Social media is a breeding ground for threat narratives and related conspiracy theories. In these, an outside group threatens the integrity of an inside group, leading to the emergence of sharply defined group identities: Insider s  agents with whom the authors identify and Outsider s  agents who threaten the insiders. Inferring the members of these groups constitutes a challenging new NLP task: (i) Information is distributed over many poorly-constructed posts; (ii) Threats and threat agents are highly contextual, with the same post potentially having multiple agents assigned to membership in either group; (iii) An agent's identity is often implicit and transitive; and (iv) Phrases used to imply Outsider status often do not follow common negative sentiment patterns. To address these challenges, we define a novel Insider Outsider classification task. Because we are not aware of any appropriate existing datasets or attendant models, we introduce a labeled dataset (CT5K) and design a model (NP2IO) to address this task. NP2IO leverages pretrained language modeling to classify Insider s and Outsider s. NP2IO is shown to be robust, generalizing to noun phrases not seen during training, and exceeding the performance of non-trivial baseline models by 20% . Narrative models  often succinctly represented as a network of characters, their roles, their interactions ( syuzhet ) and associated time-sequencing information ( fabula )  have been a subject of considerable interest in computational linguistics and narrative theory. Stories rest on the generative backbone of narrative frameworks (Bailey, 1999; Beatty, 2016). While the details might vary from one story to another, this variation can be compressed into a limited set of domain-dependent narrative roles and functions (Dundes, 1962). group identities are an emergent phenomenon resulting from distributed social discourse. Currently, this phenomenon is most readily apparent on social media platforms, with their large piazzas and niche enclaves. Here, multiple threat-centric narratives emerge and, often, over time are linked together into complex conspiracy theories (Tangher-lini et al., 2020). Conspiracy theories, and their constituent threat narratives (legend, rumor, personal experience narrative) share a signature semantic structure: an implicitly accepted Insider group; a diverse group of threatening Outsider s; specific threats from the Outsider directed at the Insider s; details of how and why Outsider s are threatening; and a set of strategies proposed for the Insider s to counter these threats (Tangherlini, 2018). Indeed, the Insider / Outsider groups are fundamental in most studies of belief narrative, and have been exhaustively studied in social theory and more specifically, in the context of conspiracy theories (Bodner et al., 2020; Barkun, 2013). On social media, these narratives are negotiated one post at a time, expressing only short pieces of the immanent narrative whole (Clover, 1986). This gives rise to a new type of computational linguistic problem: Given a large enough corpus of social media text data, can one automatically distill semantically-labeled narratives (potentially several overlapping ones) that underlie the fragmentary conversational threads? Recent work (Shahsavari et al., 2020b; Tangherlini et al., 2020; Shahsavari et al., 2020a; Holur et al., 2021) has shown considerable promise that such scalable automated algorithms can be designed. An automated pipeline of interlocking machine learning modules decomposes the posts into actors, actants and their inter-actant relationships to create narrative networks via aggregation. These network representations are interpretable on inspection , allowing for the easy identification of the various signature semantic structures: Insider s, 4975 Figure 1: A pair of inferred text segments labeled by NP2IO showing Insider-Outsider context-sensitivity: Colored spans are used to highlight noun phrases that are inferred Outsider s, strategies for dealing with Outsider s and their attendant threats and, in the case of conspiracy theories, causal chains of events that support that theory. By itself, this unsupervised platform does not understand the different narrative parts . Since the submodules are not trained to look for specific semantic abstractions inherent in conspiracy theories, the platform cannot automatically generate a semantically tagged narrative for downstream NLP tasks. It cannot, for example, generate a list across narratives of the various outside threats and attendant inside strategies being recommended on a social media forum, nor can it address why these threats and strategies are being discussed. As a fundamental first step bringing in supervised information to enable automated narrative structure discovery, we introduce the Insider Outsider classification task: To