Detecting Contextomized Quotes in News Headlines by Contrastive Learning (2023)

Abstract: Quotes are critical for establishing credibility in news articles. A direct quote enclosed in quotation marks has a strong visual appeal and is a sign of a reliable citation. Unfortunately, this journalistic practice is not strictly followed, and a quote in the headline is often “contextomized.” Such a quote uses words out of context in a way that alters the speaker’s intention so that there is no semantically matching quote in the body text. We present QuoteCSE, a contrastive learning framework that represents the embedding of news quotes based on domain-driven positive and negative samples to identify such an editorial strategy. The dataset and code are available at https://github.com/ssu-humane/contextomized-quote-contrastive .

## introduction a direct quotation, a verbatim replication of a speaker's words as opposed to offering news reporters' own opinions, manifests news stories' neutrality, factuality, and objectivity @xcite . quoting others also adds color to the news with authentic expressions and conveniently establishes authority based on the speakers' reputation @xcite . therefore, a direct quotation constitutes an integral element of news reporting @xcite . more studies have found a link between the use of direct quotations and fake news. content analyses of news stories document evidence such that deceptive (versus trustworthy) news articles contain more direct quotations @xcite ). an equally problematic but less studied concern involving direct quotations is contextomy, quoting words out of context in a way that alters the speaker's intention. a previous study argued that contextomy is a "common spin tactic" of news reporters promoting their political agenda @xcite . some news outlets have been notorious for editorializing and sensationalizing their stories with contextomized quotes in news headlines @xcite . the first example in we propose a new problem of identifying contextomized quotes in news headlines. in contrast to a modified quote, which corrects grammar, replaces unheralded pronouns with proper names, removes unnecessary phrases, and substitutes synonyms, a contextomized quote refers to the excerpt of words with semantic changes from the original statement @xcite . hence, the task is to classify whether a headline quote is semantically matched by comparing quotes in the news headline quote body-text quotes label "ᄋ ᅵᄃ ᅢᄅ ᅩ ᆫ ᄀ ᅳᄅ ᅵᄉ ᅳᄎ ᅥᄅ ᅥ ᆷ ᄑ ᅡᄐ ᅡ ᆫ" (a debt crisis, like greece, is on the horizon) to tackle the detection task, we propose using contrastive learning for quote representation, which trains a model to maximize the similarity of samples that are expected to be similar (known as positive samples). simultaneously, the model tries to reduce the similarity between samples that should be dissimilar (aka negative samples). following the recent research in contrastive sentence embedding @xcite , we introduce a positive and negative sample selection strategy that is suited to the problem. our key idea is illustrated in figure 1 . if a direct quotation appears in a news headline, there should be a quote with the same semantics in the body text. furthermore, the title quote must be distinct from other quotes in the same article or from quotes in other (randomly chosen) news articles. since quotes from the same article share common topics, it is more challenging to distinguish a headline quote from those in its body text than to understand semantic differences between quotes from distinct articles. adopting the 'hard' negatives in contrastive loss can help a model learn an effective representation, thereby capturing nuanced semantic differences between quotes. evaluation experiments show its effectiveness at the target problem as well as its high quality in terms of theoretical measures, such as alignment and uniformity. our main contributions are three-fold: ## related work following the recent success in computer vision @xcite @xcite , previous studies on contrastive sentence embedding focused on how to construct a positive pair by employing data augmentation methods to an anchor sentence @xcite @xcite . a recent study showed that a simple dropout augmentation (unlike complex augmentations) with bert to construct a positive pair could be an effective strategy known as simcse @xcite . another study improved the performance by combining simcse with masked token detection @xcite . this study proposes a strategy for selecting positive and negative samples according to journalistic ethics. ## problem and data research problem let a given news article be x : (t, b), where t is the news title, and b is the body text. our task is to predict a binary label y indicating whether the headline quote in t is either contextomized (1) or modified (0) by referring to the body-text quotes. the detection target is news articles that use at least one direct quotation in the headline and body text. ## methods to predict the label l of x : (t, b), we utilize contrastive embedding and measure the semantic relationship between quotes in the headline and body text. we introduce the main framework. ## evaluation results ## conclusion inspired by the importance of direct quotations in news reporting and their widespread misuse, this study proposed a new nlp problem of detecting contextomized news quotes. while there had been studies on quote identification @xcite and speaker attribution @xcite , this study is the first to discern a specific type of headline news quote that distorts the speaker's intention and is cut out of context. not only does it violate journalism ethics @xcite , but it can also mislead public opinion @xcite . therefore, tackling the problem of detecting contextomized quotes in news headlines can significantly aid the existing efforts to nurture healthy media environments using nlp techniques @xcite . understanding the subtle semantic differences between quotes from news headlines and those from body text is a prerequisite for detecting contextomized news quotes. to assist with this, we introduce quotecse, a contrastive learning framework for quote representation. we specifically tailored simcse @xcite to the detection of the editorial slogan by proposing a positive and negative sample selection strategy consistent with journalism ethics. in the evaluation experiments, we confirmed the effectiveness of both positive and hard negative samples in the journalism-driven contrastive learning framework. altogether, the findings imply the crucial role of domain knowledge in tackling computational social science problems.