Misinformation has recently become a well-documented matter of public concern. Existing studies on this topic have hitherto adopted a coarse concept of misinformation, which incorporates a broad spectrum of story types ranging from political conspiracies to misinterpreted pranks. This paper aims to structurize these misinformation stories by leveraging fact-check articles. Our intuition is that key phrases in a fact-check article that identify the misinformation type(s) ( e.g., doctored images, urban legends) also act as rationales that determine the verdict of the fact-check ( e.g., false). We experiment on rationalized models with domain knowledge as weak supervision to extract these phrases as rationales, and then cluster semantically similar rationales to summarize prevalent misinformation types. Using archived fact-checks from Snopes.com, we identify ten types of misinformation stories. We discuss how these types have evolved over the last ten years and compare their prevalence between the 2016/2020 US presidential elections and the H1N1/COVID-19 pandemics. Misinformation has raised increasing public concerns globally, well-documented in Africa (Ahinko-rah et al., 2020), Asia (Kaur et al., 2018), and Europe (Fletcher et al., 2018). In the US, fake news accounted for 6% of all news consumption during the 2016 US presidential election (Grinberg et al., 2019). Years later, 29% of US adults in a survey believed that the exaggerated threat of the COVID-19 pandemic purposefully damaged former US president Donald Trump (Uscinski et al., 2020), and 77% of Trump's supporters believed voter fraud manipulated the 2020 US presidential election in spite of a complete lack of evidence (Pennycook and Rand, 2021). As such misinformation continues to threaten society, researchers have started investigating this multifaceted problem, from understanding the socio-psychological foundations of susceptibility (Bakir and McStay, 2018) and measuring public responses (Jiang and Wilson, 2018; Jiang et al., 2020b), to designing detection algorithms (Shu et al., 2017) and auditing countermeasures for online platforms (Jiang et al., 2019, 2020c). These studies mostly adopted the term misin-formation as a coarse concept for any false or inaccurate information, which incorporates a broad spectrum of misinformation stories, e.g., political conspiracies to misinterpreted pranks. Although misinformation types have been theorized and categorized by practitioners (Wardle, 2017), there is, to our knowledge, no empirical research that has systematically measured these prevalent types of misinformation stories. This paper aims to unpack the coarse concept of misinformation and structurize it to fine-grained story types (as illustrated in Figure 1). We conduct this query through an empirical lens and ask the question: what are the prevalent types of misinformation stories in the US over the last ten years? The answer to our question is buried in archived fact-checks, which are specialized news articles that verify factual information and debunk false claims by presenting contradictory evidence (Jiang et al., 2020a). As a critical component of their semi-structured journalistic style, fact-checks often embed the (mis)information type(s) within their steps of reasoning. For example, consider the following snippet from a Snopes.com fact-check with a verdict of false (Evon, 2019): ...For instance, some started sharing a doctored photograph of Thunberg with alt-right boogeyman George Soros (the original photograph featured former Vice President Al Gore)... The key phrase doctored photograph in the snippet identifies the misinformation type of the fact-checked story. Additional example phrases are highlighted in Figure 1. With a large corpus of fact-checks, these phrases would accumulate and reveal prevalent types of misinformation stories. Extracting these phrases is a computational task. Our intuition is that such phrases in a fact-check also act as rationales that determine the verdict of the fact-check. In the previous example, the verdict is false in part because the story contains a doctored photograph . Therefore, a neural model that predicts the verdict of a fact-check would also use the misinformation types as rationales. To realize this intuition, we experiment on existing rationalized neural models to extract these phrases (Lei et al., 2016; Jain et al., 2020), and, to target specific kinds of rationales, we additionally propose to include domain knowledge as weak supervision in the rationalizing process. Using public datasets as validation (Zaidan et al., 2007; Carton et al., 2018), we evaluate the performance variation of different rationalized models, and show that including domain knowledge consistently improves the quality of extracted rationales. After