The massive spread of false information on social media has become a global risk especially in a global pandemic situation like COVID-19. False information detection has thus become a surging research topic in recent months. NLP4IF-2021 shared task on fighting the COVID-19 infodemic has been organised to strengthen the research in false information detection where the participants are asked to predict seven different binary labels regarding false information in a tweet. The shared task has been organised in three languages; Arabic, Bulgarian and English. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves a 0.707 mean F1 score in Arabic, 0.578 mean F1 score in Bulgarian and 0.864 mean F1 score in English ranking 4th place in all the languages.
With the outbreak of the COVID-19 pandemic, people turned to social media to read and to share timely information including statistics, warnings, advice, and inspirational stories. Unfortunately, alongside all this useful information, there was also a new blending of medical and political misinformation and disinformation, which gave rise to the first global infodemic. While fighting this infodemic is typically thought of in terms of factuality, the problem is much broader as malicious content includes not only fake news, rumors, and conspiracy theories, but also promotion of fake cures, panic…
University of Groningen（格罗宁根大学）；Utrecht University（乌得勒支大学）
This paper describes the TOKOFOU system, an ensemble model for misinformation detection tasks based on six different transformer-based pre-trained encoders, implemented in the context of the COVID-19 Infodemic Shared Task for English. We fine tune each model on each of the task's questions and aggregate their prediction scores using a majority voting approach. TOKOFOU obtains an overall F1 score of 89.7%, ranking first.
IIIT Sri City（印度国际信息技术学院斯里城校区）；IIIT Delhi（德里国际信息技术学院）；IIT Patna（巴特那印度理工学院）
Along with COVID-19 pandemic we are also fighting an `infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We benchmark the annotated dataset with four machine learning baselines - Decision Tree, Logistic Regression, Gradient Boost, and Support Vector Machine (SVM). We obtain the best performance of 93.46% F1-score with SVM. The data and code…
Qatar Computing Research Institute, HBKU（卡塔尔计算研究所（哈马德·本·哈利法大学））；University of Padova（帕多瓦大学）；Hamad bin Khalifa University（哈马德·本·哈利法大学）；Montclair State University（蒙特克莱尔州立大学）；Sofia University “St. Kliment Ohridski”（索非亚大学“圣克莱门特·奥里季斯基”）
We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that tweet contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task~2 focused on censorship detection, and was offered in Chinese. A total of ten teams …
Qatar Computing Research Institute（卡塔尔计算研究所）；HBKU（哈马德·本·哈利法大学）；Vellore Institute of Technology（韦洛尔理工学院）
Fighting the ongoing COVID-19 infodemic has been declared as one of the most important focus areas by the World Health Organization since the onset of the COVID-19 pandemic. While the information that is consumed and disseminated consists of promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic, at the same time there is information (e.g., containing advice, promoting cure) that can help different stakeholders such as policy-makers. Social media platforms enable the infodemic and there has been an effort to curate the content on such platforms, analyze and deb…
A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data
College of Economics and Management, Beijing University of Technology（北京工业大学经济与管理学院）；Guilin University of Electronic Technology（桂林电子科技大学）；LAAS-CNRS, Université de Toulouse, CNRS（法国国家科学研究中心图卢兹大学LAAS实验室）
Indian Institute of Information Technology, Allahabad（印度阿拉哈巴德信息技术学院）
Humanity is battling one of the most deleterious virus in modern history, the COVID-19 pandemic, but along with the pandemic there's an infodemic permeating the pupil and society with misinformation which exacerbates the current malady. We try to detect and classify fake news on online media to detect fake information relating to COVID-19 and coronavirus. The dataset contained fake posts, articles and news gathered from fact checking websites like politifact whereas real tweets were taken from verified twitter handles. We incorporated multiple conventional classification techniques like Naive …
Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；University of Padova（帕多瓦大学）；University of Groningen（格罗宁根大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Sofia University（索非亚大学）
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and th…
Indraprastha Institute Information Technology, Delhi（德里英德拉普拉斯塔信息技术学院）；Guru Nanak Dev University, Amritsar（阿姆利则古鲁那纳克德夫大学）
COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people's health and governance systems. Since social media is the largest source of information, managing the infodemic not only requires mitigating of misinformation but also an early understanding of psychological patterns resulting from it. During the COVID-19 crisis, Twitter alone has seen a sharp 45% increase in the usage of its curated events page, and a 30% increase in its direct messaging usage, since March 6th 2020. In this study, we analyze …
