SlowGuess/NAACL_2022_OCR huggingface dataset:
Maria Leonor Pacheco $^{1*}$  Tunazzina Islam $^{1*}$  Monal Mahajan $^{1}$
Andrey Shor<sup>1</sup> Ming Yin<sup>1</sup> Lyle Ungar<sup>2</sup> Dan Goldwasser<sup>1</sup>
$^{1}$ CS, Purdue University, USA,  $^{2}$ CIS, University of Pennsylvania, USA
{pachecog,islam32,mmahaja,ashor,mingyin,dgoldwas}@purdue.edu  
ungar@cis.upenn.edu
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and fine-grained entity level moral sentiment analysis. We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predictions even in the low-supervision settings.
One of the unfortunate side-effects of the Covid-19 pandemic is a global infodemic flooding social media with low quality and polarizing information about the pandemic, influencing public perception on it (Tagliabue et al., 2020). As studies have shown (Montagni et al., 2021), these influences have clear real-world implications, in terms of public acceptance of treatment options, vaccination and prevention measures.
Most computational approaches tackling the Covid-19 infodemic view it a misinformation detection problem. In other words, they look at identifying false claims and analyzing reactions to them on social media (Hossain et al., 2020; Alam et al., 2021; Weinzierl et al., 2021). This approach, while definitely a necessary component in fighting the infodemic, does not provide policy makers and health-professionals with much needed information, characterizing the reasons and attitudes that underlie the health and well-being choices individuals make.
Our goal in this paper is to suggest a holistic analysis framework, providing multiple interconnected views of the opinions expressed in
Figure 1: Holistic Analysis Framework of Social Media Posts, Connecting entity-level Moral Perspectives, Stance and Arguments Justifying it.
text. We specifically focus on a timely topic, attitudes explaining vaccination hesitancy. Figure 1 describes an example of our framework. Our analysis identifies the stance expressed in the post (anti-vaccination) and the reason for it (distrust of government). Given the ideologically polarized climate of social media discussion on this topic, we also aim to characterize the moral attitudes expressed in the text (oppression), and how different entities mentioned in it are perceived ("Biden, Government" are oppressing, "citizens, us" are oppressed). When constructing this framework we tackled three key challenges.
1. How should these analysis dimensions be operationalized? While stance prediction is an established NLP task, constructing the space of possible arguments justifying stances on a given topic, and their identification in text, are still open challenges. We take a human-in-the-loop approach to both problems. We begin by defining a seed set of relevant arguments based on data-driven studies (Weinzierl et al., 2021; Sowa et al., 2021), where each reason is defined by a single exemplar sentence. In a sequence of interactions, we use a pre-trained textual inference model to identify paraphrases in a large collection of Covid-19 vaccination tweets, and present a visualization of the results to humans. Humans then perform an error
Human Language Technologies, pages 5821 - 5839
analysis, and either add more sentences to help characterize the existing reason better, or add and characterize additional reasons, based on examples retrieved from the large corpus. We explain this process in detail in Section 2.
Our morality analysis is motivated by social science studies (Pagliaro et al., 2021; Diaz and Cova, 2021; Chan, 2021) that demonstrate the connection between moral foundation preferences (Haidt and Graham, 2007; Graham et al., 2009) and Covid-related health choices. For example, studies show that the endorsement of the fairness and care moral foundations is correlated with trust in science. To account for fine-grained patterns, we adapt the recently proposed morality-frame formalism (Roy et al., 2021) that identifies moral roles associated with moral foundation expressions in text. These roles correspond to actor/target roles (similar to agent/patient) and positive or negative polarity, which should be understood in the context of a specific moral foundation. In Figure 1 "Biden" is the negative actor in the context of Oppression, making him the oppressor. We explain this formalism in Section 3.
2. How should the dependencies between these dimensions be captured and utilized? The combination of stance, reason and moral attitudes provides a powerful source of information, allowing us to capture the moral attitudes expressed in the context of different stances and their reasons. These connections can also be used to help build expectations about likely attitudes in the context of each stance. As a motivating example, consider the reason "distrust in government", which can be associated with the "oppression" moral foundation only when its actor is an entity related to government functions (rather than oppression from Covid-19 illness). We model these expectation as a probabilistic inference process (Pacheco and Goldwasser, 2021), by incorporating consistency constraints over the judgements made by our model, and predicting the most likely analysis jointly, consisting of all analysis dimensions. The full model, described using a declarative modeling language, is provided in Section 5.