classify the noun phrases in a post as Insider , Outsider or neither . A working conceptualization of what we consider Insider s and Outsider s is provided in the following insets. As with most NLP tasks, we do not provide formal definitions of and rules to determine these groups. Instead we let a deep learning model learn the representations needed to capture these notions computationally by training on data annotated with human-generated labels. Insiders: Some combination of actors and their associated pronouns, who display full agency (people, organizations, govern-ment), partial agency (policies, laws, rules, current events) or no agency (things, places, circumstances), with whom the author identifies (including themselves). These are often ascribed beneficial status; Outsiders: A set of actors whom the author opposes and, in many cases, perceives as threatening the author and the insiders with disruption or harm. For our purposes, these agents need not have full agency : Diseases and natural disasters, for example, would be universal outsiders, and any man-made object/policy that works against the Insider s would be included in this group. different categories is inspired by social categorization, identification and comparison in the well-established Social Identity Theory (SIT) (Tajfel et al., 1979; Tajfel, 1974) and rests on established perspectives from Narrative Theory (Dundes, 1962; Labov and Waletzky, 1967; Nicolaisen, 1987). Following are some of the reasons why this classification task is challenging and why the concepts of Insider s/ Outsider s are not sufficiently captured by existing labeled datasets used in Sentiment 4976 Analysis 1. Commonly-held Beliefs and Worldviews: Comprehensively incorporating shared values, crucial to the classification of Insider s and Outsider s, is a task with varied complexity. Some beliefs are easily enumerated: most humans share a perception of a nearly universal set of threats (virus, bomb, cancer, dictatorship) or threatening actions (kills millions of people, tries to mind-control every-one) or benevolent actions (donating to a charitable cause, curing disease, freeing people). Similarly, humans perceive themselves and their close family units as close, homogeneous groups with shared values, and therefore I, us, my children and my family are usually Insider s. In contrast, they and them are most often Outsider s. Abstract beliefs pose a greater challenge as the actions that encode them can be varied and subtle. For example, in the post: The microchips in vaccines track us, the noun phrase microchips is in the Outsider category as it violates the Insider s' right to privacy by track[ing] us. Thus, greater attention needs to be paid in labeling datasets, highlighting ideas such as the right to freedom, religious beliefs, and notions of equality. 2. Contextuality and Transitivity: People express their opinions of Insider/Outsider affiliation by adding contextual clues that are embedded in the language of social media posts. For example, a post We should build cell phone towers suggests that cell phone towers are helpful to Insider s, whereas a post We should build cell phone towers and show people how it fries their brains suggests, in contrast, that cell phone towers are harmful to Insiders and belong, therefore, to the class of Outsider s. Insider/Outsider affiliations are also implied in a transitive fashion within a post. For example, consider two posts: (i) Bill Gates is developing a vaccine. Vaccines kill people. and (ii) Bill Gates is developing a vaccine. Vaccines can eradicate the pandemic. In the first case, the vaccine's toxic quality and attendant Outsider status would transfer to Bill Gates, making him an Outsider as well; in the second post, vaccine's beneficial qualities would transfer to him, now making Bill Gates an Insider . 3. Model Requirement under Biased Data Conditions: Designing effective classifiers that do not inherit bias from the training data  especially data in which particular groups or individuals are derided or dehumanized  is a challenging but necessary task. Because conspiracy theories evolve, building on earlier versions, and result in certain communities and individuals being othered, our models must learn the phrases, contexts, and transitivity used to ascribe group membership, here either Insider s or Outsider s and not memorize the communities and/or individuals being targeted. Figure 1 illustrates an example where we probed our model to explore whether such a requirement is indeed satisfied. The first text conforms to the bias in our data, where tech, Bill Gates, and vaccines are primarily Outsiders . The second text switches the context by changing the phrases. Our classifier is able to correctly label these same entities, now presented in a different context, as Insider s! We believe that such subtle