Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies

The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide. To contain the spread of virus and further alleviate the crisis, various health policies (e.g., stay-at-home orders) have been issued which spark heat discussion as users turn to share their attitudes on social media. In this paper, we consider a more realistic scenario on stance detection (i.e., cross-target and zero-shot settings) for the pandemic and propose an adversarial learning-based stance classifier to automatically identify the public attitudes toward COVID-19-related health policies. Specifically, we adopt adversarial learning which allows the model to train on a large amount of labeled data and capture transferable knowledge from source topics, so as to enable generalize to the emerging health policy with sparse labeled data. Meanwhile, a GeoEncoder is designed which encourages model to learn unobserved contextual factors specified by each region and represents them as non-text information to enhance model's deeper understanding. We evaluate the performance of a broad range of baselines in stance detection task for COVID-19-related policies, and experimental results show that our proposed method achieves state-of-the-art performance in both cross-target and zero-shot settings.


I. INTRODUCTION
The coronavirus disease 2019 (COVID-19) pandemic has brought serious challenges to human health, society, and the economy. 1 To curb the spread of virus and alleviate the crisis, policymakers and public authorities have imposed corresponding health policies (e.g., stay-at-home order, vaccination) [1]- [4]. The promulgation of these health mandates has sparked discussions on the Internet, but the public's attitudes towards them vary. Stance detection is of great practical values as an effective tool for Internet public opinion monitoring, which detects the attitude (i.e., in favor of, against, or neutral) of an opinionated text toward a pre-defined topic automatically [5]- [8], as shown in Table I. Applying stance detection for health policies plays a crucial role in mitigating social unrest and providing guidance to policymakers for addressing other pandemics or even further waves of the current one [9]- [12]. However, stance detection for COVID-19-related health policies is faced with several challenges: (1) Stance detection for emerging policies. As the pandemic develops, policymakers will introduce new policies based on the dynamics of the epidemic situation in response The examples of stance classification task. Given a tweet and the involved topic, the stance classifier is capable of detecting the stance label automatically.
Example 1: Only if we all do our part and wear a mask, distance and stay at home if possible. Topic: Stay at Home Order Stance label: Favor Example 2: There is no way in hell I would put a vaccine into my body that comes out under or is advocated by the trump regime. I trust COVID-19 more than I trust the trump regime.

Topic: Vaccination
Stance label: Against to complex virus challenges. For emerging health policies, the available labeled data is limited, and it is also infeasible to annotate a large amount of data in a short period of time. Existing studies adopt in-target 2 setting for tracking the public's stances [9], [11], [12], that is, the training and testing phase are under the same health policy. However, they usually require adequate labeled data to achieve decent performance, which limits their application range. Therefore, it is imperative to propose a more robust stance classifier to employ in emerging health policies. The alternative approaches are to conduct cross-target or zeroshot settings. Cross-target stance detection trains on one target and tests on a related target in a one-to-one way, while zeroshot stance detection aims to train on multiple targets with labeled data and evaluate on an unseen target. Both crosstarget and zero-shot settings are more challenging principally because the language models may not be compatible between different targets [8], [13]. Since COVID-19-related health mandates are inherently correlated, it is possible to apply cross-target or zero-shot stance detection which can provide more practical insights for administrative decision-making.
(2) Unseen contextual factors. The discussion of health policies involves a game between politics and science [14] (e.g., Example 2 shown in Table I). When people express their attitudes toward health policies online, the heterogeneous response across different regions is influenced by unobserved contextual factors, such as cultural background, political ideologies, and regional epidemic situation (i.e., epidemiological context). However, these background factors are hard to be quantified. Existing stance detection methods rely on the text only, which has not been perfect for inferring the public's attitudes toward health policies due to the ignoring of unseen contextual signals [15]. Geographic information is a kind of native proximity signals [4], [16], [17], which can reflect the homogeneity of groups and unobserved contextual factors, for example, the government in a region was not effectively implementing health interventions while led to an outbreak of the epidemic, which would result in negative mindset and resistance of residents to the health policies (i.e., the pandemic fatigue). Using geographic signals to characterize contextual factors, which can provide context-specific information for model learning, and should be a feasible yet challenging way to enhance model's understanding and the performance of stance detection on health policies.
To tackle the aforementioned challenges, in this paper, we propose an adversarial learning-based stance classifier for helping detect the public's attitudes toward COVID-19-related health policies. The domain adaptation technique is one of the best solutions for solving cross-target and zero-shot settings [6], [7], where domain-invariant information is responsible for ensuring the transferability across different domains. We model topic transfer as domain adaptation and treat each health policy as a domain. Specifically, we embed a text and a topic jointly applying Bidirectional Encoder Representations from Transformers (BERT) [18], and then we employ a feature separation module to extract and distinguish topic-specific and topic-invariant information. Moreover, a GeoEncoder is devised which encodes geographic signals as non-text features to learn unobserved contextual factors and then improve model's understanding. Following the success of adversarial learning for domain adaptation [19], [20], we integrate a topic discriminator [7] to the model for adversarial training to better capture topic-invariant information, so that enhancing the transferability of applying to the emerging health policies. Our contributions are summarized below: • We propose an adversarial learning-based stance classifier for helping detect the public's attitudes toward COVID-19-related health policies automatically, which can be applied to emerging policies with no labeled data. • We devise a GeoEncoder by characterizing geographic signals to capture regional contextual factors for model learning. The learned features will be fed into the stance classifier as non-text information to enhance model's understanding and detection performance. • We evaluate the performances of a broad range of baselines in stance detection task for COVID-19-related health policies. Experimental results show that our proposed method achieves state-of-the-art performance in both cross-target and zero-shot settings. The remainder of this paper is organized as follows. We review related works in Section II. Section III introduces the formulation of this research problem and the details of our proposed model. Then, we present the experimental setups in Section IV and discuss main results in Section V. At last, we conclude in Section VI.

