SlowGuess/NAACL_2022_OCR huggingface dataset:
Miguel Arana-Catania $^{1,2}$ , Elena Kochkina $^{3,2}$ , Arkaitz Zubiaga $^{3}$ , Maria Liakata $^{3,2}$ , Rob Procter $^{1,2}$ , Yulan He $^{1,2}$
3Queen-Mary University of London, UK
We present a comprehensive work on automated veracity assessment from dataset creation to developing novel methods based on Natural Language Inference (NLI), focusing on misinformation related to the COVID-19 pandemic. We first describe the construction of the novel PANACEA dataset consisting of heterogeneous claims on COVID-19 and their respective information sources. The dataset construction includes work on retrieval techniques and similarity measurements to ensure a unique set of claims. We then propose novel techniques for automated veracity assessment based on Natural Language Inference including graph convolutional networks and attention based approaches. We have carried out experiments on evidence retrieval and veracity assessment on the dataset using the proposed techniques and found them competitive with SOTA methods, and provided a detailed discussion.
In recent years, and particularly with the emergence of the COVID-19 pandemic, significant efforts have been made to detect misinformation online with the aim of mitigating its impact. With this objective, researchers have proposed numerous approaches and released datasets that can help with the advancement of research in this direction.
Most existing datasets (D'Ulizia et al., 2021) focus on a single medium (e.g., Twitter, Facebook, or specific websites), a unique information domain (e.g., health information, general news, or scholarly papers), a type of information (e.g., general claims or news), or a specific application (e.g., verifying claims, or retrieving useful information). This inevitably results in a limited focus on what is a complex, multi-faceted phenomenon. With the aim of furthering research in this direction, the contributions of our work are twofold: (1) creating a new comprehensive dataset of misinformation claims, and (2) introducing two novel approaches to veracity assessment.
In the first part of our work, we contribute to the global effort on addressing misinformation in the context of COVID-19 by creating a dataset for PANdemic Ai Claim vEracity Assessment, called the PANACEA dataset. It is a new dataset that combines different data sources with different foci, thus enabling a comprehensive approach that combines different media, domains and information types. To this effect our dataset brings together a heterogeneous set of True and False COVID claims and online sources of information for each claim. The collected claims have been obtained from online fact-checking sources, existing datasets and research challenges. We have identified a large overlap of claims between different sources and even within each source or dataset. Thus, given the challenges of aggregating multiple data sources, much of our efforts in dataset construction has focused on eliminating repeated claims. Distinguishing between different formulations of the same claim and nuanced variations that include additional information is a challenging task. Our dataset is presented in a large and a small version, accounting for different degrees of such similarity. Finally, the homogenisation of datasets and information media has presented an additional challenge, since fact-checkers use different criteria for labelling the claims, requiring a specific review of the different kinds of labels in order to combine them.
In the second part of our work, we propose NLI-SAN and NLI-graph, two novel veracity assessment approaches for automated fact-checking of the claims. Our proposed approaches are centred around the use of Natural Language Inference (NLI) and contextualised representations of the claims and evidence. NLI-SAN combines the inference relation between claims and evidence with attention techniques, while NLI-graph builds on graphs considering the relationship between all the different pieces of evidence and the claim.
Specifically we make the following contributions:
Human Language Technologies, pages 1496 - 1511
- We describe the development of a comprehensive COVID fact-checking dataset, PANACEA, as a result of aggregating and de-duplicating a set of heterogeneous data sources. The dataset is available in the project website<sup>1</sup>, as well as a fully operational search platform to find and verify COVID-19 claims implementing the proposed approaches.
- We propose two novel approaches to claim verification, NLI-SAN and NLI-graph.
- We perform an evaluation of both evidence retrieval and the application of our proposed veracity assessment methods on our constructed dataset. Our experiments show that NLI-SAN and NLI-graph have state-of-the-art performance on our dataset, beating GEAR (Zhou et al., 2019) and matching KGAT (Liu et al., 2020). We discuss challenging cases and provide ideas for future research directions.