learning is possible because of the use of pretrained language models. We provide several such examples in Table 3 and Figure 3 and also evaluate our model for Zero-shot learning in Table 1 and Figure 6. Recent NLP efforts have examined the effectiveness of using pretrained Language Models (LM) such as BERT, DistilBERT, RoBERTa, and XLM to address downstream classification tasks through fine-tuning (Sanh et al., 2020; Liu et al., 2019; Lample and Conneau, 2019). Pretraining establishes the contextual dependencies of language prior to addressing a more specialized task, enabling rapid and efficient transfer learning. A crucial benefit of pretraining is that, in comparison to training a model from scratch, fewer labeled samples are necessary. By fine-tuning a pretrained LM, one can subsequently achieve competitive or better performance on an NLP task. As discussed in Section 2, since our model is required to be contextual and transitive , both of which are qualities that rely on the context embedded in language, we utilize a similar architecture. In recent work involving span-based classification tasks, token-classification heads have proven to be very useful for tasks such as, Parts-of-Speech (POS) Tagging, Named Entity Recognition (NER) and variations of Sentiment Analysis (SA) (Yang et al., 2019; Vlad et al., 2019; Yin et al., 2020). Since the Insider Outsider classification task is also set up as a noun phrase labeling task, our architecture uses a similar token-classification head on top of the pretrained LM backbone. Current SA datasets' definitions of positive negative and neutral sentiments can be thought of as a particularized form of the Insider-Outsider classification task. For example, among the popular datasets used for SA, Rotten Tomatoes, Yelp reviews (Socher et al., 2013) and others (Dong et al., 2014; Pontiki et al., 2014) implicitly associate a sentiment's origin to the post's author (source) (a single Insider ) and its intended target to a movie or restaurant (a single Outsider if the sentiment is negative or an Insider if positive ). The post itself generally contains information about the target and particular aspects that the Insider found necessary to highlight.Inmorerecent SA work, such as Aspect-Based Sentiment Analysis (ABSA) (Gao et al., 2021; Li et al., 2019; Wang et al., 2021; Dai et al., 2021), researchers have developed models to extract sentiments  positive, negative, neutral  associated with particular aspects of a target entity . One of the subtasks of ABSA, aspect-level sentiment classification (ALSC), has a form that is particularly close to the Insider Outsider classification. Interpreted in the context of our task, the author of the post is an Insider although now there can potentially be multiple targets or aspects that need to be classified as Insider s and Outsider s. Still, the constructed tasks in ABSA appear to not align well with the goal of Insider Outsider classification: 1) Datasets are not transitive : Individual posts appear to have only one agent that needs classification, or a set of agents, each with their own separate sets of descriptors; 2) The ALSC data is often at the sentence-level as opposed to post-level, limiting the context-space for inference. Despite these obvious differences, we quantitatively verify our intuitions in Section 7.1, and show that ABSA models do not generalize to our dataset. Closely related to ABSA is Stance Classification (SC) (also known as Stance Detection / Iden-tification), the task of identifying the stance of the text author ( in favor of , against or neutral ) toward a target (an entity, concept, event, idea, opinion, claim, topic, etc.)(Walker et al., 2012; Zhang et al., 2017; Kk and Can, 2021). Unlike ABSA, the target in SC does not need to be embedded as a span within the context. For example, a perfect SC model given an input for classification of context: This house would abolish the monarchy. and target: Hereditary succession , would predict the Negative label (Bar-Haim et al., 2017; Du et al., 2017). While SC appears to require a higher level of abstraction and, as a result, a model of higher complexity and better generalization power than those typically used for ABSA, current implementations of SC are limited by the finite set of queried targets; in other words, SC models currently do not generalize to unseen abstract targets. Yet, in real-time social media, potential targets and agents exhibit a continuous process of emergence, combination and dissipation. We seek to classify these shifting targets using the transitive property of language, and would like the language to provide clues about the class of one span relative to another. Ultimately, while SC models are a valuable step in the direction of better semantic understanding, they are ill-suited to our task. Parallel to this work in SA, there