selecting the most appropriate method, we conduct an empirical investigation of prevalent misinformation types. Using archived fact-checks from Snopes.com, spanning from its founding in 1994 to 2021, we extract rationales by applying the selected model with theorized misinformation types for weak supervision (Wardle, 2017), and then cluster rationales based on their semantic similarity to summarize prevalent misinformation types. We identify ten types of misinformation stories, a preview of which are shown in Figure 1. Using our derived lexicon of these clustered misinformation stories, we then explore the evolution of misinformation types over the last ten years. Our key findings include: increased prevalence of conspiracy theories, fabricated content, and digital manipulation; and decreased prevalence of legends and tales, pranks and jokes, mistakes and errors, etc. We also conducted two case studies on notable events that involve grave misinformation. From the case study of US presidential elections, we observe that the most prevalent misinformation type for both the 2016 and 2020 elections is fabricated content, while the 2016 election has more hoaxes and satires. From the case study of pandemics, our results show that the H1N1 pandemic in 2009 has more legends and tales, while the COVID-19 pandemic attracts more conspiracy theories. There is a rich literature that has studied the online misinformation ecosystem from multiple perspectives (Del Vicario et al., 2016; Lazer et al., 2018). Within the computational linguistics community, from an audiences' perspective, Jiang and Wilson (2018) found that social media users expressed different linguistic signals when responding to false claims, and the authors later used these signals to model and measure (dis)beliefs in (mis)information (Jiang et al., 2020b; Metzger et al., 2021). From a platforms' perspective, researchers have assisted platforms in designing novel misinformation detection methods (Wu et al., 2019; Lu and Li, 2020; Vo and Lee, 2018, 2020), as well as audited existing misinformation intervention practices (Robertson et al., 2018; Jiang et al., 2019, 2020c; Hussein et al., 2020). In this work, we study another key player in the misinformation ecosystem, storytellers , and investigate the prevalent types of misinformation told to date. From the storytellers' perspective, Wardle (2017) theorized several potential misinformation types ( e.g., satire or parody, misleading content, and false connection), yet no empirical evidence has been connected to this typology. Additionally, researchers have investigated specific types of misinformation as case studies, e.g., state-sponsored disinformation (Starbird et al., 2019; Wilson and Starbird, 2020), fauxtography (Zannettou et al., 2018; Wang et al., 2021), and conspiracy theories (Samory and Mitra, 2018; Phadke et al., 2021). In this paper, we aim to structurize these misinformation stories to theorized or novel types. Realizing our intuition (as described in  1) requires neural models to (at least shallowly) reason about predictions. In this section, we introduce existing rationalized neural models and propose to include domain knowledge as weak supervision in the rationalizing process. We then experiment with public datasets and lexicons for evaluation. In a standard text classification problem, each instance is in a form of ( x , y ) . x = [ x i ]  V lx is the input token sequence of length l , where V x is the vocabulary of the input and i is the index of each token x i . y  { 0 , 1 } m is the binary label of length m . Rationalization requires a model to output the prediction  y together with a binary mask z = [ z i ]  { 0 , 1 } l of input length l , indicating which tokens are used ( i.e., z i = 1 ) to make the decision. These tokens are called rationales . Hard rationalization requires a model to directly output z . Initially proposed by Lei et al. (2016), the model first passes the input x to a tagger 1 module and samples a binary mask z from a Bernoulli distribution, i.e., z  Tagger ( x ) , and then uses only unmasked tokens to make a prediction of y , i.e.,  y = Predictor ( z , x ) . 2 The loss function of this method contains two parts. The first part is a standard loss for the prediction L y (  y , y ) , which can be realized using common classification loss, e.g., cross entropy. The second part is a loss L z ( z ) 3 aiming to regularize z and encourage conciseness and contiguity of rationale selection, formulated by Lei et al. (2016). Recent work proposed to improve the initial model with an adversarial component (Yu et al., 2019; Carton et al., 2018). Combining these parts together, the 1 This module was named generator by Lei et al. (2016). We name it tagger to distinguish it from the NLG problem. 