COVID-19 and misinformation datasets. Comprehensive information on COVID-19 datasets is provided in Appendix A. Such datasets include the CoronaVirusFacts/DatosCoronaVirus Alliance Database, the largest existing collection of COVID claims and the largest existing network of journalists working together on COVID misinformation, an essential reference for our work; COVID-19-TweetIDs (Chen et al., 2020) the widest dataset of COVID tweets with more than 1 billion tweets; Cord-19: The COVID-19 open research dataset (Wang et al., 2020a), the largest downloadable set of scholarly articles on the pandemic with nearly 200,000 articles. General misinformation datasets linked to our verification work include: Emergent (Ferreira and Vlachos, 2016) collection of 300 labeled claims by journalists; LIAR (Wang, 2017) with 12,836 statements from PolitiFact with detailed justifications; FakeNewsNet (Shu et al., 2020) collecting not only claims from news content, but also social context and spatio-temporal information; NELA-GT-2018 (Nørregaard et al., 2019) with 713,534 articles from 194 news outlets; FakeHealth (Dai et al., 2020) collecting information from HealthNewsReview, a project critically analysing claims about health care interventions; PUBHEALTH (Kotonya and Toni, 2020) with 11,832 claims related to health topics; FEVER (Thorne et al., 2018a) as well as its later versions
FEVER 2.0 (Thorne et al., 2018b) and FEVEROUS (Aly et al., 2021), containing claims based on Wikipedia and therefore constituting a well-defined, informative and non-duplicated information corpus; SciFact (Wadden et al., 2020) also from a very different domain, containing 1,409 scientific claims. Our dataset is a real-world dataset bringing together heterogeneous sources, domains and information types.
Approaches to claim veracity assessment. We employ our dataset for automated fact-checking and veracity assessment (Zeng et al., 2021). Researchers such as Hanselowski et al. (2018); Yoneda et al. (2018); Luken et al. (2018); Soleimani et al. (2020); Pradeep et al. (2021) analysed the veracity relation between the claim and each piece of evidence independently, combining this information later. Other authors considered multiple pieces of evidence together (Thorne et al., 2018a; Nie et al., 2019; Stammbach and Neumann, 2019). Different pieces of evidence have been previously combined using graph neural networks (Zhou et al., 2019; Liu et al., 2020; Zhong et al., 2020). Many of these authors have centred their techniques on the use of NLI (Chen et al., 2017; Ghaeini et al., 2018; Parikh et al., 2016; Li et al., 2019) to verify the claim. In our work we also make use of NLI results of claim-evidence pairs, but propose alternative approaches built on a self-attention network and a graph convolutional network for veracity assessment.
This section describes our dataset construction by selecting COVID-19 related data sources (§3.1), and applying information retrieval and re-ranking techniques to remove duplicate claims (§3.2).
We first identified a set of COVID-19 related data sources to build our dataset. Our aim is to have the largest compilation of non-overlapping, labelled and verified claims from different media and information domains (Twitter, Facebook, general websites, academia), and used for different applications (media reporting, veracity evaluation, information retrieval challenges, etc.). We have included any large dataset or media, to our knowledge, related to that objective that includes claims together with their information sources. The data sources identified are shown in Table 1. More details and preprocessing steps are presented in Appendix A. By
<https://panacea2020.github.io/ https://doi.org/10.5281/zenodo.6493847
Data Source	Description	Domain	No. of claims (False / True)
CoronaVirusFacts Database	Published by Poynter, this online source combines fact-checking articles from more than 100 fact-checkers from all over the world, being the largest journalist fact-checking collaboration on the topic worldwide.	Heterogeneous	11,647 (11,647 / 0)
CoAID dataset (Cui and Lee, 2020)	This contains fake news from fact-checking websites and real news from health information websites, health clinics, and public institutions.	News	5,485 (953 / 4,532)
MM-COVID (Li et al., 2020)	This multilingual dataset contains fake and true news collected from Poynter and Snopes.	News	3,409 (2,035 / 1,374)
CovidLies (Hossain et al., 2020)	This contains a curated list of common misconceptions about COVID appearing in social media, carefully reviewed to contain very relevant and unique claims.	Social media	62 (62 / 0)
TREC Health Misinformation track	Research challenge using claims on the health domain focused on information retrieval from general websites through the Common crawl corpus (commoncrawl.org).	General websites	46 (39 / 7)
TREC COVID challenge (Voorhees et al., 2021; Roberts et al., 2020)	Research challenge using claims on the health domain focused on information retrieval from scholar peer-reviewed journals through the CORD19 dataset (Wang et al., 2020a), the largest existing compilation of COVID-related articles.	Scholar papers	40 (3 / 37)
Table 1: Data sources used for the construction of our dataset. The last column shows the number of claims before de-duplication.
processing and combining these sources we obtained 20,689 initial claims.