News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been init…
University of Tartu（塔尔图大学）；Nanyang Technological University（南洋理工大学）
In this work, we collect a moderate-sized representative corpus of tweets (200,000 approx.) pertaining Covid-19 vaccination spanning over a period of seven months (September 2020 - March 2021). Following a Transfer Learning approach, we utilize the pre-trained Transformer-based XLNet model to classify tweets as Misleading or Non-Misleading and validate against a random subset of results manually. We build on this to study and contrast the characteristics of tweets in the corpus that are misleading in nature against non-misleading ones. This exploratory analysis enables us to design features (s…
University of Michigan-Dearborn（密歇根大学迪尔伯恩分校）；University of Michigan（密歇根大学）
Misinformation of COVID-19 is prevalent on social media as the pandemic unfolds, and the associated risks are extremely high. Thus, it is critical to detect and combat such misinformation. Recently, deep learning models using natural language processing techniques, such as BERT (Bidirectional Encoder Representations from Transformers), have achieved great successes in detecting misinformation. In this paper, we proposed an explainable natural language processing model based on DistilBERT and SHAP (Shapley Additive exPlanations) to combat misinformation about COVID-19 due to their efficiency an…
New York University Abu Dhabi（纽约大学阿布扎比分校）；Georgia Institute of Technology（佐治亚理工学院）；New York University（纽约大学）
Fact checking by professionals is viewed as a vital defense in the fight against misinformation.While fact checking is important and its impact has been significant, fact checks could have limited visibility and may not reach the intended audience, such as those deeply embedded in polarized communities. Concerned citizens (i.e., the crowd), who are users of the platforms where misinformation appears, can play a crucial role in disseminating fact-checking information and in countering the spread of misinformation. To explore if this is the case, we conduct a data-driven study of misinformation …
University of Udine（乌迪内大学）；RMIT University（皇家墨尔本理工大学）；The University of Queensland（昆士兰大学）
Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large. Very recently, the problem has been addressed with a crowdsourcing-based approach to scale up labeling efforts: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd of (non-expert) judges is exploited. We follow the same approach to study whether crowdsourcing is an effective and reliable method to assess statements truthfulness during a pandemic. We specifically target statements related to the COVID-19 health …
Purdue University（普渡大学）；University of Pennsylvania（宾夕法尼亚大学）
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and fine-grained entity level moral sentiment analysis. We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predic…
Shahjalal University of Science and Technology（沙赫贾拉尔科技大学）
The rapid outbreak of COVID-19 has caused humanity to come to a stand-still and brought with it a plethora of other problems. COVID-19 is the first pandemic in history when humanity is the most technologically advanced and relies heavily on social media platforms for connectivity and other benefits. Unfortunately, fake news and misinformation regarding this virus is also available to people and causing some massive problems. So, fighting this infodemic has become a significant challenge. We present our solution for the "Constraint@AAAI2021 - COVID19 Fake News Detection in English" challenge in…
The Hong Kong University of Science and Technology（香港科技大学）
Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information. Considering the problematic consequences that the COVID-19 fake-news have brought, the scientific community has put effort to tackle it. To contribute to this fight against the infodemic, we aim to achieve a robust model for the COVID-19 fake-news detection task proposed at CONSTRAINT 2021 (FakeNews-19) by taking two separate approaches: 1) fine-tuning transformers based language models with robust loss functions and 2) removing harmful training instances through in…
COVID-19 pandemic has generated what public health officials called an infodemic of misinformation. As social distancing and stay-at-home orders came into effect, many turned to social media for socializing. This increase in social media usage has made it a prime vehicle for the spreading of misinformation. This paper presents a mechanism to detect COVID-19 health-related misinformation in social media following an interdisciplinary approach. Leveraging social psychology as a foundation and existing misinformation frameworks, we defined misinformation themes and associated keywords incorporate…
Indraprastha Institute of Information Technology Delhi（英迪拉普拉斯塔信息技术学院德里校区）；Shiv Nadar University（希夫纳达尔大学）
The COVID-19 pandemic has put immense pressure on health systems which are further strained due to the misinformation surrounding it. Under such a situation, providing the right information at the right time is crucial. There is a growing demand for the management of information spread using Artificial Intelligence. Hence, we have exploited the potential of Natural Language Processing for identifying relevant information that needs to be disseminated amongst the masses. In this work, we present a novel Cross-lingual Natural Language Processing framework to provide relevant information by match…