2 This module was named encoder by Lei et al. (2016). We name it predictor , consistent with Yu et al. (2019), to distinguish it from the encoder-decoder framework. 3 L z ( z ) is a simplified term; we discuss its detailed implementation in Appendix A. model is trained end-to-end using reinforce-style estimation (Williams, 1992), as sampling rationales is a non-differentiable computation. The modules of hard rationalization are illustrated in Figure 2. Soft rationalization , in contrast, allows a model to first output a continuous version of importance scores s = [ s i ]  R l , and then binarize it to get z . Initially formalized by Jain et al. (2020) as a multiphase method, the model first conducts a standard text classification using a supporter module  y = Supporter ( x ) and outputs importance scores s , then binarizes s using a tagger module, i.e., z = Tagger ( s ) , and finally uses only unmasked tokens of x to make another prediction  y to evaluate the faithfulness of selected rationales. 4 These three modules are trained separately in three phases. 5 Since the supporter and predictor are standard text classification modules the only loss needed is for the prediction L y (  y , y ) . This method is more straightforward than the hard rationalization method, as it avoids non-differentiable com-4 The second and third modules were named extractor and classifier by Jain et al. (2020). We continue using tagger and predictor to align with the hard rationalization method. putations and the instability induced by reinforce-style estimation. The modules of soft rationalization are also illustrated in Figure 2. The popular attention mechanism (Bahdanau et al., 2014) provides built-in access to s . Although there have been debates on the properties achieved by attention-based explanations (Jain and Wallace, 2019; Wiegreffe and Pinter, 2019; Serrano and Smith, 2019), rationales extracted by straightforward rules on attention weights were demonstrated as comparable to human-generated rationales (Jain et al., 2020). Additionally, in our use case we only need the rationales themselves as key phrases and do not require them to faithfully predict y , therefore the last predictor module can be omitted. Both hard and soft rationalization methods can be trained with or without supervision w.r.t. rationales z (DeYoung et al., 2020) 6 . When rationales are selected in an unsupervised manner, the model would intuitively favor rationales that are most informative to predict the corresponding label as a result of optimizing the loss function. This could result in some undesirable rationales in our case: for example, certain entities like COVID-19 or Trump that are highly correlated with misinformation would be selected as rationales even though they do not suggest any misinformation types. Therefore, we propose to weakly supervise 7 the rationalizing process with domain knowledge to obtain specific, desired types of rationales. Assuming a lexicon of vocabulary V d as domain knowledge, we reprocess the input and generate weak labels for rationales z d = [ z i d ]  { 0 , 1 } l where z i d = 1 ( i.e., unmasked) if x i  V d and z i d = 0 ( i.e., masked) otherwise. Then, we include an additional loss item L d ( z , z d ) or L d ( s , z d ) for the hard or soft rationalization method. Combining the loss items together, the objective for the end-to-end hard rationalization model is: min  L y (  y , y ) +  z L z ( z ) +  d L d ( z , z d ) , where  contains the parameters to estimate and  (  ) are hyperparameters weighting loss items. Similarly, the objective function for the first phase of soft rationalization is: min  L y (  y , y ) +  d L d ( s , z d ) . 6 They are trained with supervision w.r.t. the label y . 7 Since there is inherently no ground-truth of misinformation types in fact-check articles. We conduct experiments on public datasets to evaluate the performance of hard and soft rationalization methods, particularly for our needs, and confirm that including domain knowledge as weak supervision helps with the rationalizing process. Datasets selection. An ideal dataset for our models should meet the following requirements: (a) formulated as a text classification problem, (b) annotated with human rationales, and (c) can be associated with high quality lexicons to obtain domain knowledge. We select two datasets based on these criteria: the movie reviews dataset released by Pang et al. (2002) and later annotated with rationales by Zaidan et al. (2007), which contains 2K movie reviews labeled with positive or negative sentiments; and the personal attacks dataset released by Wulczyn et al. (2017) and later annotated with rationales by Carton et al. (2018), which contains more than 100K Wikipedia comments labeled as personal attacks or not. Domain knowledge. For the sentiment