ABSTRACT
The COVID-19 pandemic has received extensive media coverage, with a vast variety of claims made about different aspects of the virus. In order to track these claims, we present COVID-19 Claim Radar(1), a system that automatically extracts claims relating to COVID-19 in news articles. We provide a comprehensive structured view of such claims, with rich attributes (such as claimers and their affiliations) and associated knowledge elements (such as events, relations and entities). Further, we use this knowledge to identify inter-claim connections such as equivalent, supporting, or refuting relations, with shared structural evidence like claimers, similar centroid events and arguments. In order to consolidate claim structures at the corpus-level, we leverage Wikidata(2) as the hub to merge coreferential knowledge elements, and apply machine translation to aggregate claims from news articles in multiple languages. The system provides users with a comprehensive exposure to COVID-19 related claims, their associated knowledge elements, and related connections to other claims. The system is publicly available on GitHub(3) and DockerHub(4), with complete documentation(5).
ABSTRACT
Over the past year, research in various domains, including Natural Language Processing (NLP), has been accelerated to fight against the COVID-19 pandemic, yet such research has just started on dialogue systems. In this paper, we introduce an end-to-end dialogue system which aims to ease the isolation of people under self-quarantine. We conduct a control simulation experiment to assess the effects of the user interface, a web-based virtual agent called Nora vs. the android ERICA via a video call. The experimental results show that the android offers a more valuable user experience by giving the impression of being more empathetic and engaging in the conversation due to its nonverbal information, such as facial expressions and body gestures.
ABSTRACT
As the sources of information that we consume everyday rapidly diversify, it is becoming increasingly important to develop NLP tools that help to evaluate the credibility of the information we receive. A critical step towards this goal is to determine the factuality of events in text. In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting with respect to the events. We crowdsource the first large-scale data set annotated with modal dependency structures that consists of 353 Covid-19 related news articles, 24,016 events, and 2,938 conceivers.(1) We also develop the first modal dependency parser that jointly extracts events, conceivers and constructs the modal dependency structure of a text. We evaluate the joint model against a pipeline model and demonstrate the advantage of the joint model in conceiver extraction and modal dependency structure construction when events and conceivers are automatically extracted. We believe the dataset and the models will be a valuable resource for a whole host of NLP applications such as fact checking and rumor detection.
ABSTRACT
We introduce a FEVER-like dataset COVID-Fact of 4;086 claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific claims, our data contains simplified general claims from media sources, making it better suited for detecting general misinformation regarding COVID-19. Our experiments indicate that COVID-Fact will provide a challenging testbed for the development of new systems and our approach will reduce the costs of building domainspecific datasets for detecting misinformation.
ABSTRACT
Knowledge bases (KBs) and text often contain complementary knowledge: KBs store structured knowledge that can support longrange reasoning, while text stores more comprehensive and timely knowledge in an unstructured way. Separately embedding the individual knowledge sources into vector spaces has demonstrated tremendous successes in encoding the respective knowledge, but how to jointly embed and reason with both knowledge sources to fully leverage the complementary information is still largely an open problem. We conduct a large-scale, systematic investigation of aligning KB and text embeddings for joint reasoning. We set up a novel evaluation framework with two evaluation tasks, few-shot link prediction and analogical reasoning, and evaluate an array of KB-text embedding alignment methods. We also demonstrate how such alignment can infuse textual information into KB embeddings for more accurate link prediction on emerging entities and events, using COVID-19 as a case study.(1)
ABSTRACT
Misinformation has recently become a well-documented matter of public concern. Existing studies on this topic have hitherto adopted a coarse concept of misinformation, which incorporates a broad spectrum of story types ranging from political conspiracies to misinterpreted pranks. This paper aims to structurize these misinformation stories by leveraging fact-check articles. Our intuition is that key phrases in a fact-check article that identify the misinformation type(s) (e.g., doctored images, urban legends) also act as rationales that determine the verdict of the fact-check (e.g., false). We experiment on rationalized models with domain knowledge as weak supervision to extract these phrases as rationales, and then cluster semantically similar rationales to summarize prevalent misinformation types. Using archived fact-checks from Snopes.com, we identify ten types of misinformation stories. We discuss how these types have evolved over the last ten years and compare their prevalence between the 2016/2020 US presidential elections and the H1N1/COVID-19 pandemics.
ABSTRACT
We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP to assess how European solidarity discourses changed before and after the COVID-19 outbreak was declared a global pandemic. To this end, we annotate 2.3k English and German tweets for (anti-)solidarity expressions, utilizing multiple human annotators and two annotation approaches (experts vs. crowds). We use these annotations to train a BERT model with multiple data augmentation strategies. Our augmented BERT model that combines both expert and crowd annotations outperforms the baseline BERT classifier trained with expert annotations only by over 25 points, from 58% macro-F1 to almost 85%. We use this highquality model to automatically label over 270k tweets between September 2019 and December 2020. We then assess the automatically labeled data for how statements related to European (anti-)solidarity discourses developed over time and in relation to one another, before and during the COVID-19 crisis. Our results show that solidarity became increasingly salient and contested during the crisis. While the number of solidarity tweets remained on a higher level and dominated the discourse in the scrutinized time frame, anti-solidarity tweets initially spiked, then decreased to (almost) pre-COVID-19 values before rising to a stable higher level until the end of 2020.
