Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection (2024)

Abstract: Multifaceted ideology detection (MID) aims to detect the ideological leanings of texts towards multiple facets. Previous studies on ideology detection mainly focus on one generic facet and ignore label semantics and explanatory descriptions of ideologies, which are a kind of instructive information and reveal the specific concepts of ideologies. In this paper, we develop a novel concept semantics-enhanced framework for the MID task. Specifically, we propose a bidirectional iterative concept flow (BICo) method to encode multifaceted ideologies. BICo enables the concepts to flow across levels of the schema tree and enriches concept representations with multi-granularity semantics. Furthermore, we explore concept attentive matching and concept-guided contrastive learning strategies to guide the model to capture ideology features with the learned concept semantics. Extensive experiments on the benchmark dataset show that our approach achieves state-of-the-art performance in MID, including in the cross-topic scenario.

## introduction multifaceted ideology detection (mid) aims to identify the ideological leanings (e.g., left, center, right, etc.) expressed in texts towards multiple facets, as shown in figure 1 . it is crucial for understanding public opinion and detecting potential extremism @xcite @xcite , which is helpful for governments and cybersecurity organizations @xcite . it can also facilitate downstream research and applications in social sciences @xcite . in most of related work, researches generally focus on modeling the text content with diversified cues, such as sentiment polarities @xcite : upper: the multifaceted ideology schema and concepts of facets and ideologies @xcite . lower left: the tree-like hierarchical structure of the schema. lower right: an example of mid. "l" denotes left, "r" denotes right. although achieving promising performance, those methods limit the ideology prediction to a generic facet. in other words, they only label a text as ideologically left-or right-leaning as a whole, regardless whether the text containing one or more different facets. furthermore, they ignore a crucial clue, label semantics, that is, what exactly does an ideology mean? in this case, ideological categories are represented as one-hot vectors without any semantic information, and models can only rely on the training data distribution to analyze latent ideology features, which could be unfavorable for the generalization ability of models @xcite . so how can we effectively detect multifaceted ideologies? and what exactly does an ideology mean? @xcite propose the multifaceted ideology detection task for the first time and design a multifaceted ideology schema which contains 12 facets covering 5 domains in a tree-like hierarchical structure (see figure 1 , details in appendix a). each facet, as well as the ideological attributes under each facet, are defined using natural texts, which can be regarded as concepts. these concepts describe the meaning of facets and ideologies, thus making it natural to represent the label semantics. in addition, in the hierarchical schema, higher-level concepts (like domain and facet) have general semantics shared by their child concepts, while lowerlevel concepts (like ideology and facet) describe their parents from various views, which can be seen as the semantic divisions of higher-level concepts. this meaningful hierarchical structure can be utilized to enrich the concept semantics. based on the motivation above, to incorporate the concept semantics and leverage the hierarchical structure of the schema in mid, we propose a novel bidirectional iterative concept flow (bico) method to encode the hierarchical schema. specifically, bico allows concepts to flow in two directions on the schema tree, enabling them to perceive both high-level general semantics and low-level specific perspectives. on the one hand, inspired by the relation rotation in complex space @xcite , we design concept metapath diffusion to perform message passing from root to leaf. on the other hand, in the direction of leaf to root, we propose concept hierarchy aggregation to aggregate concept semantics in lower levels to the ones in higher levels based on the parent-child relation. concept flow in the two directions is iterated multiple times and the final concept representations are enriched by multi-granularity semantics. for example, the facet representations capture the meanings of different ideologies in the corresponding facet, while the ideology representations also perceive information about the facet and domain they belong to. we match the text and facet representations based on the attention mechanism to recognize text-related facets. furthermore, we explore a concept-guided contrastive learning strategy to learn more distinguishable text representations under the guidance of ideology concepts. the main contributions of our work are summarized as follows: ## task description given an input text and a set of facets, multifaceted ideology detection (mid) is divided into two sub-tasks: (1) relevance recognition aims to recognize the facets that the text is related to; ## approach in this section, we first introduce the proposed bidirectional iterative concept flow (bico) for encoding the hierarchical schema, and then discuss how we augment multifaceted ideology detection based on the learned concept encodings. figure 2 illustrates the overall structure of our model. text batch concepts of facets & ideologies attention facet-aware text repre. text repre. concept attentive matching subtask 1: relevance recognition contrastive learning linear layer classification for facet 1 related unrelated text encoder concept encoder concepts of facets & ideologies ## conclusion in this paper, we have proposed a concept semantics-enhanced framework for the mid task. we have also designed a novel bidirectional iterative concept flow method to capture multigranularity concept semantics. moreover, we have explored concept attentive matching and conceptguided contrastive learning strategies to enable the model to extract ideology features with the help of concept semantics. experiment results have validated the superiority of our approach.