analysis on movie reviews, we use the EmoLex lexicon released by Mohammad and Turney (2013), which contains vocabularies of positive and negative sentiments. For identifying personal attacks, we use a lexicon released by Wiegand et al. (2018), which contains a vocabulary of abusive words. With corresponding vocabularies, we generate weak rationale labels z d for each dataset. Evaluation metrics. We choose binary precision Pr ( z ) to evaluate the quality of extracted rationales, because (a) a perfect recall can be trivially achieved by selecting all tokens as rationales, 8 and (b) our case of identifying key phrases requires concise rationales. Additionally, we measure the average percentage of selected rationales over the input length % ( z ) . For predictions, we use macro F 1 ( y ) as the evaluation metric as well as the percentage of information used % ( x ) to make the prediction. Experimental setup and results. The train, dev, and test sets are pre-specified in public datasets. We optimize hyperparameters for F 1 ( y ) on the dev sets, and only evaluate rationale quality Pr ( z ) after a model is decided. We discuss additional implementation details ( e.g., hyperparameters, loss functions, module cells) in Appendix  A. 8 We later show that this is the default model behavior if rationale selection is under-regularized. The evaluation results for all our experiments on test sets are reported in Table 1, indexed with h 0 -h 3 and s 0 -s 3 . We report the evaluation results on dev sets in Appendix  B. Regularization for hard rationalization. h 0 and h 2 are our re-implementation of Lei et al. (2016), varying the rationale regularization hyperparameter  z . Our experiments show that  z is a crucial choice. When a small  z is chosen ( i.e., rationales are under-regularized), the model has a tendency to utilize all the available information to optimize the predictive accuracy. In h 2 , we set  z = 0 and the model selects 99.9% of tokens as rationales while achieving the best F 1 ( y ) overall, which is an undesirable outcome in our case. Therefore, we increases  z so that only small parts of tokens are selected as rationales in h 0 . However, echoing Jain et al. (2020), the output when varying  z is sensitive and unpredictable, and searching for this hyperparameter is both time-consuming and energy-inefficient. We also run an experiment h 3 with the additional adversarial component proposed in (Carton et al., 2018; Yu et al., 2019), and the evaluation metrics are not consistently improved compared to h 0 . Binarization for soft rationalization. s 0 , s 2 and s 3 are our re-implementation of Jain et al. (2020). For soft rationalization, rationales are selected ( i.e., binarized) after the supporter module is trained in phase one, therefore s 0 -s 3 utilize 100% of the tokens by default, and achieve the best F 1 ( y ) overall. We implement a straightforward approach to select rationales by setting a threshold t and make z i = 1 ( i.e., unmasked) if the importance score s i > t and z i = 0 ( i.e., masked) otherwise. Intuitively, increasing t corresponds to less selected rationales, and therefore increasing Pr ( z ) . To confirm, in s 2 , we increase t until % ( z ) is exactly half of s 0 . Similarly, decreasing t corresponds to more selected rationales, and therefore decreasing Pr ( z ) . In s 3 , we decrease t until % ( z ) is exactly double of s 0 . Is domain knowledge helpful? h 1 and s 1 include domain knowledge as weak supervision. Our results show that domain knowledge improves Pr ( z ) for both hard (h 1 to h 0 ) and soft (s 1 to s 0 ) rationalization methods and on both dataset, while maintaining similar % ( z ) and F 1 ( y ) . The improvements are more substantial for soft rationalization. Hard vs. soft rationalization. To fairly compare hard and soft rationalization methods, we choose the threshold t to keep % ( z ) the same for h 1 and s 1 . 9 Our experiments show that soft rationalization weakly supervised by domain knowledge achieves better Pr ( z ) on both datasets, and therefore we chose it for rationalizing fact-checks. After determining that soft rationalization is the most appropriate method, we apply it to extract rationales from fact-checks. In this section, we introduce the dataset we collected from Snopes.com and conduct experiment with fact-checks to structurize misinformation stories. 9 We can easily and accurately manipulate % ( z ) for soft rationalization by adjusting t ; conversely, the impact of adjusting  z in hard rationalization is unpredictable. 