ABSTRACT
The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their views regarding various issues related to the pandemic, e.g. stay at home mandates and wearing face masks when out in public. We set out to make use of this data by collecting the stance expressed by Twitter users, with respect to topics revolving around the pandemic. We annotate a new stance detection dataset, called COVID-19-Stance. Using this newly annotated dataset, we train several established stance detection models to ascertain a baseline performance for this specific task. To further improve the performance, we employ self-training and domain adaptation approaches to take advantage of large amounts of unlabeled data and existing stance detection datasets. The dataset, code, and other resources are available on GitHub.(1)
ABSTRACT
This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a model trained on a small, expert-annotated dataset already lead to a substantial improvement - in terms of inter-annotator agreement (+.14 Fleiss' kappa) and annotation quality - compared to students that do not receive any label suggestions. We further find that label suggestions from interactively trained models do not lead to an improvement over suggestions from a static model. Nonetheless, our analysis of suggestion bias shows that annotators remain capable of reflecting upon the suggested label in general. Finally, we confirm the quality of the annotated data in transfer learning experiments between different annotator groups. To facilitate further research in opinion mining on social media data, we release our collected data consisting of 200 expert and 2,785 student annotations.(1)
ABSTRACT
The ongoing COVID-19 pandemic resulted in significant ramifications for international relations ranging from travel restrictions, global ceasefires, and international vaccine production and sharing agreements. Amidst a wave of infections in India that resulted in a systemic breakdown of healthcare infrastructure, a social welfare organization based in Pakistan offered to procure medical-grade oxygen to assist India - a nation which was involved in four wars with Pakistan in the past few decades. In this paper, we focus on Pakistani Twitter users' response to the ongoing healthcare crisis in India. While #IndiaNeedsOxygen and #PakistanStandsWithIndia featured among the toptrending hashtags in Pakistan, divisive hashtags such as #EndiaSaySorryToKashmir simultaneously started trending. Against the back-drop of a contentious history including four wars, divisive content of this nature, especially when a country is facing an unprecedented healthcare crisis, fuels further deterioration of relations. In this paper, we define a new task of detecting supportive content and demonstrate that existing NLP for social impact tools can be effectively harnessed for such tasks within a quick turnaround time. We also release the first publicly available data set(1) at the intersection of geopolitical relations and a raging pandemic in the context of India and Pakistan.
ABSTRACT
Conversational Agents (CAs) can be a proxy for disseminating information and providing support to the public, especially in times of crisis. CAs can scale to reach larger numbers of end-users than human operators, while they can offer information interactively and engagingly. In this work, we present Theano, a Greek-speaking virtual assistant for COVID-19. Theano presents users with COVID-19 statistics and facts and informs users about the best health practices as well as the latest COVID-19 related guidelines. Additionally, Theano provides support to end-users by helping them self-assess their symptoms and redirecting them to first-line health workers. The relevant, localized information that Theano provides, makes it a valuable tool for combating COVID-19 in Greece. Theano has already conversed with different users in more than 170 different conversations through a web interface as a chatbot and over the phone as a voice bot.
ABSTRACT
Recent events, such as the 2016 US Presidential Campaign, Brexit and the COVID-19 "infodemic", have brought into the spotlight the dangers of online disinformation. There has been a lot of research focusing on fact-checking and disinformation detection. However, little attention has been paid to the specific rhetorical and psychological techniques used to convey propaganda messages. Revealing the use of such techniques can help promote media literacy and critical thinking, and eventually contribute to limiting the impact of "fake news" and disinformation campaigns. Prta (Propaganda Persuasion Techniques Analyzer) allows users to explore the articles crawled on a regular basis by highlighting the spans in which propaganda techniques occur and to compare them on the basis of their use of propaganda techniques. The system further reports statistics about the use of such techniques, overall and over time, or according to filtering criteria specified by the user based on time interval, keywords, and/or political orientation of the media. Moreover, it allows users to analyze any text or URL through a dedicated interface or via an API. The system is available online: https://www.tanbih.org/prta.
ABSTRACT
We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries. In contrast to previous attempts to dependency-based search, we introduce a light-weight query language that does not require the user to know the details of the underlying linguistic representations, and instead to query the corpus by providing an example sentence coupled with simple markup. Search is performed at an interactive speed due to efficient linguistic graphindexing and retrieval engine. This allows for rapid exploration, development and refinement of user queries. We demonstrate the system using example workflows over two corpora: the PubMed corpus including 14,446,243 PubMed abstracts and the CORD-19 dataset(1), a collection of over 45,000 research papers focused on COVID-19 research. The system is publicly available at https://allenai.github.io/spike