are complementary efforts in consensus threat detection on social media (Wester et al., 2016; Kandias et al., 2013; Park et al., 2018), a task that broadly attempts to classify longer segments of text  such as comments on YouTube or tweets on Twitter  as more general threats. The nuanced instruction to the labelers of the data is to identify whether the author of the post is an Outsider from the labeler's perspective as an Insider . Once again, we observe that this task aligns with the Insider Outsider paradigm, but does not exhaust it, and the underlying models cannot accomplish our task. The sets of Insider s and Outsider s comprise a higher-order belief system that cannot be adequately captured with the current working definitions of sentiment nor the currently available datasets. This problem presents a primary motivation for creating a new dataset . For example, the post: Microchips are telling the government where we are, does not directly feature a form of prototypical sentiment associated with microchips, the government and we, yet clearly insinuates an invasion on our right to privacy making clear the Insider s (we) and Outsider s (microchips, the government) in the post. To construct our novel dataset  C onspiracy T heory-5000 ( CT5K )  we designed crawlers to extract a corpus of social media posts generated by the underlying narrative framework of vaccine hesitancy (Details of the crawlers are documented in Appendix A.1). Vaccine hesitancy is a remarkably resilient belief fueled by conspiracy theories that overlaps with multiple other narratives including ones addressing depopulation, government 4978 overreach and the deep state, limits on freedom of choice and Satanism. The belief's evolution on social media has already enabled researchers to take the first steps in modeling critical parts of the underlying generative models that drive antivaccination conversations on the internet (Tangher-lini et al., 2016; Bandari et al., 2017). Moreover, vaccine hesitancy is especially relevant in the context of the ongoing COVID-19 pandemic (Burki, 2020).Onthe crawled corpus, we extract the noun-chunks from each post using SpaCy's noun chunk extraction module and dependency parsers (Hon-nibal and Johnson, 2015). A noun chunk is a subtree of the dependency parse tree, the headword of which is a noun. The result is a set of post-phrase pairs, ( p , n ) , where p is a post and n is one of the noun phrases extracted from the post. Amazon Mechanical Turk (AMT) (see Appendix A.2 for labeler instructions) was used to label the post-phrase pairs. For each pair, the labeler was asked, given the context , whether the writer of the post p perceives the noun phrase n to be an Insider , Outsider or neither (N/A). The labeler then provides a label c  C , where C = { Insider , Outsider , N/A } (hence |C| = 3 ). The triplets of post-phrase pairs along with their labels form the dataset D = (cid:8)(cid:0) ( p i , n i ) , c i (cid:1)(cid:9) |D| i =1 . Note that a single post can appear in multiple triplets, because multiple different noun phrases can be extracted and labeled from a single post. The overall class distribution and a few conditional class distributions across the labeled samples for several particular noun phrases are provided in Figure 5 in the Appendix B. Manual inspection of the labeled samples (( p , n ) , c ) suggests that the quality of the dataset is good ( < 10% misclassified by random sam-pling). The now-labeled CT5K dataset (Holur et al., 2022) 1 ( |D| = 5000 samples) is split into training ( 90% ), and 10% testing sets. 10% of the training set is held out for validation. The final training set is 20 -fold augmented by BERT-driven multi-token insertion (Ma, 2019). The N ounP hrase-toI nsider O utsider (NP2IO) model 2 adopts a token classification architecture comprising a BERT-like pre-trained backbone and a softmax classifier on top of the backbone. Token-1 See: Data and Model Checkpoints 2 Code Repository: NP2IO level labels are induced from the span-level labels for the fine-tuning over CT5K, and the span-level labeling of noun phrases is done through majority vote during inference. An outline of the fine-tuning pipeline is provided in Figure 2. Given a labeled example (( p , n ) , c ) , the model labels each token t i in the post p = [ t 1 , . . . , t N ] , where N is the number of tokens in the post p . The BERT-like backbone embeds each token t i into a contextual representation  i  R d (for example, d = 768 for BERT-base or RoBERTa-base). The embedding is then passed to the softmax classification layer  i  Softmax( WT  i + b ) (1) where  i   |C| is the Insider Outsider classification prediction probability vector of the i th token, and W  R d |C| and b  R |C| are the parameters of the classifier. The ground truth class label c accounts for all occurrences of the noun phrase n