II. RELATED WORKS A. Stance Detection
Stance detection, which aims to classify the attitude of a text toward a topic, has received considerable attention in recent years. Previous works mainly focus on the in-target setting [5], [21], [22], that is, both training and testing phase are under the same topic. However, when faced with an emerging topic, the problem of sparse labeled data becomes serious. As such, there is an urgent need to design a cross-target or zero-shot stance classifier for targets with few or no labeled data. Studying cross-target and zero-shot settings for unseen targets goes beyond in-target research and can contribute to a more flexible framework for stance detection. Cross-target stance detection is training on one target and evaluating on one different unseen topic that has a known relationship with the training topic. Xu et al. [13] proposed CrossNet which utilized BiLSTM as the encoder and designed an attention mechanism to learn the importance of each word. Liang et al. [23] generated two heterogeneous graphs for interactively capturing the linguistic roles (i.e., target-independent and target-dependent) of words behind a text. Unlike cross-target setting, zero-shot stance detection does not assume a correlation between training and test topics, using multiple topics with adequate labeled data for training and transferring to an unseen topic for inference. Allaway and McKeown [24] developed TGANet which applied hierarchical clustering to construct generalized topic representation and introduced an attention mechanism to capture potential associations between topics for zero-shot stance detection. Liang et al. [8] employed contrastive learning to better generalize stance features for unseen targets, thereby improving the performance of zero-shot stance detection.

B. Adversarial Domain Adaptation
Domain adaptation is an effective way to deal with sparse labeled data in emerging fields, which utilizes the sufficient label information of source domains to compensate for the sparse label information in the target domain. The ability of domain adaptation methods depends on the transferability and discriminability [25]. Transferability ensures that knowledge can be efficiently transferred from the source domain to the target domain, while discriminability requires low generalization errors when transferring to the target domain. Inspired by generative adversarial networks (GANs) [26], adversarial manner has been successfully explored in domain adaptation. In Domain Adversarial Neural Network (DANN) [19], the authors regarded the domain adaptation as a game between transferability (domain classifier) and discriminability (label predictor), and proposed a gradient reversal layer to confuse the domain classifier and make the feature extractor can capture domain-invariant information. Many subsequent methods, such as ADDA [27], GVB [20], built adversarial frameworks to minimize domain discrepancy. Domain adaptation is one of the best solutions for cross-target or zero-shot stance detection. Wei and Mao [6] firstly applied domain adaptation to stance detection and demonstrated its effectiveness in cross-target setting. Allaway et al. [7] modeled zero-shot topic transfer as adversarial domain adaptation and treated each topic as a domain, which is a more robust and efficient approach without the assumption of known relationships between topics.

