Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation

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 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 10,619 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.Comment: EMNLP'22 Main Conferenc

Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting

News media is expected to uphold unbiased reporting. Yet they may still affect public opinion by selectively including or omitting events that support or contradict their ideological positions. Prior work in NLP has only studied media bias via linguistic style and word usage. In this paper, we study to which degree media balances news reporting and affects consumers through event inclusion or omission. We first introduce the task of detecting both partisan and counter-partisan events: events that support or oppose the author's political ideology. To conduct our study, we annotate a high-quality dataset, PAC, containing 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets. We benchmark PAC to highlight the challenges of this task. Our findings highlight both the ways in which the news subtly shapes opinion and the need for large language models that better understand events within a broader context. Our dataset can be found at https://github.com/launchnlp/Partisan-Event-Dataset.Comment: EMNLP'23 Finding

All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison

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 unobtrusive 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. Our codebase and dataset are available at https://github.com/launchnlp/ATC.Comment: EMNLP'23 Main Conferenc

Can CANVAS due to RFC1 biallelic expansions present with pure ataxia?

BACKGROUND: Biallelic expansion of AAGGG in the replication factor complex subunit 1 (RFC1) was identified as a major cause of cerebellar ataxia, neuropathy (sensory ganglionopathy, or SG) and vestibular areflexia syndrome (CANVAS). We wanted to clarify if RFC1 expansions can present with pure ataxia and if such expansions could be responsible for some cases where an alternative diagnosis had been made. METHODS: We identified patients with a combination of ataxia and SG and no other cause found, patients where an alternative diagnosis had been made, and patients with pure ataxia. Testing for RFC1 expansions was done using established methodology. RESULTS: Among 54 patients with otherwise idiopathic sporadic ataxia without SG, none was found to have RFC1 expansions. Among 38 patients with cerebellar ataxia and SG in which all other causes were excluded, 71% had RFC1 expansions. Among 27 patients with cerebellar ataxia and SG diagnosed with coeliac disease or gluten sensitivity, 15% had RFC1 expansions. CONCLUSIONS: Isolated cerebellar ataxia without SG makes the diagnosis of CANVAS due to RFC1 expansions highly improbable, but CANVAS is frequently the cause of the combination of idiopathic cerebellar ataxia with SG. It is important to screen patients diagnosed with other causes of acquired ataxia and SG as a small percentage were found to have RFC1 expansions

Clinical risk score for persistent postconcussion symptomsamong children with acute concussion in the ED

IMPORTANCE Approximately one-third of children experiencing acute concussion experience ongoing somatic, cognitive, and psychological or behavioral symptoms, referred to as persistent postconcussion symptoms (PPCS). However, validated and pragmatic tools enabling clinicians to identify patients at risk for PPCS do not exist. OBJECTIVE To derive and validate a clinical risk score for PPCS among children presenting to the emergency department. DESIGN, SETTING, AND PARTICIPANTS Prospective, multicenter cohort study (Predicting and Preventing Postconcussive Problems in Pediatrics [5P]) enrolled young patients (aged 5-\u3c18 years) who presented within 48 hours of an acute head injury at 1 of 9 pediatric emergency departments within the Pediatric Emergency Research Canada (PERC) network from August 2013 through September 2014 (derivation cohort) and from October 2014 through June 2015 (validation cohort). Participants completed follow-up 28 days after the injury. EXPOSURES All eligible patients had concussions consistent with the Zurich consensus diagnostic criteria. MAIN OUTCOMES AND MEASURES The primary outcomewas PPCS risk score at 28 days, which was defined as 3 or more new or worsening symptoms using the patient-reported Postconcussion Symptom Inventory compared with recalled state of being prior to the injury. RESULTS In total, 3063 patients (median age, 12.0 years [interquartile range, 9.2-14.6 years]; 1205 [39.3%] girls) were enrolled (n = 2006 in the derivation cohort; n = 1057 in the validation cohort) and 2584 of whom (n = 1701 [85%] in the derivation cohort; n = 883 [84%] in the validation cohort) completed follow-up at 28 days after the injury. Persistent postconcussion symptoms were present in 801 patients (31.0%) (n = 510 [30.0%] in the derivation cohort and n = 291 [33.0%] in the validation cohort). The 12-point PPCS risk score model for the derivation cohort included the variables of female sex, age of 13 years or older, physician-diagnosed migraine history, prior concussion with symptoms lasting longer than 1 week, headache, sensitivity to noise, fatigue, answering questions slowly, and 4 or more errors on the Balance Error Scoring System tandem stance. The area under the curve was 0.71 (95%CI, 0.69-0.74) for the derivation cohort and 0.68 (95%CI, 0.65-0.72) for the validation cohort. CONCLUSIONS AND RELEVANCE A clinical risk score developed among children presenting to the emergency department with concussion and head injury within the previous 48 hours had modest discrimination to stratify PPCS risk at 28 days. Before this score is adopted in clinical practice, further research is needed for external validation, assessment of accuracy in an office setting, and determination of clinical utility

“This Candle Has No Smell”: Detecting the Effect of COVID Anosmia on Amazon Reviews Using Bayesian Vector Autoregression

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

Technology and dialogue in the primary classroom

Language is a teacher’s main pedagogical tool. ‘Classroom dialogue’ can be thought of as a specific use of language, that involves using talk as a cultural and psychological tool in a way that is productive for learning. In this chapter, the authors consider how technologies can be used to develop dialogue within classrooms in one setting and between classrooms in different locations. The chapter enables the reader to recognise how language is a teacher’s main pedagogical tool and that they can use technology effectively to support this in the primary classroom. The chapter also helps teachers to feel better prepared to realise the potential of a balanced use of technologies to initiate and sustain dialogue within and between classrooms, both local and international, and then to recognise the affordances of technologies on their own, or in combination, that can be used to encourage dialogue to support learning in the primary classroom