3.2 Claim De-duplication
We processed claims and removed: exact duplicates; claims making only a direct reference to existing content in other media (audio, video, photos); automatically obtained content not representing claims; entries with claims or fact-checking sources in languages other than English.
The similarity of claims was then analysed using: BM25 (Robertson et al., 1995; Crestani et al., 1998; Robertson and Zaragoza, 2009) and BM25 with MonoT5 re-ranking (Nogueira et al., 2020). BM25 is a commonly-used ranking function that estimates the relevance of documents to a given query. MonoT5 uses a T5 model trained using as input the template 'Query: [query] Document: [doc] Relevant:', fine-tuned to produce as output the token 'True' or 'False'. A softmax layer applied to those tokens gives the respective relevance probabilities. These methods are used to identify not only claims similar in content, but also distinct claims that are sufficiently relevant when searching for information about them. This ensures that the claims presented are unique, and avoids overlap between training and testing cases when using the data to train veracity assessment models. These methods were carried out using
Pyserini $^{2}$  and PyGaggle $^{3}$ . The set of claims was indexed and a search was performed for each of the claims to detect similar claims. We created two versions of the dataset by varying the similarity threshold between claims. The LARGE dataset excludes claims with a  $90\%$  probability of being similar, while in the SMALL dataset the probability is increased to  $99\%$ , as obtained through the MonoT5 model. These thresholds were chosen empirically by manual inspection of the results with simultaneous consideration of the efficiency of the method.
As a further assessment of the uniqueness of the claims, we evaluated the de-duplication process using BERTScore $^{4}$  (Zhang et al., 2019) on the resulting datasets. We used the linked code with a RoBERTa-large model with baseline rescaling. We compared each claim with all the other claims in the dataset and kept the score of the most similar match. The mean and standard deviation, and the 90th percentile of claim similarity values are shown in the upper part of Table 3. The average claim similarity has been drastically reduced in the LARGE dataset compared to the original dataset and further reduced in the SMALL dataset.
To illustrate the difference between the two ver
<sup>3</sup>https://github.com/castorini/pygaggle
4https://github.com/Tiiger/bert_score
Claim 1: Losing your sense of smell may be an early symptom of COVID-19.
Exclude from LARGE and SMALL:
Loss of smell may suggest milder COVID-19.
Exclude from SMALL only:
Loss of smell and taste validated as COVID-19 symptoms in patients with high recovery rate.
Claim 2: COVID-19 hitting some African American communities harder.
Exclude from LARGE and SMALL:
The African American community is being hit hard by COVID-19.
Exclude from SMALL only:
COVID-19 impacts in African-Americans are different from the rest of the U.S. population.
sions of the dataset, we present some examples of claims in Table 2. For Claim 1, the semantically similar claim 'Loss of smell may suggest milder COVID-19' is identified and excluded from both LARGE and SMALL datasets. But the claim 'Loss of smell and taste validated as COVID-19 symptoms in patients with high recovery rate', which includes mentions of another symptom and the recovery rate, is only excluded from the SMALL dataset. For Claim 2, the rephrased claim 'The African American community is being hit hard by COVID-19' is excluded from both datasets. But the claim 'COVID-19 impacts in African-Americans are different from the rest of the U.S. population', which refers specifically to the U.S. population, is only excluded from the SMALL dataset.
Our final dataset statistics are shown in the lower part of Table 3, where the original and the two reduced versions are presented. After the steps described in Section 3.2 the LARGE dataset contains 5,143 claims, and the SMALL version 1,709 claims.
Table 2: Claim de-duplication examples.
Category	Orig.	LARGE	SMALL
Similarity	0.67 ± 0.23	0.43 ± 0.13	0.37 ± 0.14
η.90	0.99	0.60	0.56
False	14,739	1,810	477
True	5,950	3,333	1,232
Total	20,689	5,143	1,709
Table 3: The average claim similarity values and the PANACEA LARGE and SMALL dataset statistics.  $\eta_{.90}$  denotes the 90th percentile value.
Example claims contained in the dataset are shown in Table 4. Each of the entries in the dataset
contains the following information:
- Claim. Text of the claim.
- Claim label. The labels are: False, and True.
- Claim source. The sources include mostly fact-checking websites, health information websites, health clinics, public institutions sites, and peer-reviewed scientific journals.
- Original information source. Information about which general information source was used to obtain the claim.
- Claim type. The different types, explained in Section A.2, are: Multimodal, Social Media, Questions, Numerical, and Named Entities.