C. Applications in COVID-19 Pandemic
The COVID-19 pandemic has spread to more than 200 countries and territories, causing immeasurable human losses and economic burdens. Responding to this crisis that threatens public health and serving the global community, many researchers are putting their knowledge to devise effective ways to counter COVID-19, involving time series analysis, computer vision, natural language processing, and other fields. Epidemic forecasting plays an essential role in pandemic surveillance [28], [29]. Accurate epidemic prediction can provide an opportunity for timely administrative decision-making and healthcare resource scheduling. Computer Vision has successfully solved various complex problems in healthcare in recent years and contributed to the fight against COVID-19, such as the screening and diagnosis of COVID-19 virus [30], masked face recognition [31]. The ongoing pandemic has also aroused heated discussion on the Internet, and a flood of pandemic-related user-generated content (UGC) has created on social media, containing the public's attitudes [10], rumors [32], sentiments [33], and misinformation [34]. In response to the infodemic, natural language processing is the key to effectively handling high volumes of COVID-19-related texts and monitoring social change.

A. Problem Formulation
Given a set of labeled texts from source targets , and a set of unlabeled texts from a destination target (i.e., unseen target) where t is the target in the text x, and y is the corresponding stance label.
We also have a large set of unlabeled texts from both targets where each sample belongs to the source targets or the destination target. Cross-target stance detection task is using one source target to predict a stance label (i.e., "favor", "against", or "none") of texts in D d , while zero-shot setting is utilizing all source targets. Note that, for each sample in D s or D d , it is associated with a Geographic Identifier (GeoID) indicating the user's location. Fig. 1 presents an overview of our proposed model and in the following sections, we will introduce the building modules in detail.

B. Encoder Module
Bidirectional Encoder Representations from Transformers (BERT) [18] has shown tremendous success in various Natural Language Processing (NLP) tasks, and there are derived many BERT-based models, such as RoBERTa [35], ALBERT [36]. BERT is pre-trained in a completely unsupervised fashion, i.e. masked language modeling (MLM) and next sentence prediction (NSP), which can capture deep semantic contextual information as well as high-level linguistic coherence [37]. The pre-trained BERT can be fine-tuned then as an encoder for diverse downstream tasks. To take full advantage of contextual information, we jointly condition and embed topic and text using BERT. Nevertheless, the original topic words are too short, which results in limited information available for classifier. Since the proposal of a health policy is accompanied by the corresponding description, and people's attitudes are based on the contents and even details of policies. Thus, instead of using the topic words directly, we leverage external knowledge about the topic (i.e., policy description) to enhance the model's understanding of the health policy and tokenize the text x and the policy-related description p as the input sentence pair: where, [CLS] is a special symbol that be used as the aggregate representation for the overall semantic context, while [SEP ] is the special separator token. We use the final hidden state of [CLS] as the contextual conditional encoding for subsequent modeling.

C. Feature Separation Module
The contextual representation generated by BERT contains both topic-specific and topic-invariant information. To allow the model to generalize to unseen topics, it is effective to learn and utilize transferable topic knowledge (i.e., topicinvariant information). Inspired by [20], we employ a simple linear transformation to separate and distinguish topic-specific and topic-invariant information, which can reduce the transfer difficulty without removing stance cues. First, we employ a linear layer to extract topic-specific features: where W f s and b f s are weight parameters. By removing target-specific feature from the h, results in the constructed target-invariant representation: The target-invariant features will enable the transferability of the model and serve as the input to the stance classifier and topic discriminator.