3. How can text analysis models be adapted to this highly dynamic domain, without extensive and costly manual annotation? While our analysis in this paper focuses on a specific issue, vaccination hesitancy, we believe that our analysis framework should be easily adaptable to new issues.
Relying on human insight to characterize and operationalize stance and reason identification is one aspect, that characterizes issue-specific considerations. Moral Foundation Theory, by its definition, abstracts over specific debate topics, and offers a general account for human morality. However, from a practical perspective, models for predicting these highly abstract concepts are trained on data specific to a debate topic and might not generalize well. Instead of retraining the model from scratch, we hypothesize that given an initial model constructed using out-of-domain data, and a small amount of in-domain labeled data, we can obtain acceptable performance by modeling the interaction between reasons, stances and moral foundations. We study these settings, along with the fully supervised setting in Section 6.
The data, code and tools used in this paper are publicly available*.
2 Opinion Analysis
To analyze opinions about the Covid-19 vaccine, we model the vaccination stance expressed in each tweet (i.e. pro-vaccine, anti-vaccine, neutral) and the underlying reason behind such stance. For example, in Figure 1 the tweet expresses an antivaccine stance, and mentions their distrust of the Biden administration as the reason to take this stance.
There are three main challenges involved in this analysis: 1) predicting the stance, 2) constructing the space of possible reasons, and 3) mapping tweets to the relevant reasons. Stance prediction is an established NLP classification task (Glandt et al., 2021). However, uncovering latent themes from text automatically remains an open challenge, traditionally approached using noisy unsupervised techniques such as topic models (Zamani et al., 2020b), or by manually identifying and annotating them in text (Hasan and Ng, 2014).
Instead, we combine computational and qualitative techniques to uncover the most frequent reasons cited for pro and anti vaccination stances. We build on previous health informatics studies that characterized the arguments made against the Covid-19 vaccine in social media (Wawrzuta et al., 2021). In this work, researchers come up with a code-book of 12 main themes, frequently used as reasons to refuse or cast doubt on the vaccine. We
\*https://gitlab.com/mlpacheco/covid-moral-foundations
show Reasons() lists the current list of reasons (e.g. Government Distrust, Natural Immunity.)
showclosesttweets(result, K) lists the K tweets closest to a given reason, based on their embedding similarity.
wordcloud(result) Rendering a word cloud to visualize the arguments associated to a given reason, based on bigram and trigram TF-IDF features.
show_assignments(threshold) Rendering a bar plot showing the assignment of tweets to reasons, based on embedding similarity. An optional threshold can be used to limit assignments.
tsne(threshold) Rendering a visualization of the reason clusters in a 2D map. Threshold is optional.
silhouette_score(threshold) Measures the overlapping degree between clusters. Threshold is optional.
add_reason(result, phrase) Adds a new reason with a phrase that characterizes it in natural language
remove_reason(result) Removes a given reason
addphrase(result, phrase)Adds an additional phrase to an existing reason.
propose an interactive, humans-in-the-loop protocol to learn representations for these 12 initial reasons, ground them in data, evaluate their quality, and refine them to better capture the discussion. To do this, we build a tool to explore repeating arguments and their reasons in the Covid-19 vaccine debate. The tool consists of an interactive Google Colab notebook equipped with a custom API to query current arguments, ground them in data, and visualize them. To initialize the system, we use the 12 reasons suggested by Wawrzuta et al. (2021), and represent them using the one-sentence explanation provided. Our main goal is to ground these reasons in a set of approximately 85,000 unlabeled tweets about the Covid-19 vaccine (details in Section 4). To map tweets to reasons, we use the similarity between their SBERT embeddings (Reimers and Gurevych, 2019). The interaction is centered around the operations outlined in Table 1. Intuitively, the first six operations allow humans to diagnose how reasons map to text, and the last three allows them to act on the result of this diagnosis, by adding and removing reasons, and modifying the phrases characterizing each reason.
