 2017; Pomerleau and Rao, 2017; Baly et al., 2018b; Gorrell et al., 2019; Lillie et al., 2019; Alhindi et al., 2021).
<sup>1</sup>The result from dominiks can be found at https://competitions.codalab.org/competitions/18814#results
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 (Nie et al., 2019), weighting classes (Veyseh et al., 2017),<sup>3</sup> crafting artificial examples from auxiliary tasks (Yang et al., 2019; Hardalov et al., 2022), or training on multiple datasets (Schiller et al., 2021; Hardalov et al., 2021, 2022).
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 (Augenstein et al., 2018), 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 Hardalov et al. (2021), 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 small-size 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 Table 1), and they are small in size and do not offer a cross-lingual setup. Recently, Vamvas and Senrich (2020) proposed such a setup for three languages for stance in debates, Schick and Schütze (2021) explored few-shot learning, and Hardalov et al. (2022) extended that paradigm with sentiment and stance pre-training and evaluated on twelve languages from various domains. Since cultural norms and expressed linguistic phenomena play a crucial role in understanding the context of a claim (Sap et al., 2019), we do not argue for a completely
language-agnostic framework. Yet, empirically, training in cross-lingual setups improves performance by leveraging better representations learned on a similar language or by acting as a regulariser.
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 (Zubiaga et al., 2016b; Gorrell et al., 2019; Li et al., 2019b). 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 (Baly et al., 2019; Darwish et al., 2020; Stefanov et al., 2020; Baly et al., 2020). 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 (Nakamura et al., 2020), claim verification for images (Zlatkova et al., 2019), or searching for fact-checked information to alleviate the spread of fake news (Vo and Lee, 2020). There has been work on meme analysis for related tasks: detecting hateful (Kiela et al., 2020), harmful (Pramanick et al., 2021; Sharma et al., 2022a), and propaganda-mistic memes (Dimitrov et al., 2021a,b); see also a recent survey of harmful memes (Sharma et al., 2022b). 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 (Qazvinian et al., 2011; Zubiaga et al., 2016b; Derczynski et al., 2017; Hossain et al., 2020); see Section 5.
${}^{3}$  Weighting is not trivial for some setups, e.g.,threaded stance (Zubiaga et al., 2018b)
Shades of Truth The notion of shades of truth is important in mis- and disinformation detection. For example, fact-checking often goes beyond binary true/false labels, e.g., Nakov et al. (2018) used a third category half-true, Rashkin et al. (2017) included mixed and no factual evidence, and Wang (2017); Santia and Williams (2018) adopted an even finer-grained schema with six labels, including barely true and utterly false. We believe that such shades could be applied to stance and used in a larger pipeline. In fact, fine-grained labels are common for the related task of Sentiment Analysis (Pang and Lee, 2005; Rosenthal et al., 2017).
Label Semantics As research in stance detection has evolved, so has the definition of the task and the label inventories, but they still do not capture the strength of the expressed stance. As shown in Section 3 (also Appendix 2), labels can vary based on the use case and the setting they are used in. Most researchers have adopted a variant of the Favour, Against, and Neither labels, or an extended schema such as (S)upport, (Q)query, (D)eny, and (C)omment (Mohammad et al., 2016), but that is not enough to accurately assess stance. Moreover, adding label granularity can further improve the transfer between datasets, as the stance labels already share some semantic similarities, but there can be mismatches in the label definitions (Schiller et al., 2021; Hardalov et al., 2021, 2022).
Explainability The ability for a model to be able to explain its decisions is getting increasingly important, especially for mis- and disinformation detection, as one could argue that it is a crucial step towards adopting fully automated fact-checking. The FEVER 2.0 task formulation (Thorne et al., 2019) can be viewed as a step towards obtaining such explanations, e.g., there have been efforts to identify adversarial triggers that offer explanations for the vulnerabilities at the model level (Atanasova et al., 2020b). However, FEVER is artificially created and is limited to Wikipedia, which may not reflect real-world settings. To mitigate this, explanation by professional journalists can be found on fact-checking websites, and can be further combined with stance detection in an automated system. In a step in this direction, Atanasova et al. (2020a) generated natural language explanations for claims from PolitiFact<sup>4</sup> given gold evidence document summaries by journalists.
Moreover, partial explanations can be obtained automatically from the underlying models, e.g., from memory networks (Mohtarami et al., 2018), attention weights (Zhou et al., 2019; Liu et al., 2020b), or topic relations (Si et al., 2021). However, such approaches are limited as they can require gold snippets justifying the document's stance, attention weights can be misleading (Jain and Wallace, 2019), and topics might be noisy due to their unsupervised nature. Other existing systems (Popat et al., 2017, 2018; Nadeem et al., 2019) offer explanations to a more limited extent, highlighting span overlaps between the target text and the evidence documents. Overall, there is a need for holistic and realistic explanations of how a fact-checking model arrived at its prediction.
Integration People question false information more and tend to confirm true information (Mendoza et al., 2010). Thus, stance can play a vital role in verifying dubious content. In Appendix C, we discuss existing systems and real-world applications of stance for mis- and disinformation identification in more detail. However, we argue that a tighter integration between stance and fact-checking is needed. Stance can be expressed in different forms, e.g., tweets, news articles, user posts, sentences in Wikipedia, and Wiki tables, among others and can have different formulations as part of the fact-checking pipeline (see Section 3). All these can guide human fact-checkers through the process of fact-checking, and can point them to relevant evidence. Moreover, the wisdom of the crowd can be a powerful instrument in the fight against mis- and disinformation (Pennycook and Rand, 2019), but we should note that vocal minorities can derail public discourse (Scannell et al., 2021). Nevertheless, these risks can be mitigated by taking into account the credibility of the user or of the information source, which can be done automatically or with the help of human fact-checkers.
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.
<sup>4</sup>http://www.politifact.com/
We would like to thank the anonymous reviewers for their useful feedback. Isabelle Augenstein's research is partially funded by a DFF Sapere Aude research leader grant with grant number 0171-00034B. The work is also part of the Tanbih megaproject, which is developed at the Qatar Computing Research Institute, HBKU, and aims to limit the impact of "fake news," propaganda, and media bias by making users aware of what they are reading, thus promoting media literacy and critical thinking.
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