D. GeoEncoder Module
As mentioned in the Introduction section, the geographical signal is the native proximity information [17], which can reflect potential characteristics of groups, such as regional cultural background, political ideologies, epidemiological context, etc. Here, we propose GeoEncoder which utilizes geographic signals to learn contextual factors. Given the GeoId of a text, an intuitive design is applying an embedding layer, that is, each region is associated with a randomly initialized vector, which is updated individually during training phase. We use the learnable embeddings to capture the unobserved contextual factors of each region: where E ∈ R N ×F are learnable embedding vectors specified by the GeoId. N represents the number of total regions, F is the hidden dimension of f geo . This simple solution can only learn regional contexts individually. However, the sampled training data may be imbalanced which will result in insufficient training of GeoEncoder, for example, if a region in test set does not appear in training set, it will use a randomized vector as f geo , which will introduce noise then undermine the performance (i.e., no training samples exist for this region). Moreover, graph structures (e.g., geographical topology) describe connectivity among nodes which grants us to explore hidden relationships between nodes and also can reflect some unobserved contextual factors. Intuitively, geographically adjacent regions are more likely to have similar contextual factors because they tend to have the similar cultural background, topographic characteristics, or climatic features, and also have more frequent communications. Therefore, we further use the adjacent matrix as another kind of proximity information to facilitate making full training and capturing hidden contextual factors. Given an adjacent matrix A 3 , inspired by Graph Convolution Networks (GCNs) [38], we apply the following equation to investigate underlying interactions and hidden contextual factors: where W (l) is a layer-specific weight matrix, and E (l) is embeddings matrix at l th layer, with E (0) = E. σ(·) is the activation function (e.g., Rectified Linear Units (ReLU)).

E. Stance Classifier
We apply a linear layer with softmax as the stance classifier to predict the stance label of each text. Since stance is inherently dependent on a topic [7], meanwhile, the topicspecific representation extracted by Eq. 2 that encodes the individual characteristics of each topic is also informative [39]. Hence, we combine topic-invariant, topic-specific, and f geo 3 By default, each region is adjacent to itself. representations as the final latent features for joint modeling. We use the Cross Entropy loss as loss function: L sc = x∈Ds CrossEntropy(y sc ,ŷ sc ), where y sc is the ground truth stance label, W sc and b sc are parameters of stance classifier, and ⊕ represents the concatenation operation.

F. Topic Discriminator
To further ensure the topic-invariant representation can distill more transferable topics knowledge to facilitate model adaptation across different topics, inspired by [6], [7], we use a linear network with softmax as a topic discriminator to classify the corresponding topic label based on topic-invariant features. The topic discriminator is also trained by minimizing the Cross Entropy loss:ŷ where y td is the ground truth topic label, and W td and b td are parameters of topic discriminator. The training process of discriminator and topic-invariant feature f i is adversarial. Specifically, in order to learn the topic-invariance on both the source domain and target domain, we expect that f i can be generic enough to confuse topic discriminator, that is, the discriminator cannot correctly classify the topic through f i . Therefore, target-invariant features aim to maximize L td . In contrast, for topic discriminator, which makes efforts to minimize L td . Inspired by [19], we adopt the gradient reversal layer (GRL) in our model, which is a widely used technique in transfer learning-based methods. The adversarial training process is essentially a minmax game: where Θ M including fine-tuned BERT, GeoEncoder, W f s , b f s , W sc , and b sc , and α is a trade-off parameter. During the forward propagation, GRL acts as an identity transform. During the backpropagation though, GRL takes the gradient from the subsequent level, multiplies it by −λ, and passes it to the preceding layer for more stable update [19].

Topic Policy Description
Stay at Home Under a stay-at-home order, all non-essential workers must stay home. People can leave their homes only for essential needs like grocery stores and medicine, or for solo outdoor exercise.

Wear Masks
Masks are a key measure to reduce transmission and save lives. Wearing well-fitted masks should be used as part of a comprehensive Do it all! approach including maintaining physical distancing, avoiding crowded, closed and close-contact settings, ensuring good ventilation of indoor spaces, cleaning hands regularly, and covering sneezes and coughs with a tissue of bent elbow.

Vaccination
Getting vaccinated could save your life. COVID-19 vaccines provide strong protection against serious illness, hospitalization and death. There is also some evidence that being vaccinated will make it less likely that you will pass the virus on to others, which means your decision to get the vaccine also protects those around you.