in the post p . We use this span-level label to induce the token-level label and facilitate the computation of the fine-tuning loss. Concretely, consider the spans where the noun phrase n occurs in the post p : S n = { s 1 , . . . , s M } , where s j  S n denotes the span of the j th occurrence of n , and M is the number of occurrences of n in p . Each span is a sequence of one or more tokens. The set of tokens appearing in one of these labeled spans is: T n = { t  p |  s  S n s.t. t  s } . We define the fine-tuning loss L of the labeled example (( p , n ) , c ) as the cross-entropy (CE) loss computed over T n using c as the label for each token in it, i : t i  T n where (  i ) c denotes the prediction probability for the class c  C of the i th token. The fine-tuning is done with mini-batch gradient descent for the classification layer and a number of self-attention layers in the backbone. The number of fine-tuned self-attention layers is a hyperparameter. The scope of hyperparameter tuning is provided in Table 4. During fine-tuning, we extend the label of a noun phrase to all of its constituent tokens; during inference, conversely, we summarize constituent token labels to classify the noun phrases by a majority vote. For a pair of post and noun-phrase ( p , n ) , assuming the definition of { t i } Ni =1 , {  i } Ni =1 and T n from the Section 5.1, the Insider Outsider label prediction  c is given by  c = arg max k (cid:88) i : t i  T n 1 { k =(arg max  (  i )  ) } . Now c can be compared to  c with a number of classification evaluation metrics. Visual display of individual inference results such as those in Figure 1 are supported by displaCy (Honnibal and Mon-tani, 2017). In this section, we list baselines that we compare to our model's performance ordered by increasing parameter complexity. Random Model (RND): Given a sample from the testing set { p , n } ,  c is randomly selected with uniform distribution from C = { Insider , Outsider , N/A } . post-phrase pair ( p , n ) , give a fixed classification prediction:  c = Insider (DET-I),  c = Outsider (DET-O) or  c = N/A (DET-NA). Nave Bayes Model (NB / NB-L): Given a training set, the nave Bayes classifier estimates the likelihood of each class conditioned on a noun chunk PC , N ( c | n ) assuming its indepen-dence w.r.t. the surrounding context. That is, a noun phrase predicted more frequently in the training-set as an Insider will be predicted as an Insider during the inference, regardless of the context. For noun phrases not encountered during training, the uniform prior distribution over C is used for the prediction. The noun chunk may be lemmatized (by word) during training and testing to shrink the conditioned event space. We abbreviate the nave Bayes model without lemmatization as NB, and the one with lemmatization as NB-L. GloVe+CBOW+XGBoost (CBOW 1/2/5): This baseline takes into account the context of a post but uses global word embeddings, instead of contextual-embeddings. A window length w is fixed such that for each noun phrase, we extract the w words before and w words after the noun phrase, creating a set of context words, S w . Stopwords are filtered, and the remaining con-4980 text words are lemmatized and encoded via 300 dimensional GloVe (Pennington et al., 2014). The Continuous Bag of Words (CBOW) model (Mikolov et al., 2013) averages the representative GloVe vectors in S w to create an aggregate contextual vector for the noun phrase. XGBoost (Chen and Guestrin, 2016) is used to classify the aggregated contextual vector. The same model is applied on the test set to generate labels. We consider window lengths of 1 , 2 and 5 (CBOW-1, CBOW-2 and CBOW-5 respectively). 7 Results and Evaluation Comparison of NP2IO to baselines is provided in Table 1. The random (RND) and deterministic (DET-I, DET-O, DET-NA) models perform poorly. We present these results to get a better sense of the unbalanced nature of the labels in the CT5K dataset (see Figure 5). The nave Bayes model (NB) and its lemmatized form (NB-L) outperform the trivial baselines. However, they perform worse than the two contextual models, GloVe+CBOW+XGBoost and NP2IO. This fact validates a crucial property of our dataset: Despite the bias in the gold standard labels for particular noun phrases such as I,they and mi-crochip  see Figure 5 in Appendix B  context dependence plays a crucial role in Insider-Outsider classification. Furthermore, NP2IO outperforms GloVe+CBOW+XGBoost (CBOW-1, CBOW-2, CBOW-5) summarily. While both types of models employ context-dependence to classify noun phrases, NP2IO does so more effectively. The fine-tuning loss convergence plot for the optimal performing NP2IO model is presented in Figure 4 in Appendix B and model checkpoints are uploaded in the data repository. Given the limitations of current ABSA datasets for our task (see Section 2 and