2018). We collect HTML webpages of fact-check articles from Snopes.com, spanning from its founding in 1994 to the beginning of 2021. Preprocess and statistics. We first preprocess collected fact-checks by extracting the main article content and verdicts from HTML webpages using a customized parser, and tokenizing the content with NLTK (Bird, 2006). The preprocessing script is included in our released codebase. After preprocessing, the median sequence length of fact-checks is 386 tokens, and 88.6% of fact-checks containing  1,024 tokens. Jiang et al. (2020a) found that the most informative content in fact-checks tended to be located at the head or the tail of the article content. Therefore, we set the maximum sequence length to 1,024 and truncate over-length fact-checks. Next, we label each fact-check with a binary label depending on its verdict: (truthful) information if the verdict is at least mostly true and misinformation otherwise, which results in 2,513 information and 11,183 misinformation instances. Additionally, we preemptively mask tokens that are the exact words as its verdict ( e.g., rate it as false to rate it as [MASK] ), 10 otherwise predicting the verdict would be trivial and the model would copy overlapping tokens as rationales. Domain knowledge for misinformation types. The domain knowledge comes from two sources: (a) the misinformation types theorized by Wardle (2017), e.g., misleading or fabricated content; and (b) certain variants of verdicts from Snopes.com such as satire or scam (Snopes.com, 2021a). We combine these into a small vocabulary V d containing 12 words, listed in Appendix  A. 4.2 Experiments and Results We randomly split the fact-checks to 80% train, 10% dev, and 10% test sets, and adjust hyperparameters to optimize F 1 ( y ) on dev set. For initialization, we train word embeddings using Gensim (Re-hurek and Sojka, 2011) on the entire corpus. The final model achieves F 1 ( y ) = 0 . 75 / 0 . 74 on the test set with/without domain knowledge. Clustering rationales. To systematically understand extracted rationales, we cluster these rationales based on semantic similarity. For each rationale, we average word embeddings to represent 10 Verdicts from Snopes.com are structured HTML fields that can be easily parsed. the embedding of the rationale, and then run a hierarchical clustering for these embeddings. The hierarchical clustering uses cosine similarity as the distance metric, commonly used for word embeddings (Mikolov et al., 2013), and the complete link method (Voorhees, 1986) to obtain a relatively balanced linkage tree. The results from the clustering are shown in Figure 3. From the root of the dendrogram, we can traverse its branches to find clusters until we reach a sensible threshold of cosine distance, and categorize the remaining branches and leaf nodes ( i.e., rationales) to multiple clusters. Figure 3 shows an example visualization that contains ten clusters of rationales that are semantically similar to the domain knowledge, and leaf nodes in each cluster are aggregated to plot a word cloud, with the frequency of a node encoded as the font size of the phrase. Note that rationales extracted from soft rationalization are dependent on the chosen threshold t to binarize importance scores. The example in Figure 3 uses a threshold of t = 0 . 01 . Varying the threshold would affect extracted rationales but mostly the ones with low prevalence, and these rare rationales also correspond to small font sizes in the word cloud. Therefore, the effect from varying t would be visually negligible in Figure 3. Structure of misinformation stories. First, the clusters empirically confirm existing domain knowledge in V d . Certain theorized misinformation types, such as satires and parodies (cid:4) from (Wardle, 2017), are identified as individual clusters from fact-checks. Second, the clusters complement V d with additional phrases describing (semantically) similar misinformation types. For example, our results add humor and gossip to the same category as satires and parodies (cid:4) and add tales and lore to the same category as legends (cid:4) . This helps us grasp the similarity between misinformation types, and also enriches the lexicon V d , which proves useful for subsequent analysis in  5. Third, we discover novel, fine-grained clusters that are not highlighted in V d . There are multiple possible explanations as to why these misinformation types form their own clusters. Conspiracy theories (cid:4) are often associated with intentional political campaigns (Samory and Mitra, 2018) which can affect their semantics when referenced in fact-checks. In contrast, digital alteration (cid:4) is a relatively recent misinformation tactic that has been enabled by technological developments such as FaceSwap (Ko-rshunova et al., 2017) and DeepFake (Westerlund, 2019). Hoaxes and pranks (cid:4) often have a mischievous intent that distinguishes them from other clusters. Other new clusters include clickbait with inflammatory and sensational language (cid:4) and entirely