Nick Beauchamp

Substance: Political opinion, psychology, deliberation, and persuasion
Methods: Natural language processing, network analysis, AI, Bayesian statistics

Publications

MOKA: Moral Knowledge Augmentation for Moral Event Extraction. Proceedings of the 2024 North American Association for Computational Linguistics (NAACL) (X.F. Zhang, W. Wu, N. Beauchamp, and L. Wang ) Forthcoming 2024.
Abstract Paper

News media often strive to minimize explicit moral language in news articles, yet most articles are dense with moral values as expressed through the reported events themselves. However, values that are reflected in the intricate dynamics among participating entities and moral events are far more challenging for most NLP systems to detect, including LLMs. To study this phenomenon, we annotate a new dataset, MORAL EVENTS, consisting of 5,494 structured event annotations on 474 news articles by diverse US media across the political spectrum. We further propose MOKA, a moral event extraction framework with MOral Knowledge Augmentation, which leverages knowledge derived from moral words and moral scenarios to produce structural representations of morality-bearing events. Experiments show that MOKA outperforms competitive baselines across three moral event understanding tasks. Further analysis shows even ostensibly nonpartisan media engage in the selective reporting of moral events.

Construction Risk Identification using a Multi-sentence Context-aware Method. Automation in Construction (N. Gao, A. Touran, Q. Wang, N. Beauchamp) Forthcoming 2024.
Abstract Paper

Knowledge of risk events with potentially negative consequences from previous projects is essential for risk identification in early stages of new infrastructure projects. However, historical risk events are usually scattered in various sources and reports, rendering collecting such risk information time-consuming and expensive. To expand the current risk data sources and facilitate risk events' extraction, the study presents a synthetic approach that utilizes Natural Language Processing (NLP) techniques to automatically identify and extract risk-related sentences from news articles. A supervised Multi-sentence Context-aware Risk Identification (MCRI) model is devised to exploit both sentence-level and multi-sentence level context to boost the sentence classification performance. The MCRI model outperformed several baseline models with a risk-class F1-score of 87.1% and an accuracy of 86.7%. This paper provides a baseline for future studies aimed at automating the extraction of project-level risk information within the construction domain.

All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 15472-15488. (Y. Liu, X.F. Zhang, K. Zou, R. Huang, N. Beauchamp, and L. Wang ) 2023.
Abstract Paper

Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention has been devoted to media bias via overt ideological language or topic selection, a more profound way in which the media shape opinion is via the strategic inclusion or omission of partisan events that may support one side or the other. We develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology. Our experiments first validate the existence of partisan event selection, and then show that article alignment and cross-document comparison detect partisan events and article ideology better than competitive baselines. Our results reveal the high-level form of media bias, which is present even among mainstream media with strong norms of objectivity and nonpartisanship.

How expertise mediates the effects of numerical and textual communication on individual and collective accuracy. Decision, 11(1), 194-211. (N. Beauchamp, S. Shugars, B. Swire-Thompson, D. Lazer). 2023.
Abstract Paper

Performance on difficult tasks such as forecasting generally benefits from the "wisdom of crowds," but communication among individuals can harm performance by reducing independent information. Collective accuracy can be improved by weighting by expertise, but it may also be naturally improved within communicating groups by the tendency of experts to be more resistant to peer information, effectively upweighting their contributions. To elucidate precisely how experts resist peer information, and the downstream effects of that on individual and collective accuracy, we construct a set of event-prediction challenges and randomize the exchange of both numerical and textual information among individuals. This allows us to estimate a continuous nonlinear response function connecting signals and predictions, which we show is consistent with a novel Bayesian updating framework which unifies the tendencies of experts to discount all peer information, as well as information more distant from their priors. We show via our textual treatment that experts are similarly less responsive to textual information, where nonexperts are more affected and benefited overall, but experts are helped by the highest quality text. We apply our Bayesian framework to show that the collective benefits of expert nonresponsivity are highly sensitive to the variance in expertise, but that individual predictions can be "corrected" back toward their unobserved pretreatment states, boosting the collective accuracy of nonexperts close to the level of experts, and restoring much of the accuracy lost due to intragroup communication. We conclude by examining potential avenues for further improving collective accuracy by structuring communication within groups.