Section 3), we computationally show that CT5K is indeed a different dataset, particularly in comparison to other classical ones in Table 2. For this experiment, we train near-state-of-the-art ABSA models with RoBERTa-base backbone (Dai et al., 2021) on three popular ABSA datasets  Laptop reviews and Restaurant reviews from SemEval 2014 task 4 (Pontiki et al., 2014), and Tweets (Dong et al., 2014). Each trained model is then evaluated on all three datasets as well as the test set of CT5K. The Insider class in CT5K is mapped to the positive sentiment and the Outsider class to the negative sentiment. The F1-macro scores of the models trained and tested among the three ABSA datasets are much higher than the scores when testing on the CT5K dataset. Clearly, models that are successful with typical ABSA datasets do not effectively generalize to CT5K, suggesting that our dataset is different. A challenge for any model, such as NP2IO, is zero-shot performance, when it encounters noun phrases never tagged during training. Answering this question offers a means for validating the context-dependence requirement, mentioned in Section 2. This evaluation is conducted on a subset of the entire testing set: A sample of the subset { p , n } is such that the word-lemmatized, stopword-removed form of n does not exist in the set of word-lemmatized, stopword-removed noun phrases seen during training. We extract 30% of test samples to be in this set. The results are presented in Table 1. As expected, the performance of the nave Bayes models (NB, NB-L) degrades severely to random. The performance of the contextual models CBOW-1/2/5, and NP2IO stay strong, suggesting effective context sensitivity in inferring the correct labels for these models. A visualization of the zero-shot capabilities of NP2IO on unseen noun phrases is presented in Figure 6 in Appendix B. 7.3 Does NP2IO Memorize? We construct a set of adversarial samples to evaluate the extent to which NP2IO accurately classifies a noun phrase that has a highly-biased label distribution in CT5K. We consider 3 noun phrases in particular: microchip, government, and chemi-cal. Each of these has been largely labeled as Outsider s. The adversarial samples for each phrase, in contrast, are manually aggregated ( 5 seed posts augmented 20 times each) to suggest that the phrase is an Insider (see Table 5 in Appendix B for the seed posts). We compute the recall of NP2IO in detecting these Insider labels (results in Table 3). NP2IO is moderately robust against adversarial attacks: In other words, highly-skewed distributions of labels for noun phrases in our dataset do not appear to imbue a similar drastic bias into our model. We presented a challenging Insider Outsider classification task, a novel framework necessary for addressing burgeoning misinformation and the proliferation of threat narratives on social media. We compiled a labeled CT5K dataset of conspiracy-theoretic posts from multiple social media platforms and presented a competitive NP2IO model that outperforms non-trivial baselines. We have demonstrated that NP2IO is contextual and transitive via its zero-shot performance, adversarial studies and qualitative studies. We have also shown that the CT5K dataset consists of underlying information that is different from existing ABSA datasets. Given NP2IO's ability to identify Insider s and Outsider s in a text segment, we can extend the inference engine to an entire set of interrelated samples in order to extract, visualize and interpret the underlying narrative (see Figure 3). This marks a first and significant step in teasing out narratives from fragmentary social media records, with many of its essential semantic parts  such as, Insider / Outsider  tagged in an automated fashion. As extensive evaluations of the NP2IO model show, our engine has learned the causal phrases used to designate the labels. We believe an immediate future work can identify such causal phrases, yet another step toward semantic understanding of the parts of a narrative. Broadly, work similar to this promises to expedite the development of models that rely on a computational foundation of structured information, and that are better at explaining causal chains of inference, a particularly important feature in the tackling of misinforma-4982 Figure 3: An actor-actant subnarrative network constructed from social media posts: Selected posts from anti-vaccination forums such as qresearch on 4chan were decomposed into relationship tuples using a state-of-the-art relationship extraction pipeline from previous work (Tangherlini et al., 2020) and these relationships are overlayed with the inferences from NP2IO. tion. Indeed, NP2IO's success has answered the question: Which side are you on? What remains to be synthesized from language is: Why?
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