Quotatives Indicate Decline in Objectivity in U.S. Political News

Abstract

According to journalistic standards, direct quotes should be attributed to sources with objective quotatives such as ``said'' and ``told,'' since nonobjective quotatives, e.g., ``argued'' and ``insisted,'' would influence the readers' perception of the quote and the quoted person. In this paper, we analyze the adherence to this journalistic norm to study trends in objectivity in political news across U.S. outlets of different ideological leanings. We ask: 1) How has the usage of nonobjective quotatives evolved? 2) How do news outlets use nonobjective quotatives when covering politicians of different parties? To answer these questions, we developed a dependency-parsing-based method to extract quotatives and applied it to Quotebank, a web-scale corpus of attributed quotes, obtaining nearly 7 million quotes, each enriched with the quoted speaker's political party and the ideological leaning of the outlet that published the quote. We find that, while partisan outlets are the ones that most often use nonobjective quotatives, between 2013 and 2020, the outlets that increased their usage of nonobjective quotatives the most were ``moderate'' centrist news outlets (around 0.6 percentage points, or 20% in relative percentage over seven years). Further, we find that outlets use nonobjective quotatives more often when quoting politicians of the opposing ideology (e.g., left-leaning outlets quoting Republicans) and that this ``quotative bias'' is rising at a swift pace, increasing up to 0.5 percentage points, or 25% in relative percentage, per year. These findings suggest an overall decline in journalistic objectivity in U.S. political news.

Predicting Sentence-Level Factuality of News and Bias of Media Outlets

“…Since factual sentences are impartially focused on objective facts, in contrast to the biased ones that are partially presented and focused on subjective interpretations, we built a model to predict sentence-level factuality based on aspects of subjectivity and impartiality. Finally, once both biased spans and quotes present evidence of subjective interpretation of facts (Hu et al, 2023), our sentence-level factuality model is composed of a binary class variable from biased spans and quotes (1,949 labels) versus factual spans (1,949 labels).…”