Unveiling Partisan and Counter-Partisan Events in News Reporting. Findings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 621-632. (K. Zou, X. F. Zhang, W. Wu, N. Beauchamp, L. Wang) 2023.
Abstract Paper

News media sway public opinions by selectively reporting events that are in favor of their ideological stances and against the opposing side. While prior work in NLP has studied media bias through their word usage, few have focused on the supposedly objective ingredients of news production, i.e., how authors purposely include or omit certain events. In this work, we introduce a novel task of detecting partisan and counter-partisan events: events that advance or suppress the author's political ideology. To support the study, we annotate a high-quality dataset containing 6,162 such events in 206 news articles from diverse media outlets. We benchmark this dataset with pre-trained language models to highlight challenges for this task. In particular, we find that even using carefully constructed prompts with demonstrations, ChatGPT largely ignores events with ideological implications due to their seeming objectivity. Our findings underscore future directions on empowering large language models to better understand events as interactions among their participants, as well as the intentions of the media for including such events.

While there have been many efforts to monitor or predict Covid using digital traces such as social media, one of the most distinctive and diagnostically important symptoms of Covid -- anosmia, or loss of smell -- remains elusive due to the infrequency of discussions of smell online. It was recently hypothesized that an inadvertent indicator of this key symptom may be misplaced complaints in Amazon reviews that scented products such as candles have no smell. This paper presents a novel Bayesian vector autoregression model developed to test this hypothesis, finding that "no smell" reviews do indeed reflect changes in US Covid cases even when controlling for the seasonality of those reviews. A series of robustness checks suggests that this effect is also seen in perfume reviews, but did not hold for the flu prior to Covid. These results suggest that inadvertent digital traces may be an important tool for tracking epidemics.

A Generative Entity-to-Entity Stance Detection Framework. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9950-9969. (X.F. Zhang, N. Beauchamp and L. Wang) 2022.
Abstract Paper

Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the imperative need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10, 641 annotations labeled at the sentence-level from news articles of different ideological leanings. We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them. We further enhance the model with a graph encoder to summarize entity activities and external knowledge surrounding the entities. Experiments show that our model outperforms strong comparisons by large margins. Further analyses demonstrate the usefulness of E2E stance detection for understanding media quotation and stance landscape as well as inferring entity ideology.

Sentence-level Media Bias Analysis Informed by Discourse Structures. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 10040-10050. (Y. Lei, R. Huang, L. Wang, N. Beauchamp) 2022.
Abstract Paper

As polarization continues to rise among both the public and the news media, increasing attention has been devoted to detecting media bias. Most recent work in the NLP community, however, identify bias at the level of individual articles. However, each article itself comprises multiple sentences, which vary in their ideological bias. In this paper, we aim to identify sentences within an article that can illuminate and explain the overall bias of the entire article. We show that understanding the discourse role of a sentence in telling a news story, as well as its relation with nearby sentences, can reveal the ideological leanings of an author even when the sentence itself appears merely neutral. In particular, we consider using a functional news discourse structure and PDTB discourse relations to inform bias sentence identification, and distill the auxiliary knowledge from the two types of discourse structure into our bias sentence identification system. Experimental results on benchmark datasets show that incorporating both the global functional discourse structure and local rhetorical discourse relations can effectively increase the recall of bias sentence identification by 8.27% - 8.62%, as well as increase the precision by 2.82% - 3.48%1.

"A Multisource Database Tracking the Impact of the COVID-19 Pandemic on the Communities of Boston, MA" Nature Scientific Data. (A. Ristea et al.) 2022.
Abstract Paper

A pandemic, like other disasters, changes how systems work. In order to support research on how the COVID-19 pandemic impacted the dynamics of a single metropolitan area and the communities therein, we developed and made publicly available a "data-support system" for the city of Boston. We actively gathered data from multiple administrative (e.g., 911 and 311 dispatches, building permits) and internet sources (e.g., Yelp, Craigslist), capturing aspects of housing and land use, crime and disorder, and commercial activity and institutions. all the data were linked spatially through BaRI's Geographical Infrastructure, enabling conjoint analysis. We curated the base records and aggregated them to construct ecometric measures (i.e., descriptors of a place) at various geographic scales, all of which were also published as part of the database. the datasets were published in an open repository, each accompanied by a detailed documentation of methods and variables. We anticipate updating the database annually to maintain the tracking of the records and associated measures.

"POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection," Proceedings of the North American Association for Computational Linguistics (NAACL, Short papers) (Y. Liu, X. F. Zhang, D. Wegsman, N. Beauchamp, L. Wang) 2022.
Abstract Paper

Ideology is at the core of political science research. Yet there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.

"DebateVis: Visualizing Political Debates for Non-Expert Users." IEEE VIS Short Papers (L. South, M. Schwab, N. Beauchamp, L. Wang, J. Wihbey and M.A. Borkin) 2020.
Abstract Paper

Political debates provide an important opportunity for voters to observe candidate behavior, learn about issues, and make voting decisions. However, debates are generally broadcast late at night and last more than ninety minutes, so watching debates live can be inconvenient, if not impossible, for many potential viewers. Even voters who do watch debates may find themselves overwhelmed by a deluge of information in a substantive, issue-driven debate. Media outlets produce short summaries of debates, but these are not always effective as a method of deeply comprehending the policies candidates propose or the debate techniques they employ. In this paper we contribute reflections and results of an 18-month design study through an interdisciplinary collaboration with journalism and political science researchers. We characterize task and data abstractions for visualizing political debate transcripts for the casual user, and present a novel tool (DEBATEVIS) to help non-expert users explore and analyze debate transcripts.

"Educational Accountability and State ESSA Plans," Education Policy (J. Portz and N. Beauchamp) 2020.
Abstract Paper

This paper examines different state approaches to educational accountability in response to the Every Student Succeeds Act. Cluster analysis reveals three groups of states with similar indicator weights and rating systems, and principal component analysis identifies two dimensions underlying these clusters. We find that state-level demographics are correlated with the types of assessment policies adopted by states: policy liberalism is associated with putting greater weight on school quality and student success, while economic variables are associated with traditional performance measures, such as graduation rates and testing. These clusters reveal different approaches to measuring accountability and prioritizing different kinds of information, which can in turn influence the nature of education politics.

"Why Keep Arguing? Predicting Engagement in Political Conversations Online," Sage Open (S. Shugars and N. Beauchamp) 2019.
Abstract Paper

Individuals acquire increasingly more of their political information from social media, and ever more of that online time is spent in interpersonal, peer-to-peer communication and conversation. Yet many of these conversations can be either acrimoniously unpleasant, or pleasantly uninformative. Why do we seek out and engage in these interactions? Who do people choose to argue with, and what brings them back to repeated exchanges? In short, why do people bother arguing online? We develop a model of argument engagement using a new dataset of Twitter conversations about President Trump. The model incorporates numerous user, tweet, and thread-level features to predict user participation in conversations with over 98% accuracy. We find that users are likely to argue over wide ideological divides, and are increasingly likely to engage with those who are different from themselves. Additionally, we find that the emotional content of a tweet has important implications for user engagement, with negative and unpleasant tweets more likely to spark sustained participation. Though often negative, these extended discussions can bridge political differences and reduce information bubbles. This suggests a public appetite for engaging in prolonged political discussions that are more than just partisan potshots or trolling, and our results suggest a variety of strategies for extending and enriching these interactions.

"Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse," Proceedings of the North American Association for Computational Linguistics(X. Zeng, J. Li, L. Wang, N. Beauchamp, S. Shugars and K.F. Wong) 2018.
Abstract Paper

Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation. While much prior work has focused on post-level recommendation, we exploit both the conversational context, and user content and behavior preferences. We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics. Experimental results on two Twitter datasets demonstrate that our system outperforms methods that only model content without considering discourse.

"Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes," Transactions of the Association for Computational Linguistics (L. Wang, N. Beauchamp, S. Shugars and K. Qin) 2018.
Abstract Paper

Debate and deliberation play essential roles in politics and government, but most models of debate presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates both the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments.