fictional content (cid:4) . Fourth, the clusters reorganize the structure of these misinformation types based on their semantics, e.g., fabricated and misleading content (cid:4) belongs to two types of misinformation in (Wardle, 2017), while in our results they are clustered together. This suggests that the semantic distance between fabricated and misleading content is less than the chosen similarity threshold, at least when these misinformation types are referred to by fact-checkers when writing articles. Finally, the remaining words in V d are also found in our rationales. However, due to low prevalence, they are not visible in Figure 3 and do not form their own clusters. In this section, we leverage the clusters of misinformation types identified by our method as a lexicon and apply it back to the our original fact-check dataset. Specifically, we analyze the evolution of misinformation types over the last ten years and compare misinformation trends around major real-world events. Evolution over the last ten years. We first explore the evolution of misinformation over time. We map each fact-check article with one or more corresponding misinformation types identified by our method, and then aggregate fact-checks by year from before 2010 11 to the end of 2020 to estimate the relative ratio of each misinformation type. As shown in Figure 4, 12 the prevalence of certain misinformation types on Snopes.com has drastically changed over the last ten years. Heavily politicized misinformation types, such as digitally altered or doctored images or photographs (cid:4) , fabricated and misleading content (cid:4) , and conspiracy theories (cid:4) have nearly doubled in relative ratios over the last ten years. In contrast, the prevalence of (arguably) less politicized stories, such as legends and tales (cid:4) , hoaxes and pranks (cid:4) , and mistakes and errors (cid:4) have decreased. These trends may be a proxy for the underlying prevalence of different misinformation types within the US. Studies that measure political ideologies 11 Since there are relatively few fact-checks before 2010, we aggregate them together to the year 2010. 12 95% confidence intervals. Additionally, the convenience offered by modern digital alteration software and applications (Korshunova et al., 2017; Westerlund, 2019) provides a gateway to proliferating manipulated images or photographs in the misinformation ecosystem. Alternatively, these trends may reflect shifts in Snopes.com's priorities. The website, launched in 1994, was initially named Urban Legends Reference Pages . Since then it has grown to encompass a broad spectrum of subjects. Due to its limited resources, fact-checkers from Snopes.com only cover a subset of online misinformation, and their priority is to fact-check whatever items the greatest number of readers are asking about or searching for at any given time (Snopes.com, 2021b). 13 Given the rising impact of political misinformation in recent years (Zannettou et al., 2019, 2020), such misinformation could reach an increasing number of Snopes.com readers, and therefore the website may dedicate more resources to fact-checking related types of misinformation. Additionally, Snopes.com has established collaborations with social media platforms, e.g., Facebook (Green and Mikkelson), to specifically target viral misinformation circulating on these platforms, where the rising meme culture could also attract Snopes.com's attention and therefore explain a surge of digitally altered images (Ling et al., 2021; Wang et al., 2021). 13 Users can submit a topic to Snopes.com on its contact page (Snopes.com, 2021c), the results from which may affect Snopes.com's priorities. 2016 vs. 2020 US presidential election. We now compare misinformation types between the 2016 and 2020 elections. To filter for relevance, we constrain our analysis to fact-checks that (1) were published in the election years and (2) included the names of the presidential candidates and/or their running mates ( e.g., Joe Biden and Kamala Har-ris). This results in 2,586 fact-checks for the 2016 election and 2,436 fact-checks for 2020. The prevalence of each misinformation type is shown in Figure 5. We observe that the relative ratios of many misinformation types are similar between the two elections, e.g., legends and tales (cid:4) and bogus scams (cid:4) , while the 2016 election has more hoaxes (cid:4) , satires (cid:4) , etc. The most prevalent type during both elections is fabricated and misleading content (cid:4) , next to conspiracy theories (cid:4) . H1N1 vs. COVID-19. Finally, we compare misinformation types between the H1N1 pandemic in 2009 and the COVID-19 pandemic. For H1N1 related fact-checks, we search for keywords flu, influenza, and H1N1 in fact-checks and constrain the publication date until the end of 2012. 