KCD : Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media (2022)

Abstract: Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. In light of these limitations, we propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning and incorporate textual cues as paragraph-level labels. Specifically, we firstly generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles. Finally, we adopt relational graph neural networks for graph-level representation learning and conduct political perspective detection. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on two benchmark datasets. We further examine the effect of knowledge walks and textual cues and how they contribute to our approach’s data efficiency.

## introduction political perspective detection aims to identify ideological stances of textual data such as social media posts and news articles. previous approaches generally leverage the textual content of news articles with various text modeling techniques to identify political stances. those works @xcite @xcite leveraged diversified text models, such as recurrent neural networks @xcite , word embedding techniques @xcite , convolutional neural networks @xcite , and pre-trained language models @xcite , to encode news paragraphs and classify them into different perspective labels. later approaches incorporate information sources beyond text to facilitate argument mining and boost task performance. news discussion on social networks @xcite , social and linguistic information about news articles @xcite , media sources and information @xcite as well as external knowledge from knowledge graphs @xcite are introduced in the task of political perspective detection and achieve better performance. although these methods attempted to leverage more than news content, they fail to present a framework capable of reasoning with background knowledge and leveraging implicit semantic and syntactic indicators such as sentiment and tense of news articles. for example, figure 1 presents a typical news article from daily kos foot_0 . this article discusses remarks from the trump campaign team about wikileaks and its effect on hillary clinton's bid for president. individuals often rely on the multi-hop reasoning that clinton and trump are from opposite political parties and run against each other to inform their perspective analysis process. besides, the negative sentiment expressed in satiric tones and the quotation of trump campaign staff also give away the author's denial and left-leaning perspective. that being said, knowledge reasoning and implicit textual indicators are essential in the news bias detection process. in light of these limitations, we propose a political perspective detection framework kcd (knowledge walks and textual cues enhanced political perspective detection). specifically, kcd generates multi-hop knowledge walks, aggregates them based on semantic relevance and incorporates them in textual representations with multi-head attention. kcd then constructs a heterogeneous information network to jointly model knowledgeenriched news content and diversified textual cues as paragraph-level labels. finally, kcd learns graph representations with relational graph neural networks and conduct perspective detection with different aggregation strategies. our main contributions are summarized as follows: ## methodology figure 2 presents an overview of our proposed political perspective detection framework kcd (knowledge walks and textual cues enhanced political perspective detection). we firstly generate knowledge walks on the external knowledge graph. these knowledge walks are then selected based on semantic relevance and injected into textual representations with multi-head attention. we then construct a heterogeneous information network to jointly model knowledge-enriched news content and diversified textual cues as paragraph-level labels and supernodes. finally, we adopt relational graph neural networks and different aggregation strategies to learn graph-level representation and conduct political perspective detection. ## conclusion in this paper,we propose kcd, a political perspective detection approach that reasons with multi-hop external knowledge and leverages diversified implicit textual indicators. we firstly generate multihop knowledge walks, dynamically aggregate them based on semantic relevance and infuse into news text representations. we then construct a heterogeneous information network to jointly model knowledge-enriched news content and diversified textual cues as paragraph-level labels. finally, we learn graph representations with relational graph neural networks under different aggregation settings and conduct political perspective detection as graph-level classification. extensive experiments demonstrate that our approach consistently outperforms state-of-the-art baselines on two benchmark datasets. further experiments also bear out the necessity of knowledge walks and textual cues in modeling political perspectives in news media.