We follow a simple protocol during interaction, where three human coders use the operations above to explore the initial reasons. The coders start by looking at the global picture: the reasons distribution, the 2D visualizations (van der Maaten and Hinton, 2008) and the silhouette score (Rousseeuw, 1987). Then, they query the reasons one by one, looking at the word cloud (characterizing the distribution of short phrases over all texts assigned to the reason) and the 10 closest tweets to each reason. Following these observations, there is a discussion
Table 1: Interactive API Operations
PRO
VAX	government distrust, vaccine dangerous, covid fake, vaccine oppression, pharma bad, natural immunity effective, vaccine against religion, vaccine does not work, vaccine not tested, bill gates' micro chip, vaccine tested on dogs, vaccine has fetal tissue, vaccine makes you sterile
ANTI
VAX	government trust, vaccine safe, covid real, vaccine not op-pression, pharma good, natural immunity ineffective, vac-cine not against religion, vaccine works, vaccine tested
Table 2: Resulting Reasons
phase in which the coders follow a thematic analysis approach (Braun and Clarke, 2012) to uncover the overarching themes that are not covered by the current set of reasons, as well as the argumentation patterns that the method fails to identify. Then, they are allowed to add and remove reasons, as well as explanatory phrases for them in natural language. Every time a reason or phrase is added or removed, all tweets are reassigned to their closest reasons. This process was done over two one-hour sessions. The coders were NLP and Computational Social Science researchers, two female and one male, between the ages of 25 and 40.
In the first session, the coders focused on adding new reasons and removing reasons that were not prevalent in the data. For example, they noticed that the initial set of reasons contained mostly anti-vaccine arguments, and added a positive reason for each negative reason (e.g. government distrust  $\Rightarrow$  government trust). In addition to this, they broke down the reason "Conspiracy Theory" into specific conspiracy theories, such as Bill Gates' micro chip, the vaccine contains fetal tissue, and the vaccine makes you sterile. They also removed infrequent reasons, such as the swine flu vaccine. The final set of reasons can be observed in Table 2.
In the second session, the coders focused on identifying the argumentative patterns that were not being captured by the original reason explanations, and came up with overarching patterns to create new examples to improve the representation of the reasons. For example, in the case of the government distrust reason, the coders found that phrases with strong words were needed (e.g.  $F$  the government), examples that suggested that the government was "good at being bad" (e.g. the government strong record of screwing things up), and examples with explicit negations (e.g. the government does not work logically). Once patterns were identified, each coder contributed a set of 2 to 5 examples, which were introduced to the reason representation.
CARE/HARM: Underlies virtues of kindness, gentleness, and nurturance.
FAIRNESS/CHEATING: Generates ideas of justice, rights, and autonomy.
LOYALTY/BETRAYAL: Underlies virtues of patriotism and self-sacrifice for the group. It is active anytime people feel that it's "one for all, and all for one."
AUTHORITY/SUBVERSION: Underlies virtues of leadership and followerhip, including deference to legitimate authority and respect for traditions.
PURITY/DEGRADATION: Underlies religious notions of striving to live in an elevated, less carnal, more noble way. It underlies the widespread idea that the body is a temple which can be desecrated by immoral activities and contaminants.
LIBERTY/OPPRESSION: The feelings of reactance and resentment people feel toward those who dominate them and restrict their liberty.
Table 3: Moral Foundations (Haidt and Graham, 2007)
In Appendix A.1, we include screenshots of the interactive notebook, and tables enumerating the full list derived patterns and phrases. To visualize the impact of interaction, we also show the overall distribution of reasons before and after interaction, and word clouds for a select set of reasons. The methodology and tool we developed are broadly applicable for diagnosing NLP models.
3 Morality Frame Analysis
Moral Foundations Theory (Haidt and Graham, 2007) suggests that there are at least six basic foundations that account for the similarities and recurrent themes in morality across cultures, each with a positive and negative polarity (See Table 3).
To analyze moral perspectives in tweets, we build on the definition of morality frames proposed by Roy et al. (2021), where moral foundations are regarded as frame predicates, and associated with positive and negative entity roles.