"Predicting and Interpolating State-level Polls using Twitter Textual Data," American Journal of Political Science (N. Beauchamp) 2017.
Abstract Paper

Spatially or temporally dense polling remains both difficult and expensive using existing survey methods. In response, there have been increasing efforts to approximate various survey measures using social media, but most of these approaches remain methodologically flawed. To remedy these flaws, this paper combines 1200 state-level polls during the 2012 presidential campaign with over 100 million state-located political Tweets; models the polls as a function of the Twitter text using a new linear regularization feature-selection method; and shows via out-of-sample testing that when properly modeled, the Twitter-based measures track and to some degree predict opinion polls, and can be extended to unpolled states and potentially sub-state regions and sub-day timescales. An examination of the most predictive textual features reveals the topics and events associated with opinion shifts, sheds light on more general theories of partisan difference in attention and information processing, and may be of use for real-time campaign strategy.

"What Terrorist Leaders Want: A Content Analysis of Terrorist Propaganda Videos," Studies in Conflict and Terrorism (M. Abrahms, N. Beauchamp and J. Mroszczyk) 2016.
Abstract Paper

In recent years, a growing body of empirical research suggests that indiscriminate violence against civilian targets tends to carry substantial political risks compared to more selective violence against military targets. To better understand why terrorist groups sometimes attack politically suboptimal targets, scholars are increasingly adopting a principal-agent framework where the leaders of terrorist groups are understood as principals and lower level members as agents. According to this framework, terrorist leaders are thought to behave as essentially rational political actors, whereas lower level members are believed to harbor stronger non-political incentives for harming civilians, often in defiance of leadership preferences. We test this proposition with an original content analysis of terrorist propaganda videos. Consistent with the principal-agent framework, our analysis demonstrates statistically that terrorist leaders tend to favor significantly less indiscriminate violence than their operatives actually commit, providing unprecedented insight into the incentive structure of terrorist leaders relative to the rank-and-file.

"A Bottom-up Approach to Linguistic Persuasion in Advertising," (Research Note) The Political Methodologist (N. Beauchamp) 2011.

"Modeling and Measuring Deliberation Online," Book chapter, Oxford Handbook of Networked Communication (N. Beauchamp) 2018.
Abstract Paper

Online communication is often characterized as predominated by antagonism or groupthink, with little in the way of meaningful interaction or persuasion. This essay examines how we might detect and measure instances of more productive conversation online, considered through the lens of deliberative theory. It begins with an examination of traditional deliberative democracy, and then explores how these concepts have been applied to online deliberation, and by those studying interpersonal conversation in social media more generally. These efforts to characterize and measure deliberative quality have resulted in a myriad of criteria, with elaborate checklists that are often as superficial as they are complex. This essay instead proposes that we target what is arguably the core deliberative process -- a mutual consideration of conceptually interrelated ideas in order to distinguish the better from the worse and to construct better conceptual structures. The essay finishes by discussing two computational models of argument quality and interdependence as templates for richer, scalable, nonpartisan measures of deliberative discussion online.

"Measuring Public Opinion with Social Media Data," Book chapter, Oxford Handbook of Polling and Polling Methods (M. Klasnja, P. Barbera, N. Beauchamp, J. Nagler and J.A. Tucker) 2017.
Abstract Paper

This chapter examines the use of social networking sites such as Twitter in measuring public opinion. It first considers the opportunities and challenges that are involved in conducting public opinion surveys using social media data. Three challenges are discussed: identifying political opinion, representativeness of social media users, and aggregating from individual responses to public opinion. The chapter outlines some of the strategies for overcoming these challenges and proceeds by highlighting some of the novel uses for social media that have fewer direct analogs in traditional survey work. Finally, it suggests new directions for a research agenda in using social media for public opinion work.

"The Ideological Position of Obama's SOTU Relative to Past Presidents," The Monkey Cage, Washingtonpost.com, January 2012

"Findings of an independent panel on allegations of statistical evidence for fraud during the 2004 Venezuelan Presidential recall referendum," in. Observing the Venezuela Presidential Recall Referendum: Comprehensive Report, The Carter Center, 2004 (with Henry Brady, Richard Fowles, Aviel Rubin, and Jonathan Taylor)
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Teaching

Introduction to Computational Statistics, INSH 5301 (Syllabus)
Bayesian and Network Statistics, NETS 7983 (Syllabus)
Social Network Analysis, POLS 7334 (Syllabus)
Congress, POLS 3300 and POLS 7251 (Syllabus)
Bostonography, INSH 2102 (Syllabus)

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