Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives (2020)

Abstract: Crime reporting is a prevalent form of journalism with the power to shape public perceptions and social policies. How does the language of these reports act on readers? We seek to address this question with the SuspectGuilt Corpus of annotated crime stories from English-language newspapers in the U.S. For SuspectGuilt, annotators read short crime articles and provided text-level ratings concerning the guilt of the main suspect as well as span-level annotations indicating which parts of the story they felt most influenced their ratings. SuspectGuilt thus provides a rich picture of how linguistic choices affect subjective guilt judgments. We use SuspectGuilt to train and assess predictive models which validate the usefulness of the corpus, and show that these models benefit from genre pretraining and joint supervision from the text-level ratings and span-level annotations. Such models might be used as tools for understanding the societal effects of crime reporting.

## introduction news outlets around the world routinely report on crimes and alleged crimes, ranging from petty misdemeanors to large-scale international criminal conspiracies. each of these reports will frame events in ways that shape reader perceptions, and these perceptions will in turn shape public perception of how much crime there is, who is responsible for crime, and what policy decisions should be made to address crime. it is therefore important to understand how the language in these reports acts on readers, and there is clear value in developing nlp models that approximate these reader perceptions at a large scale, as a tool for estimating the aggregate effects of crime reporting on society. to begin to address these needs, we present the suspectguilt corpus of annotated crime stories * equal contribution. figure 1 : the suspectguilt corpus highlighting interface. after participants responded to a question about the guilt of the main suspect in the report, they completed this highlighting phase intended to provide insights into how they took themselves to be reasoning about the text. suspectguilt contains 1.8k stories with at least 5 participants responding to each. from english-language newspapers in the u.s. 1 each story in the corpus is multiply-annotated with participants' assessments (on a continuous scale) of the guilt of the main suspect(s) and of the author's belief in the guilt of the suspect(s). in addition, for each of these guilt-rating questions, the participants highlighted the spans of text in the story that they felt contributed to their decision (figure 1 ). these additional annotations provide a window into the language that participants took themselves to be attending to as part of their personal verdicts, and thus they are especially useful for understanding how authors' low-level linguistic choices feed into readers' overall judgments. we also explore a range of methods for developing predictive models on the basis of suspectguilt annotations which exemplify the usefulness of the resource. our models are built on top of pretrained bert parameters. in the simplest case, we learn to predict the author or subject guilt ratings without any other supervision. this basic model is improved if it is jointly trained on the guilt ratings and the span-level annotations that suspectguilt provides, which helps to quantify the value of these low-level linguistic annotations. in addition, we explore unsupervised pretraining on a modestly-sized unlabeled corpus of crime stories, finding that it too increases the effectiveness of suspectguilt models. the span-level annotations offer new opportunities for analysis as well. using the integrated gradients method of @xcite , we identify the token-level features that our models rely on when trained without span-level supervision, and we compare this to the span-level annotations provided by suspectguilt. overall, the correspondence between the two is not high, which explains why the span-level objective helps our models and suggests that the document-level ratings alone might not suffice to yield models that attend to texts in the same ways that humans do. ## related work our work draws on prior research into the relationship between language and assessments of guilt, as well as work seeking to jointly model text-level and token-level annotations using neural networks. ## data the suspectguilt corpus is a resource to investigate how the language of crime reports affects readers. this section describes the data collection and annotation process. we provide qualitative and quantitative analyses of suspectguilt that exemplify its usefulness for psycholinguistic investigations and nlp applications. ## models this section summarizes the family of models we consider in this work. all of them begin with bert. we explore models with and without additional unsupervised pretraining on crime stories. we build regression models on top of these parameters using just the cls token, which is the initial token in all bert input sequences and is often taken to provide an aggregate sequence representation, as well as mean-pooling over all the final output states, and we additionally define extensions for predicting token-level highlighting. ## experiments in this section, we report the evaluation procedure for the models described in section 4. the results underline the usefulness of genre-pretraining and the rich annotations in the suspectguilt corpus. ## gradient-based token importance although our models predict human guilt judgments well, the performance metrics don't tell us how they make predictions. do they use information similarly to what we see in the human highlighting? recent gradient-based methods for assessing feature importance in models like bert @xcite can help us answer this question. figure 