While Roy et al. (2021) defined different roles types for each moral foundation (e.g. entity causing harm, entity ensuring fairness), we aggregate them into two general role types: actor and target, each with an associated polarity (positive, negative). An actor is a "do-er" whose actions or influence results in a positive or negative outcome for the target (the "do-ee"). For each moral foundation in a given tweet, we identify the "entity doing good/bad" (positive/negative actor) and "entity benefiting/suffering" (positive/negative target). For example, the statement "We are suffering from the pandemic" expresses harm as the moral foundation, where "pandemic" is a negative actor, and "we" is a negative target (i.e. the entity suffer
ing from the actor's actions). There can be zero, one or multiple actors and targets in a given tweet. Entities can correspond to specific individuals or groups (e.g., I, democrats, people of a given demographic), organizations (e.g., political parties, CDC, FDA, companies), legislation or other political actions (e.g., demonstrations, petitions), disease or natural disasters (e.g., Covid, global warming), scientific or technological innovations (e.g., the vaccine, social media, the Internet), among others.
We break down the task of predicting morality frames into four classification tasks. For each tweet, our goal is to predict whether it is making moral judgement or not, and identify its prominent moral foundation. For each entity mentioned in the tweet, we predict whether it is a target or a role, and whether it has positive or negative polarity.
4 Data Collection and Annotation
There is no existing corpus of arguments about the Covid-19 vaccine annotated for morality frames and vaccination stance, so we collected and annotated our own. First, we searched for tweets between April and October of 2021 mentioning specific keywords, such as covid vaccine and vaccine mandate. The full list of keywords, as well as the procedure to obtain them, can be seen in Appendix A.2.
Then, we created an exclusive web application for annotating our task. Moral foundation and vaccination stance labels can be annotated directly. To identify entities, annotators were able to highlight the relevant text spans, and choose its role label (i.e. positive/negative actor or target). We annotated our dataset using three in-house annotators pursuing a Ph.D. in Computer Science. We awarded the annotators \(0.75 per tweet and bonus \((2*\$0.75 = \$1.5)\) for completing two practice examples. Our work is IRB approved, and we follow their protocols.
To ensure quality work, we provided annotators with eight examples covering all six moral foundations and non-moral cases. Before starting the annotation task, the annotators had to read the instructions, go through the examples, and annotate two practice questions. The annotation interface, examples and practice questions can be seen in Appendix A.3.
Inter-annotator agreement We calculated the agreement among annotators using Krippendorff's  $\alpha$  (Krippendorff, 2004), where  $\alpha = 1$  suggests perfect agreement, and  $\alpha = 0$  suggests chance-level
(a) Reasons and Vax Stance
(b) Reasons and Moral Foundations
(c) Moral Foundations and Vax Stance
Figure 2: Correlation Heatmaps
agreement. We found  $\alpha = 60.82$  for moral foundations, and  $\alpha = 78.71$  for stance. For roles, we calculated the character by character agreement between annotations. For example, if one annotator marked "Dr Fauci" as a target in a tweet, and another marked "Fauci", it was considered to be an agreement on the characters "Fauci" but disagreement on "Dr". Doing this, we found  $\alpha = 83.46$ . When removing characters marked by all three annotators as "non-role", the agreement dropped to  $\alpha = 67.15$ .
Resulting annotated dataset We used a majority vote to get moral foundation and vaccination stance labels, and obtained 750 annotated tweets. Similarly, we defined a text span to be an entity mention E, having a moral role R and polarity P, in a tweet T, if it was annotated as such by at least two annotators. Our resulting dataset contains 891 (T,E,R,P) tuples. Statistics can be seen in Table 4.
