Sentence-level Media Bias Analysis with Event Relation Graph (2024)

Abstract: Media outlets are becoming more partisan and polarized nowadays. In this paper, we identify media bias at the sentence level, and pinpoint bias sentences that intend to sway readers’ opinions. As bias sentences are often expressed in a neutral and factual way, considering broader context outside a sentence can help reveal the bias. In particular, we observe that events in a bias sentence need to be understood in associations with other events in the document. Therefore, we propose to construct an event relation graph to explicitly reason about event-event relations for sentence-level bias identification. The designed event relation graph consists of events as nodes and four common types of event relations: coreference, temporal, causal, and subevent relations. Then, we incorporate event relation graph for bias sentences identification in two steps: an event-aware language model is built to inject the events and event relations knowledge into the basic language model via soft labels; further, a relation-aware graph attention network is designed to update sentence embedding with events and event relations information based on hard labels. Experiments on two benchmark datasets demonstrate that our approach with the aid of event relation graph improves both precision and recall of bias sentence identification.

## introduction media bias refers to the ideological leaning of the journalists within news reporting @xcite . news media plays an important role not only in providing information, but also in shaping public opinions @xcite @xcite . media outlets are exhibiting growing partisanship and polarization, with a greater potential to influence public opinions and interfere democratic process @xcite @xcite . thus, developing sophisticated models to detect media bias is important and necessary. while media bias detection has received growing research interests, the majority of prior work detect media bias at the article level @xcite . nevertheless, each sentence within an article serves for different purposes in narrating a news story. it is important to pinpoint those bias sentences that aim to implant ideological bias and manipulate public opinions. bias sentence, as interpreted by @xcite ; @xcite ; @xcite , is the content providing supportive or background information to shift pubic opinions in an ideological direction, though that may be done via information selective inclusion or omission as well as overt ideological language. this paper aims to identify media bias at sentence level, and develop computational model to detect bias sentences. identifying sentence-level media bias still remains a challenging task @xcite , especially considering its subtle and implicit nature. take the example article in figure 1 as an instance, while the first bias sentence s5 contains overt ideological language and may be easily identified, the second bias sentence s6 looks completely neutral and factual, and a model merely examining the sentences in isolation without considering broader context may not readily reveal such bias cases. but it becomes clear that s6 carries bias if we contrast s6 (trump always decided not to follow through presidential selections) with what trump said in the statement (his willingness to still run if he was displeased with the candidates). interestingly, it appears that the author hinted on the relatedness between the historical events described in s6 and the statement event by explicitly stating a temporal relation between them. to further elaborate, trump claimed a causal relation between events drop plans example article donald j. trump said in a statement on tuesday that he dropped plans to moderate a presidential debate. trump said that he dropped because the republican party asked him to give up the right to run as an independent candidate if he moderates the debate. "it is very important to me that the right republican candidate be chosen to defeat the failed obama administration," mr. trump said in his statement. mr. trump drew considerable criticism from the republican establishment and attracted the ridicule from the public for his decision to drop and his willingness to still run if he was displeased with the candidates. to moderate a debate and give up right as an independent candidate, revealing the historical events in s6 propels the reader to rethink whether the stated cause is indeed the real reason, given his consistent past behavior of dropping presidential election several times since 1980s. ## related work source-level media bias attracted research attention in prior years. the work in this area assumed all the articles and journalists within one media outlet share the same political ideology. prior work such as @xcite ; gentzkow and shapiro (2010) measures source-level media bias via citation patterns or consumer political preferences. budak et al. (2016) implemented a crowdsourcing approach to rate the political slant of media sources. baly et al. (2018) combined text-based methods with social media behaviors to create a bias classification system. article-level media bias has been researched for years @xcite . early work used text-based approach (sapiro-gheiler, 2019). chen et al. (2020) developed gaussian mixture model to incorporate fine-grained bias information. baly et al. (2020) collected a largescale dataset and provided manual labels, showing that only three percentage of articles have a different ideology label from their source's. liu et al. (2022) pre-trained a political domain language model. different from previous work on source-level or article-level media bias, we aim to identify media bias at the fine-grained sentencelevel, and pinpoint the content that can illuminate and explain the overall article bias. sentence-level media bias has a relatively short research history @xcite @xcite . the initial work in this field is @xcite , where they annotate sentence-level media bias within political news document. another work @xcite also annotate media bias at sentence level while considering the article context. a discourse structure method was developed by @xcite , in which the discourse structures knowledge were distilled into bias sentence identification via a knowledge distillation framework. different from @xcite , our event relation graph interprets news narrative from the perspective of event reporting, and enables constructing event-level discourse relations for every possible pair of sentences, fostering a comprehensive understanding of the broader context. event graph was introduced by @xcite where they presented a graph-based structure connecting events and entities nodes through event argument role relations. this event graph was deployed in several down-stream applications, such as sentence fusion @xcite , story generation @xcite , and misinformation detection @xcite . the event graph constructed in their work comprises entity-entity links and event-entity links via event argument roles, while lacks event-event relations. differently, our proposed event relation graph focuses on events and establishes event interconnections through four types of event-event relations. event and event relations were researched for decades. previous work explored event identification and event extraction @xcite . there are four common relations between events: coreference @xcite , temporal @xcite , causal @xcite , and subevent relations @xcite . while each type of relation was previously studied in isolation, a recent work @xcite provided the first large-scale corpus with all four relations annotated. instead of modeling each event relation separately, we aim to develop a model that unifies all four relations for comprehensive discourse analysis. ## event relation graph the event relation graph can incorporate broader contextual information outside the single sentence, and thus help reveal the underlying bias inside the sentence. hence, we propose to create an event relation graph for each article. in this section, we explain the components, training process, and construction process of the event relation graph. ## bias sentences identification the event relation graph is incorporated into bias sentences identification within two consecutive steps, as illustrated in figure 2 . firstly, an eventaware language model is developed to inject the events and event relations knowledge into the basic language model using the soft labels. secondly, a relation-aware graph attention network is devised to encode the event relation graph based on hard labels, and update sentence embeddings with events and event relations information. ## conclusion this paper identifies media bias at the sentence level, and demonstrates the pivotal role of events and event-event relations in identifying bias sentences. we observe that interpreting events in association with other events in a document is critical in identifying bias sentences. inspired by this observation, this paper proposes to construct an event relation graph as the extra guidance for bias sentence detection. to integrate the event relation graph, we design a novel framework that consists of two consecutive steps to first update the language model and then further update sentence embeddings, by leveraging the soft and hard event relation labels respectively from the event relation graph. experimental results demonstrate the effectiveness of our approach. for future work, we will further improve the extraction of implicit event relations as well as develop new interpretable methods that leverage event relation graph for media bias analysis.