A. Data Preparation
Previous studies [10], [40] have constructed COVID-19related stance datasets, where tweets are manually annotated as three stance labels: favor, against, and none (i.e., neutral). In this work, we adopt three health policies: Stay at Home Order, Wear masks, and Vaccination, and only select labeled tweets posted in the USA. Meanwhile, we also collect unlabeled tweets for these three health policies via Twitter API 4 . The statistics of prepared data are summarized in Table II. In addition, each labeled text is associated with a GeoId which indicates user's location. We use the "location" field provided by Twitter API to collect the shared place attachment of tweets. There are 52 GeoIds in total, including 50 states, 1 capital city (i.e., Washington, D.C.), and a general identifier: "USA". In geographic adjacent matrix A, the node with "USA" label is an individual node without connecting with any other nodes. We collect policy descriptions as external knowledge from World Health Organization (WHO) 5 , as shown in Table III.

B. Competitors
We select the following methods in the literature as comparison baselines: Network takes text as input and utilizes the hidden states from both directions to predict the stance label, without explicitly using the target information. • BiCond [21] This model uses both text and target information to jointly generate contextual conditional encoding for detecting stance expression. • TextCNN-C [42] The extended TextCNN to the crosstarget setting, in which a self-attention is adopted to capture the importance of each convolution filter. Wikipedia into stance encoding, which aims to enhance BERT's language understanding. We adopt WS-BERT-S and WS-BERT-D which encode Wikipedia knowledge in the single manner and dual manner respectively. To verify the effectiveness of each component, we introduce the following variants for conducting ablation studies: • w/o D is the removal of the topic discriminator, which means the model without an adversary. • w/o GeoEnc indicates that the model without GeoEncoder and thus does not infuse any geographic signals. • w/ GeoEmb applies an Embedding layer to generate and train f geo intuitively (i.e., Eq 4). • w/o EK means instead of using policy description p in Eq 1 as external knowledge, we use topic word t directly.

C. Metrics
Following previous works [5], [7], [43], [44], we leverage the average F1-score (denoted as F avg ) on three labels as the evaluation metric, which is formulated as: Due to the imbalanced class distributions, following [13], [44], we also compute both micro-average and macro-average F1-scores, and adopt their average (denoted as F m ) as evaluation metric:

D. Implementation Details
All programs are implemented using Python 3.6.13 and PyTorch 1.10.2 with CUDA 11.3 on an NVIDIA GeForce RTX 3090 GPU. The reported results are the averaged score of 5 runs with different random initialization. To facilitate the research community, we release datasets and the implementation of all models: https://github.com/Xiefeng69/ stance-detection-for-covid19-related-health-policies.
1) Training setting: In cross-target setting, the models are trained and validated on one topic and evaluated on another. It can be categorized into six source→destination tasks for cross-target evaluation: SH→WM, SH→VA, WM→SH, WM→VA, GV→SH, and VA→WM. In zero-shot setting, the models are trained and validated on multiple topics and tested on one unseen topic. We use unseen topic's name as task's name, thus, the zero-shot evaluation can be set into: SH, WM, and VA. For all tasks, the batch size is set to 16, the dropout rate is set to 0.1, and the input texts are truncated or padded to IV: Performance comparison for cross-target and zero-shot stance detection measured by F avg and F m . Bold face indicates the best result of each column and underlined the second-best. Relative gain is compared with the second best result. a maximum of 100 tokens. We train all models using AdamW [45] optimizer with weight decay 5e-5 for a maximum of 100 epochs with patience of 10 epochs, and the learning rate is chosen in {1e-5,2e-5}.
2) Models configuration: For BiLSTM, BiCond, TAN, CrossNet, TextCNN-C, the word embeddings are initialized with the pre-trained word vectors from GloVe [46] 6 , and the hidden dimension is optimized in {128,256}. For BERT, we fine-tune the pre-trained language model from the Hugging Face Transformer Library 7 to predict the stance by appending a linear classification layer to the hidden representation of the [CLS] token. In terms of WS-BERT-S and WS-BERT-D, considering the computational resource and fair comparison, the maximum length of Wikipedia summaries is set to 100 tokens and we use the pre-trained uncased BERT-base [18] as encoder, in which each word is mapped to a 768-dimensional embedding. To speed up the training process, we only finetune the top layers of the Wikipedia encoder in WS-BERT-D, which is consistent with [43]. In our model, we also adopt the pretrained uncased BERT-base as encoder. The maximum length of policy description is fixed at 50. The trade-off parameter α is set to 0.01, and the GRL's parameter λ is set to 0.1. The hidden dimension of GeoEncoder module is optimized in {128,256} and the layer number l of GCN is chosen in {1,2}.