MORAL FOUNDATION	NUM. TW.	VACCINATION STANCE
PRO	ANTI	NEUT	NO AGREE
Care/Harm	96	77	17	2	0
Fairness/Cheating	75	33	28	14	0
Loyalty/Betrayal	33	26	2	5	0
Authority/Subversion	114	26	72	13	3
Purity/Degradation	24	2	22	0	0
Liberty/Oppression	93	9	78	6	0
Non-moral	304	188	68	44	4
No Agreement	11	6	5	025	0
TOTAL	750	367	292	84	7
Table 4: Dataset Summary
To evaluate the correlation between the different dimensions of analysis, we calculate the Pearson correlation matrices and present them in Figure 2. We can interpret reasons as distributions over moral foundations and stances (and vice-versa). This anal
ysis provides a useful way to explain each of these dimensions. For example, we see that care/harm is strongly correlated with reasons such as covid is real, the vaccine works, and natural immunity is ineffective. Other expected trends emerge, such as purity/degradation being highly correlated with vaccine against religion. To evaluate the modeling advantage of our opinion analysis framework, we look at the correlation between stance, moral foundations and topics extracted in an unsupervised fashion using Latent Dirichlet Allocation (LDA) (Blei et al., 2003). We find that the reasons extracted interactively have higher correlations with both vaccination stance and moral foundations. The LDA correlation matrices can be seen in Figure 3.
In Table 5 we show the top four reasons for fairness/cheating. We choose this moral foundation given that it is evenly split among stances and it is active for different reasons. We show the top two (E,R,P) tuples for each reason. We can appreciate that while this moral foundation is used by people on both sides, the reasons offered and entities used vary. On the anti-vax side, authority figures and vaccine trials are portrayed as negative actors, while women and children are portrayed as targets. On the pro-vax side, Covid and unvaccinated people are portrayed as negative actors, and the general public is portrayed as a target.
Unlabeled Covid-19 vaccine corpus In addition to our annotated dataset, we collected a corpus of 85,000 tweets in English mentioning the pandemic vaccine, uniformly distributed between January and October of 2021. These tweets are unlabeled, and are used to ground arguments (Section 2) and to augment data for indirect supervision (Section 5).
(a) LDA Topics and Vax Stance
Figure 3: Correlation Heatmaps for LDA Topics
(b) LDA Topics and Moral Foundations
VAXNOTOPPRESSION	VAXDANGER
70% Pro-Vax (responsible people, target, neg) (un-vax people, actor, neg)	60% Anti-Vax (pregnant women, target, neg) (trial vax, actor, neg)
GOVDISTRUST	VAXWORKS
75% Anti-Vax (children, target, neg) (Fauci, actor, neg)	75% Pro-Vax (people, target, neg) (COVID, actor, neg)
Table 5: Top 4 reasons for Fairness/Cheating, and their most frequent opinions and entity roles
5 Joint Probabilistic Model
We propose a joint probabilistic model that reasons about the arguments made, their morality frames, stances, reasons, and the dependencies between them. We implement our model using DRaiL (Pacheco and Goldwasser, 2021), a declarative modeling framework for specifying deep relational models. Deep relational models combine the strengths of deep neural networks and statistical relational learning (SRL) to model a joint distribution over relational data. This hybrid modeling paradigm allows us to leverage expressive textual encoders, and to introduce contextualizing information and model different interdependent decisions. SRL methods have proven effective to model domains with limited supervision (Johnson and Goldwasser, 2018; Subramanian et al., 2018), and approaches that combine neural networks and SRL have shown consistent performance improvements (Widmoser et al., 2021; Roy et al., 2021).
Following the conventions of statistical relational learning models, we use horn-clauses of the form  $p_0 \wedge p_1 \wedge \ldots \wedge p_n \Rightarrow h$  to describe relational properties. Each logical rule defines a probabilistic scoring function over the relations expressed in its
body and head.
Base rules/classifiers We define three base rules to score whether a tweet  $\mathfrak{t}_{\mathrm{i}}$  has a moral judgment, what is its prominent moral foundation  $\mathfrak{m}$ , and what is its vaccination stance.
$$
r _ {0}: \operatorname {T r e e t} \left(t _ {i}\right) \Rightarrow \operatorname {I s M o r a l} \left(t _ {i}\right)
$$
$$
r _ {1}: \operatorname {T w e e t} \left(t _ {i}\right) \Rightarrow \operatorname {H a s M F} \left(t _ {i}, m\right) \tag {1}
$$
$$
r _ {2}: \operatorname {T r e e t} \left(t _ {i}\right) \Rightarrow \operatorname {V a x S t a n c e} \left(t _ {i}, s\right)
$$