4 Claim Veracity Assessment
We develop a pipeline approach consisting of three steps: document retrieval, sentence retrieval and veracity assessment for claim veracity evaluation. Given a claim, we first retrieve the most relevant documents from COVID-19 related sources and then further retrieve the top  $N$  most relevant sentences. Considering each retrieved sentence as evidence, we train a veracity assessment model to assign a True or False label to the claim.
Document Dataset. In order to retrieve documents relevant to the claims, we first construct an additional dataset containing documents obtained from reliable COVID-19 related websites. These information sources represent a real-world comprehensive database about COVID-19 that can be used as a primary source of information on the pandemic. We have selected four organisations from which to collect the information: (1) Centers for Disease Control and Prevention (CDC), national public health agency of the United States; (2) European Centre for Disease Prevention and Control (ECDC), EU agency aimed at strengthening Europe's defenses against infectious diseases; (3) WebMD, online publisher of news and information on health; and (4) World Health Organization (WHO), agency of the United Nations responsible for international public health.
All pages corresponding to the COVID-19 subdomains of each site have been downloaded. The
Claim	Category	Source	Orig. data src.	Type
Stroke Scans Could Reveal COVID-19 Infection.	True	ScienceDaily	CoAID
Whiskey and honey cure coronavirus. COVID-19 is more deadly than Ebola or HIV.	False	Independent news site	CovidLies
False	Australian Associated Press	Poynter
Dextromethorphan worsens COVID-19.	True	Nature	TREC Health Misinformation track
ACE inhibitors increase risk for coronavirus.	False	Infectious Disorders - Drug Targets journal	TREC COVID challenge
Nancy Pelosi visited Wuhan, China, in November 2019, just a month before the COVID-19 outbreak there.	False	Snopes	MM-COVID	Named Entity, Numerical content
Table 4: Example entries in the constructed PANACEA dataset.
web content was downloaded using the Beautiful-Soup<sup>5</sup> and Scrapy<sup>6</sup> packages. Social networking sites and non-textual content were discarded. In total 19,954 web pages have been collected. The list of websites and the full content of each website constitute this additional dataset used for document retrieval. This dataset is enhanced with some additional websites used only in the document retrieval experiments, detailed in Section 5.1.
Method. Information sources were indexed by creating a Pyserini Lucene index and PyGaggle was used to implement a re-ranker model on the results. The documents were split into paragraphs of 300 tokens segmented with a BERT tokenizer.
To retrieve the information we first used a BM25 score. Additionally, we tested the effect of multi-stage retrieval by re-ranking the initial results using MonoBERT (Nogueira et al., 2019) and MonoT5 models, and query expansion using RM3 pseudorelevance feedback (Abdul-Jaleel et al., 2004) on the BM25 results (Lin, 2019; Yang et al., 2019).
MonoBERT uses a BERT model trained using as inputs the query and each of the documents to be re-ranked encoded together ([CLS] query [SEP] doc [SEP]), and then the [CLS] output token is passed to a single layer fully-connected network that produces the probability of the document being relevant to the query.
4.2 Sentence Retrieval
For each claim, once documents are retrieved using BM25 and MonoT5 re-ranking of the top 100 BM25 results, we then further retrieve the  $N$  most similar sentences obtained from the 10 most relevant documents. The relevance of the sentences is
calculated using cosine similarity in relation to the original claim. The similarity is obtained with the pre-trained model MiniLM-L12-v2 (Wang et al., 2020b), using Sentence-Transformers $^{7}$  (Reimers and Gurevych, 2019) to encode the sentences.
4.3 Veracity Assessment
We propose two veracity assessment approaches built on the NLI results of claim-evidence pairs. For each of the most similar sentences (pieces of evidence) retrieved for a claim, we apply the pretrained NLI model RoBERTa-large-MNLI $^{8}$ (Liu et al., 2019). This model acts as a cross-encoder on pairs of sentences, trained to detect the relationship between the two sentences: contradiction, neutrality, or entailment. The model is trained on the Multi-Genre Natural Language Inference (MultiNLI) dataset (Williams et al., 2018). The inference results are then used in our proposed approaches described below.
