184 research outputs found
Overview of the CLEF-2019 Checkthat! LAB: Automatic identification and verification of claims. Task 2: Evidence and factuality
We present an overview of Task 2 of the second edition of the CheckThat! Lab at CLEF 2019. Task 2 asked (A) to rank a given set of Web pages with respect to a check-worthy claim based on their usefulness for fact-checking that claim, (B) to classify these same Web pages according to their degree of usefulness for fact-checking the target claim, (C) to identify useful passages from these pages, and (D) to use the useful pages to predict the claim's factuality. Task 2 at CheckThat! provided a full evaluation framework, consisting of data in Arabic (gathered and annotated from scratch) and evaluation based on normalized discounted cumulative gain (nDCG) for ranking, and F1 for classification. Four teams submitted runs. The most successful approach to subtask A used learning-to-rank, while different classifiers were used in the other subtasks. We release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in the important task of evidence-based automatic claim verification
Dense vs. Sparse representations for news stream clustering
The abundance of news being generated on a daily basis has made it hard, if not impossible, to monitor all news developments. Thus, there is an increasing need for accurate tools that can organize the news for easier exploration. Typically, this means clustering the news stream, and then connecting the clusters into story lines. Here, we focus on the clustering step, using a local topic graph and a community detection algorithm. Traditionally, news clustering was done using sparse vector representations with TF\u2013IDF weighting, but more recently dense representations have emerged as a popular alternative. Here, we compare these two representations, as well as combinations thereof. The evaluation results on a standard dataset show a sizeable improvement over the state of the art both for the standard F1 as well as for a BCubed version thereof, which we argue is more suitable for the task
Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 2: Factuality
We present an overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims, with focus on Task 2: Factuality. The task asked to assess whether a given check-worthy claim made by a politician in the context of a debate/speech is factually true, half-true, or false. In terms of data, we focused on debates from the 2016 US Presidential Campaign, as well as on some speeches during and after the campaign (we also provided translations in Arabic), and we relied on comments and factuality judgments from factcheck.org and snopes.com, which we further refined manually. A total of 30 teams registered to participate in the lab, and five of them actually submitted runs. The most successful approaches used by the participants relied on the automatic retrieval of evidence from the Web. Similarities and other relationships between the claim and the retrieved documents were used as input to classifiers in order to make a decision. The best-performing official submissions achieved mean absolute error of .705 and .658 for the English and for the Arabic test sets, respectively. This leaves plenty of room for further improvement, and thus we release all datasets and the scoring scripts, which should enable further research in fact-checking
Overview of the CLEF-2018 checkthat! lab on automatic identification and verification of political claims
We present an overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. In its starting year, the lab featured two tasks. Task 1 asked to predict which (potential) claims in a political debate should be prioritized for fact-checking; in particular, given a debate or a political speech, the goal was to produce a ranked list of its sentences based on their worthiness for fact-checking. Task 2 asked to assess whether a given check-worthy claim made by a politician in the context of a debate/speech is factually true, half-true, or false. We offered both tasks in English and in Arabic. In terms of data, for both tasks, we focused on debates from the 2016 US Presidential Campaign, as well as on some speeches during and after the campaign (we also provided translations in Arabic), and we relied on comments and factuality judgments from factcheck.org and snopes.com, which we further refined manually. A total of 30 teams registered to participate in the lab, and 9 of them actually submitted runs. The evaluation results show that the most successful approaches used various neural networks (esp. for Task 1) and evidence retrieval from the Web (esp. for Task 2). We release all datasets, the evaluation scripts, and the submissions by the participants, which should enable further research in both check-worthiness estimation and automatic claim verification
Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness
We present an overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims, with focus on Task 1: Check-Worthiness. The task asks to predict which claims in a political debate should be prioritized for fact-checking. In particular, given a debate or a political speech, the goal was to produce a ranked list of its sentences based on their worthiness for fact checking. We offered the task in both English and Arabic, based on debates from the 2016 US Presidential Campaign, as well as on some speeches during and after the campaign. A total of 30 teams registered to participate in the Lab and seven teams actually submitted systems for Task 1. The most successful approaches used by the participants relied on recurrent and multi-layer neural networks, as well as on combinations of distributional representations, on matchings claims' vocabulary against lexicons, and on measures of syntactic dependency. The best systems achieved mean average precision of 0.18 and 0.15 on the English and on the Arabic test datasets, respectively. This leaves large room for further improvement, and thus we release all datasets and the scoring scripts, which should enable further research in check-worthiness estimation
Partisan US News Media Representations of Syrian Refugees
We investigate how representations of Syrian refugees (2011-2021) differ
across US partisan news outlets. We analyze 47,388 articles from the online US
media about Syrian refugees to detail differences in reporting between left-
and right-leaning media. We use various NLP techniques to understand these
differences. Our polarization and question answering results indicated that
left-leaning media tended to represent refugees as child victims, welcome in
the US, and right-leaning media cast refugees as Islamic terrorists. We noted
similar results with our sentiment and offensive speech scores over time, which
detail possibly unfavorable representations of refugees in right-leaning media.
