My side, your side and the evidence: Discovering aligned actor groups and the narratives they weave (2023)

Abstract: News reports about emerging issues often include several conflicting story lines. Individual stories can be conceptualized as samples from an underlying mixture of competing narratives. The automated identification of these distinct narratives from unstructured text is a fundamental yet difficult task in Computational Linguistics since narratives are often intertwined and only implicitly conveyed in text. In this paper, we consider a more feasible proxy task: Identify the distinct sets of aligned story actors responsible for sustaining the issue-specific narratives. Discovering aligned actors, and the groups these alignments create, brings us closer to estimating the narrative that each group represents. With the help of Large Language Models (LLM), we address this task by: (i) Introducing a corpus of text segments rich in narrative content associated with six different current issues; (ii) Introducing a novel two-step graph-based framework that (a) identifies alignments between actors (INCANT) and (b) extracts aligned actor groups using the network structure (TAMPA). Amazon Mechanical Turk evaluations demonstrate the effectiveness of our framework. Across domains, alignment relationships from INCANT are accurate (macro F1 >= 0.75) and actor groups from TAMPA are preferred over 2 non-trivial baseline models (ACC >= 0.75).

## background and motivation discussions about current events in public forums involve consensus building, with the exchange of beliefs and perspectives producing competing, often conflicting, narratives. a person reading these discussions parses natural language and is able to tease out and maintain representations of the various narratives, including the central actors, their alignments, and the often-contrasting points-ofview presented by the narratives. replicating this type of comprehension in machines by creating interpretable, mathematical representations of narra-tive structure is a field of continued computational linguistics efforts @xcite . a narrative is usually modeled as a narrative network of actors (nodes) and their inter-actor relationships (edges). this graph building is, however, a challenging aggregation task since the same narrative can be expressed in natural language in several ways. conversely, a given text span can include signatures of several underlying narratives. it is worth noting that a coherent narrative usually features a small set of critical actors that emerge through the give and take of online discussions and provides a distilled representation of a particular world view. we refer to these key sets of critical actors that are narratively aligned to a shared worldview as "actor groups". people reading or participating in the discussion, in turn, support or even identify with these story actor groups, ensuring the persistence of the narrative in the discussion domain. identifying these groups of aligned actors is essential to defining the boundaries of a narrative, its current scope and, possibly, its future viability (i.e. if people do not recognize actor groups as central to a narrative, that group and its constitutive members is likely to disappear over time from the narrative space). we therefore consider the detection of actor groups from text as an accessible first step in the larger task of estimating the total narrative structure. ## our approach and related work computational efforts to address prob-1 model human experience by adapting pre-trained large language models (plm). these models demonstrate considerable semantic awareness in several well-known nlp tasks (a product of the knowledge embedded in the exhaustive training corpora); semantic role labeling (srl) @xcite , question-answering (qa) @xcite , sentiment analysis (sa) @xcite , and language generation (lg) @xcite for instance, all make use of pre-training to boost performance. the transitivity requirement in prob-2 is often addressed by fine-tuning plms on biased datasets containing implicit transitivity constraints @xcite . fine-tuning weights encourages generalization across data samples. however, these fine-tuned models are datasetspecific and must be retrained for every encountered domain: an expensive and time-intensive task. alternative approaches use models that are trained to generate an external representation of the domain, often in the form of a network @xcite . the network structure enables higher-order consensus insights. the semantic awareness exhibited by plms motivate the adoption of a transfer learning approach to extract alignment implicit in text segments. in our work, this is facilitated by question-answering (qa) (see sec. 2.1) that outputs an alignment network specific to a conversation domain: the network is a joint representation of individual pairwise alignment relationships between actors (in-cant). actor groups are identified by exploiting this network structure (tampa). the task of discovering aligned actor groups has strong parallels to identifying homophily between users on social media platforms @xcite . homophily refers to the tendency for individuals to interact more frequently with those who share similar beliefs and attitudes. identifying homophilic user groups involves exploiting latent features in the social media with which the users interact; for example, šćepanović et al. (2017) identifies user cohorts on twitter by profiling their engagement with political parties; meanwhile del @xcite utilizes the neighborhood of a user within a social network to enable inter-user comparison. our work extends these ethnographic efforts to the narrative landscape: we identify groups of actors that feature in the narrative using the contextual alignment clues present in the language. ## data collection news reports are a fertile ground for exploring the formation of opposing narratives and their attendant actor groups since, for any domain, these accounts contain fragments of the various emergent perspectives, the actors aligned with each narrative and the contrast between potentially opposing sides. while individual news articles may favor one narrative perspective over another, a large corpus of articles concerning a single event or issue may, in the aggregate, capture a wide range of these conflicting (sub)narratives. we use a bootstrapped weakly-supervised process to assemble a corpus of such articles particular to a domain c: we believe there are sufficient actors who are influential in swaying consensus opinion and are unlikely to change their pairwise alignments during the 3-month window. this stabilizes the performance of the inter-actor alignment framework tampa (see sec. 4.2.1) as indicated by our results. the proposed framework also enables identifying actors that switch sides during the observation time window: such actors are positioned by the framework at the outskirts of the core aligned actor groups enabling us to discover the multitude of groups to which they are aligned. for example, we find that in the ukraine war domain, many republicans were aligned to russia (fig. 4 ). however, mitt romney, a moderate republican, aligns weakly with ukraine. the 6 evaluated domains in tab. 2 have significantly different time frames, ranging from longstanding debates such as roe v. wade to more recent events like the war in ukraine. these domains also involve a diverse set of actors, range in scope from national issues like gun regulations to global concerns like recession fears, and are universally recognized as contentious, with multiple viewpoints to consider. segmenting the long-form text: transformerbased models accept a limited token length of context. we split the news articles into smaller segments while retaining many long-range coreference dependencies: 1. auto-regressive coreference resolution: each news article is sentence-tokenized {s 1 , s 2 , . . . , s n }. the auto-regressive seq-2seq module greedily resolves references in a sliding window @xmath0, . . . , s i+k } using a transformer model trained on ontonotes 5.0 @xcite . the enriched sentences {ŝ i , ŝ2 , . . . , ŝi+k } replace the original set, and the process is repeated after moving the window by a stride @xmath1. the updated sequence is {ŝ 1 , ŝ2 , . . . , ŝn }. ## evaluation and discussion to assess the effectiveness of our framework, we use a two-step evaluation approach. first, we rate the quality of the alignment relationships in the incant networks. second, we evaluate the actor groups generated by tampa. it is important to note that the inter-actor relationship quality directly impacts the quality of the resulting actor groups. for further reference, we have attached the codebase and supplemental network files. you can access them in our repository at the following link: foot_2 . ## concluding remarks in this work, we propose a novel approach for identifying aligned actors and actor groups from the mixture of latent narratives that undergird domainconditioned free-form text. the success of our approach is evaluated using both qualitative (see figs. 3 , 4 and 5 ) and quantitative (see tabs. 4,5 and 6) evidence. we show in fig. 5 that these groups can be used to assemble corresponding narrative networks that convey "my side, your side and the evidence" supporting each side. when these narrative networks are viewed jointly, we observe a struggle for narrative dominance. in many cases, the tactics proposed in one narrative to counter external threats become threats in and of themselves in other narratives. for instance, in fig. 5 , the relationship tuple "biden administration" → looking to @xmath2, a threat. conversely, this same strategy is perceived as a threat by gun rights activists. this example highlights the complexity of the narrative landscape and how the same inter-actor relationship can take on distinct, often conflicting roles, depending on the side we choose.