NLI-SAN. The first approach, named NLI-SAN, incorporates the inference results of claim-evidence pairs into a Self-Attention Network (SAN) (See Figure 1a). First, a claim is paired with each piece of retrieved relevant evidence. Each pair  $(c,e_i)$  is fed into a RoBERTa-large model, and the last hidden layer output  $\mathbf{S}_{\mathbf{i}}$  is used as its representation. Additionally, each pair is also fed to the mentioned RoBERTa-large-MNLI model obtaining  $\mathbf{I}_{\mathbf{i}}$ , a triplet containing the probability of contradiction, neutrality, or entailment.
$$
\begin{array}{l} \mathbf {S} _ {\mathbf {i}} = \operatorname {R o B E R T a} (c, e _ {i}) \\ \mathbf {I} = \operatorname {R o B E R T a} (c) \end{array} \tag {1}
$$
$$
\mathbf {I} _ {\mathbf {i}} = \operatorname {R o B E R T a} _ {\mathrm {N L I}} (c, e _ {i})
$$
<https://www.cummy.com/software/BeautifulSoup/>
$^{6}$ https://scrapy.org/
$^{7}$ https://github.com/UKPLab/sentence-transformers
<sup>8</sup>https://huggingface.co/
(a) NLI-SAN
(b) NLI-graph
Figure 1: Proposed veracity classification models.  $\oplus$  means concatenation.
The sentence representation is combined with the NLI output through a Self Attention Network (SAN) (Galassi et al., 2020; Bahdanau et al., 2015).
The RoBERTa-encoded claim-evidence representation  $\mathbf{S_i}$  with length  $n_S = n_K = n_V$  is mapped onto a Key  $\mathbf{K} \in \mathbb{R}^{n_K \times d_K}$  and a Value  $\mathbf{V} \in \mathbb{R}^{n_V \times d_V}$ , while the NLI output  $\mathbf{I_i}$  of each claim-evidence pair is mapped onto a Query  $\mathbf{Q} \in \mathbb{R}^{n_Q \times d_Q}$ . The representation dimensionality is  $d_K = d_V = d_Q = 1024$ . The attention function is defined as:
$$
\operatorname {A t t} (\mathbf {Q}, \mathbf {K}, \mathbf {V}) = \operatorname {s o f t m a x} \left(\mathbf {Q} \mathbf {K} ^ {\top} / \sqrt {d}\right) \mathbf {V} \tag {2}
$$
While standard attention mechanisms use only the sentence representation information for the Key, Value and Query, here the inference information is used in the Query. This attention mechanism is applied to each of the claim-evidence pairs, and the outputs are concatenated into an output  $\mathrm{O_{SAN}}$  that is passed through a Multi-Layer Perceptron (MLP) with hidden size  $d_h$  and a Softmax layer to generate the veracity classification output.
$$
\hat {\boldsymbol {y}} = \operatorname {s o f t m a x} \left(\mathrm {M L P} _ {\text {R e L U}} \left(\mathrm {O} _ {\text {S A N}}\right)\right) \tag {3}
$$
NLI-graph. We propose an alternative approach based on Graph Convolutional Networks (GCN). First, for each claim-evidence pair, we derive RoBERTa-encoded representations for the claims and evidence separately (using the pooled output of the last layer) and obtain NLI results of the pairs
$$
\mathbf {C} _ {\mathbf {i}} = \operatorname {R o B E R T a} (c); \quad \mathbf {E} _ {\mathbf {i}} = \operatorname {R o B E R T a} \left(e _ {i}\right) \tag {4}
$$
$$
\mathbf {I} _ {\mathrm {i}} = \operatorname {R o B E R T a} _ {\mathrm {N L I}} (c, e _ {i}) \tag {5}
$$
Next, we build an evidence network in which the central node is the claim and the rest of the nodes are the evidence. Two nodes are linked if their similarity value exceeds a pre-defined threshold, which is empirically set to 0.9 by comparing the results of the experimental evaluation described in the following section using different thresholds. The similarity is considered between claim and evidence, but also between pieces of evidence. Similarity calculation is performed following the same approach as in Section 4.2. The features considered in each evidence node are the concatenation of  $\mathbf{E_i}$  and  $\mathbf{I_i}$ . For the claim node we use its representation  $\mathbf{C_i}$  and a unity vector  $(0,0,1)$  for the inference. The network is implemented with the package PyTorch Geometric (Fey and Lenssen, 2019), using in the first layer the GCNConv operator (Kipf and Welling, 2016) with 50 output channels and self-loops to the nodes, represented by:
$$
\mathbf {X} ^ {\prime} = \hat {\mathbf {D}} ^ {- 1 / 2} \hat {\mathbf {A}} \hat {\mathbf {D}} ^ {- 1 / 2} \mathbf {X} \mathbf {W}, \tag {6}
$$