14 For COVID-19 related fact-checks, we search for keywords COVID-19 and coronavirus, and only consider fact-checks published in 2019 or later, which results in 833 fact-checks for the H1N1 pandemic and 656 fact-checks for COVID-19. The relative ratio of each misinformation type is also shown in Figure 5. We observe that the prevalence of some misinformation types are sig-14 WHO declared an end to the global 2009 H1N1 pandemic on August 10, 2010, yet misinformation about H1N1 continues to spread (Sundaram et al., 2013), therefore we extend the time window by two more years. Notably, the H1N1 pandemic has many more legends and tales (cid:4) , while COVID-19 has more conspiracy theories (cid:4) . The increased prevalence of COVID-19 related conspiracies aligns with recent work measuring the same phenomena (Uscinski et al., 2020; Jolley and Paterson, 2020), especially as the COVID-19 pandemic becomes increasingly politicized (Hart et al., 2020; Rothgerber et al., 2020; Weisel, 2021). Limitations and future directions. We adopted a computational approach to investigate our research question, and this method inherently shares common limitations with observational studies, e.g., prone to bias and confounding (Benson and Hartz, 2000). Specifically, our corpus contains fact-checks from Snopes.com, one of the most comprehensive fact-checking agencies in the US. Snopes.com covers a broader spectrum of topics than politics-focused fact-checkers ( e.g., Politi-Fact.com, FactCheck.org), 15 and thus we argue that it covers a representative sample of misinformation within the US. However, Snopes.com may not be representative of the international misinformation ecosystem (Ahinkorah et al., 2020; Kaur et al., 2018; Fletcher et al., 2018). In the future, we hope that our method can help characterize misinformation comparatively on a global scale when more structured fact-checks become available. 16 Additionally, fact-checkers are time constrained, as thus the misinformation stories they cover tend to be high-profile. Therefore low-prevalence, long-tail misinformation stories may not be observed in our study. Understanding low-volume misinformation types may require a different collection of corpora other than fact-checks, e.g., a cross-platform investigation on social media conversations (Wilson and Starbird, 2020; Abilov et al., 2021). Lastly, the misinformation types we extract from our weakly supervised approach are not validated with ground-truth labels. This is largely due to the lack of empirical knowledge on misinformation types, and therefore we are unable to provide specific guidance to annotators. Although the clusters in Figure 3 provide straightforward structure of misinformation stories, in future work, we plan to leverage these results to construct annotation guidelines and obtain human-identified misinformation types for further analysis. Conclusion. In this paper, we identify ten prevalent misinformation types with rationalized models on fact-checks and analyze their evolution over the last ten years and between notable events. We hope that this paper offers an empirical lens to the systematic understanding of fine-grained misinformation types, and complements existing work investigating the misinformation problem. This research was supported in part by NSF grant IIS-1553088. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. This paper uses Snopes.com fact-checks to train and validate our models, and also includes several quotes and snippets of fact-checks. We consider our case a fair use under the US 17 copyright law, which permits limited use of copyrighted material without the need for permission from the copyright holder. According to 17 U.S.C.  107, we discuss how our research abides the principles that are considered for a fair use judgment:  Purpose and character of the use: we use fact-checks for noncommercial research purpose only, and additionally, using textual content for model training is considered to be transformative, cf. Authors Guild, Inc. v. Google Inc. (2013, 2015, 2016). Amount and substantiality: we present only snippets of fact-checks for illustrative purpose in our paper ( i.e., several quotes and snippets in text and figures), and only URLs to original fact-checks in our public dataset. Effect upon work's value: we do not identify any adverse impact our work may have on the potential market ( e.g., ads, memberships) of the copyright holder. The end goal of our research aligns with that of Snopes.com, i.e., to rebut misinformation and to restore credibility to the online information ecosystem. We hope the aggregated knowledge of fact-checks from our models can shed light on this road and be a helpful addition to the literature.
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