7 presents one analysis of this form. we ran the integrated gradients method of sundarara-jan et al., as implemented in the pytorch captum library, on models which received genre pretraining but no highlighting supervision. the figure includes test-set runs averaged across 20 models with different random train-test splits. a positive score means that the token increases the predicted rating; a negative score corresponds to a decrease. like our highlighting data, the neural network's importance scores show the highest variance for words with low frequency. words that received higher highlighting proportions for their frequency primarily affect the model predictions positively. in addition, we find that words that are more likely than random to be highlighted (as described in section 3) are also significantly more likely to receive a higher token importance score in the model (welch two sample t-test: @xmath0.01). beyond this, however, there is little correlation between the absolute attribution score for each word and its highlighting proportion (@xmath1.07). while we can't rule out the possibility that this traces to the approximations introduced by integrated gradients, it seems likely that it helps explain why the span highlighting objective has a large impact on model predictions, as it is bringing in very different information than the model would otherwise attend to. ## conclusion we introduced the suspectguilt corpus, which provides a basis for a quantitative study of how readers arrive at judgments of reader perception and author belief. we also showed that suspectguilt can be used to train predictive models on top of bert parameters, and that these models are improved by genre-specific pretraining and supervision derived from token-level highlighting. understanding how news reporting affects reader judgments is a difficult task. the span-level highlighting in suspectguilt provides some insight into the factors at work here. we sought to match this with an introspective analysis of our predictive models using the gradient-based token importance method of @xcite . this yielded a very different picture from what we see in suspectguilt. ultimately, this combination of annotations and model introspection might lead to new insights concerning how our models make decisions in this and other domains. we also hope that this work paves the way to large-scale studies of how readers formulate judgments of guilt in crime reporting and encourages the development of systems that provide guidance on the presentation of these reports. aaron steven white, rachel rudinger, kyle rawlins, and benjamin van durme. 2018. lexicosyntactic inference in neural models. in proceedings of the 2018 conference on empirical methods in natural language processing, pages 4717-4724, brussels, belgium. association for computational linguistics. frank wilcoxon. 1992. individual comparisons by ranking methods. in breakthroughs in statistics, pages 196-202. springer. thomas wolf, lysandre debut, victor sanh, julien chaumond, clement delangue, anthony moi, pierric cistac, tim rault, r'emi louf, morgan funtowicz, and jamie brew. 2019. huggingface's transformers: state-of-the-art natural language processing. arxiv, abs/1910.03771. yukun zhu, ryan kiros, rich zemel, ruslan salakhutdinov, raquel urtasun, antonio torralba, and sanja fidler. 2015. aligning books and movies: towards story-like visual explanations by watching movies and reading books. in proceedings of the ieee international conference on computer vision, pages 19-27. appendices a data 2,818 annotators contributed to 3,463 submissions on amazon's mechanical turk. the approximate time for completion was 15 minutes, and each participant was paid $2.50. we restricted participation to ip addresses within the us and an approval rate higher than 97%. participants were asked to read 5 stories and respond to three questions about them (as described in section 3.2). the full design of the trials is shown in figure 8 . we excluded participants who indicated that they did the study incorrectly or were confused (544), whose self-reported native language was not english (71), who spent less than 3.5 minutes on the task (53), and who gave more then 2 out of 5 erroneous responses in the control questions (359). a response is considered erroneous when a clearly true or false question incorrectly received a slider value below or above 50 (the center of the scale) respectively. additionally, we excluded 120 annotations because annotators had seen this story in a previous submission. overall, we excluded 1,035 submissions and 120 annotations (15,405 annotations out of 51,945, resulting in 36,420 annotations). a majority of annotators (89%) only participated once, which makes up 74% of all annotations. only 14 annotators participated more than three times (0.7%). the average age of annotators was 36 with a slightly higher proportion of male over female participants. the median time annotators spent on the study was 15.2 minutes, which is in-line with our original time estimates. overall, annotators indicated that they enjoyed the study. annotators also had the option to indicate that the question cannot be applied to the news report. overall, participants rarely used that option, but more so for the question about the author belief (1.6%) than the reader perception (10.5%) question. if several annotators agree that a question cannot be answered in the context of one particular story, it might be an indication that this story is not suitable for the corpus. we therefore decided to exclude stories where this box was selected more than 30% of the time with that particular question. further inspection showed that this mainly affected summary news articles which addressed multiple stories and suspects and therefore the questions could not be uniquely attributed to one specific case.