A. Performance Comparison
We evaluate all models both in cross-target and zero-shot settings. Table IV reports the results measured using F avg and F m . There is an overall phenomenon that the accuracy in zero-shot setting is better than cross-target setting. This is because zero-shot stance detection leverages a broader source of supervision, which allows models have better semantic understanding across different topics. Our proposed method outperforms comparison baselines on most tasks and improves the average F avg and F m by 3.5% and 2.7% respectively. 6  We observe that BiLSTM, BiCond, and TextCNN-C overall perform worst mainly because they do not explicitly use topic information or have limited perception for unknown destination topic, which affirms the importance of designing a topic-aware approach for cross-target and zero-shot settings. For TAN and CrossNet, by designing an attention mechanism in the network to notice the importance of each word and reflect users' concerns, they can provide interpretable evidence to enable semantic understanding across different topics. BERT is still a comprehensive baseline, even though it ignores transferable knowledge between topics, but has strong generalization ability because it learns from a large-scale unsupervised corpus. Therefore, BERT exploits rich semantic information to perform relatively good performance in both scenarios. WS-BERT is a powerful stance classifier that leverages external knowledge as a bridge to enable model's deeper understanding and then precisely captures the stance towards a topic. However, since WS-BERT does not make constraints for learning transferable knowledge between topics on training phase, it will prone to fitting to a specific topic, which results in bringing performance drops when topic transfer. Compared with the above baselines, our proposed model takes topic information, background knowledge (i.e., policy description), and non-text information (i.e., geographic signals) into account to improve model's discriminability. Meanwhile, adversarial learning is applied as a constraint to learn topic-invariance to facilitate enhancing model's transferability.

B. Ablation Study
We conduct ablation studies with variants to clearly show the effectiveness of each key component, and the results are shown in Fig. 2. Overall, the complete model achieves optimal performance in most tasks. w/o D performs worse than other variants on multiple tasks, which demonstrates the effectiveness and significance of adversarial learning applied to cross-target or zero-shot settings. Applying adversarial learning and training the topic discriminator to capture transferable knowledge enables the model to generalize to unseen topics. w/o GeoEnc brings performance drops, which suggests that infusing geographic signals can provide the model with an opportunity to learn regional contextual factors and improve the detection accuracy by using them as non-text information. In addition, we note that, compared to w/ GeoEmb which ignores any geographic signal, w/o GeoEnc causes performance degradation in some cases, such as WM→SH and VA→SH. A potential reason could be the introduction of noise due to insufficient training, which will undermine the performance. The complete model outperforms w/o EK, which is attributed to the integration of external knowledge as a bridge, allowing the model to understand topics better.

C. Model Size and Efficiency
In Table V, we record and compare the model size and efficiency of all methods on SH task, which uses the most samples for training. For the legibility of the comparison results, we plot the relationship between model performance, model size, and training time in Fig. 3. BERT-based models have large trainable parameters because increasing model size when pretraining natural language representations often result  in improved performance on downstream tasks. Compared to WS-BERT, our proposed model has an acceptable model size. Among all models, TextCNN-C is the fastest in terms of training time and inference time, as it applies convolutional neural networks to capture word coherence in parallel. Our model has the longest training time per epoch, this is because, during adversarial learning process, it is necessary to leverage a large amount of unlabeled data to train the discriminator for domain adaptation. Therefore, the increase of training samples will inevitably increase the training time of each epoch. Despite this, adversarial learning has no obvious adverse effect on inference time. Thus, the increment of training time can be seen as a trade-off against the performance gains by training the discriminator to extract transferable knowledge.

VI. CONCLUSION
In this paper, we consider a more realistic scenario on stance detection (i.e., cross-target and zero-shot settings) for the pandemic and propose an adversarial learning-based stance classifier to automatically identify the public's attitudes toward COVID-19-related health policies. Specifically, we apply adversarial learning to ensure transferability and discriminability to emerging health policies and devise a GeoEncoder to characterize geographic signals as non-text features to capture unseen contextual factors and then enhance the model's deeper understanding. Extensive experiments show that our proposed method achieves state-of-the-art performance in both crosstarget and zero-shot settings.