33 research outputs found
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