A strength of our work is how the different techniques we have applied validate
each other. Based on our results, we provide several recommendations.
Stakeholders may utilize our findings to intervene around refugee
representations, and design communications campaigns that improve the way
society sees refugees and possibly aid refugee outcomes
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
In the last few years thousands of scientific papers have investigated
sentiment analysis, several startups that measure opinions on real data have
emerged and a number of innovative products related to this theme have been
developed. There are multiple methods for measuring sentiments, including
lexical-based and supervised machine learning methods. Despite the vast
interest on the theme and wide popularity of some methods, it is unclear which
one is better for identifying the polarity (i.e., positive or negative) of a
message. Accordingly, there is a strong need to conduct a thorough
apple-to-apple comparison of sentiment analysis methods, \textit{as they are
used in practice}, across multiple datasets originated from different data
sources. Such a comparison is key for understanding the potential limitations,
advantages, and disadvantages of popular methods. This article aims at filling
this gap by presenting a benchmark comparison of twenty-four popular sentiment
analysis methods (which we call the state-of-the-practice methods). Our
evaluation is based on a benchmark of eighteen labeled datasets, covering
messages posted on social networks, movie and product reviews, as well as
opinions and comments in news articles. Our results highlight the extent to
which the prediction performance of these methods varies considerably across
datasets. Aiming at boosting the development of this research area, we open the
methods' codes and datasets used in this article, deploying them in a benchmark
system, which provides an open API for accessing and comparing sentence-level
sentiment analysis methods
Creep and residual properties of cracked macro-synthetic fibre reinforced concretes
[EN] This study analysed the creep behaviour of five concretes incorporating 0.5% by volume of four macro-synthetic fibres (MSFs) and one steel fibre. After 28 d moist curing, prisms were pre-loaded up to a crack opening of 0.5 mm and held for 90 d in three different environments - ambient laboratory air, seawater at 45 degrees C and air flow at 45 degrees C. Sustained loads were then applied on three specimens from each group while the other prisms remained unloaded. The remaining residual capacity was evaluated by bending tests. It was concluded that the residual capacity of cracked MSF concretes is not affected by long-term loading.The authors wish to thank the technicians of ICITECH, where the experimental work was developed. The financial support of the project FISNE with reference BIA2012-35776, supported by the Spanish Ministry of Economy and Competiveness and the FEDER fund, is also gratefully acknowledged. The doctoral scholarship of M. E. Bossio was supported by the project EuroTANGO (European Commission - EACEA).Serna Ros, P.; Martà Vargas, JR.; Bossio, ME.; Zerbino, R. (2016). Creep and residual properties of cracked macro-synthetic fibre reinforced concretes. Magazine of Concrete Research. 68(4):197-207. https://doi.org/10.1680/macr.15.00111S19720768
Meta-analysis of genome-wide association studies from the CHARGE consortium identifies common variants associated with carotid intima media thickness and plaque
Carotid intima media thickness (cIMT) and plaque determined by ultrasonography are established measures of subclinical atherosclerosis that each predicts future cardiovascular disease events. We conducted a meta-analysis of genome-wide association data in 31,211 participants of European ancestry from nine large studies in the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. We then sought additional evidence to support our findings among 11,273 individuals using data from seven additional studies. In the combined meta-analysis, we identified three genomic regions associated with common carotid intima media thickness and two different regions associated with the presence of carotid plaque (P < 5 × 10 -8). The associated SNPs mapped in or near genes related to cellular signaling, lipid metabolism and blood pressure homeostasis, and two of the regions were associated with coronary artery disease (P < 0.006) in the Coronary Artery Disease Genome-Wide Replication and Meta-Analysis (CARDIoGRAM) consortium. Our findings may provide new insight into pathways leading to subclinical atherosclerosis and subsequent cardiovascular events
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