Sofia University “St Kliment Ohridski”（索非亚大学“圣克里门特·奥赫里德斯基”）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）
While misinformation and disinformation have been thriving in social media for years, with the emergence of the COVID-19 pandemic, the political and the health misinformation merged, thus elevating the problem to a whole new level and giving rise to the first global infodemic. The fight against this infodemic has many aspects, with fact-checking and debunking false and misleading claims being among the most important ones. Unfortunately, manual fact-checking is time-consuming and automatic fact-checking is resource-intense, which means that we need to pre-filter the input social media posts an…
During the COVID-19 pandemic, social media platforms were ideal for communicating due to social isolation and quarantine. Also, it was the primary source of misinformation dissemination on a large scale, referred to as the infodemic. Therefore, automatic debunking misinformation is a crucial problem. To tackle this problem, we present two COVID-19 related misinformation datasets on Twitter and propose a misinformation detection system comprising network-based and content-based processes based on machine learning algorithms and NLP techniques. In the network-based process, we focus on social pr…
Qatar Computing Research Institute, HBKU（卡塔尔计算研究所，哈马德·本·哈利法大学）；University of Pittsburgh（匹兹堡大学）
The emergence of the COVID-19 pandemic and the first global infodemic have changed our lives in many different ways. We relied on social media to get the latest information about the COVID-19 pandemic and at the same time to disseminate information. The content in social media consisted not only health related advises, plans, and informative news from policy makers, but also contains conspiracies and rumors. It became important to identify such information as soon as they are posted to make actionable decisions (e.g., debunking rumors, or taking certain measures for traveling). To address this…
The COVID-19 pandemic has been accompanied by an `infodemic' -- of accurate and inaccurate health information across social media. Detecting misinformation amidst dynamically changing information landscape is challenging; identifying relevant keywords and posts is arduous due to the large amount of human effort required to inspect the content and sources of posts. We aim to reduce the resource cost of this process by introducing a weakly-supervised iterative graph-based approach to detect keywords, topics, and themes related to misinformation, with a focus on COVID-19. Our approach can success…
Indian Institute of Technology, Gandhinagar（印度理工学院甘地纳加尔分校）
The entire world is engulfed in the fight against the COVID-19 pandemic, leading to a significant surge in research experiments, government policies, and social media discussions. A multi-modal information access and data visualization platform can play a critical role in supporting research aimed at understanding and developing preventive measures for the pandemic. In this paper, we present a multi-faceted AI-based search and visualization engine, CovidExplorer. Our system aims to help researchers understand current state-of-the-art COVID-19 research, identify research articles relevant to th…
Technische Universität Dresden（德累斯顿工业大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）；Otto-von-Guericke-Universität Magdeburg（奥托·冯·格里克大学马格德堡）
The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it hard to identify concise and credible information for COVID-19-related questions, like incubation time, infection rates, or the effectiveness of vaccines. As a novel solution, our paper is concerned with designing a question-answering system based on modern technologies from natural language processing to overcome information overload and misinformation in pandemic situations. To carry out our research, we followed a design science research approach and applied Ingwersen's cognitive model of information retrieval inter…
The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to the lack of information and knowledge. In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating covidAsk, a question answering (QA) system that combines biomedical text mining and QA techniques to provide answers to questions in real-time. Our system also leverages information retrieval (IR) approaches to provide entity-level answers that are complementary to QA models. Evalua…
University of Catania（卡塔尼亚大学）；INFN Sezione di Catania（意大利国家核物理研究所卡塔尼亚分部）；Complexity Science Hub Vienna（维也纳复杂性科学中心）
In early January 2020, after China reported the first cases of the new coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully accurate information has started spreading faster than the virus itself. Alongside this pandemic, people have experienced a parallel infodemic, i.e., an overabundance of information, some of which misleading or even harmful, that has widely spread around the globe. Although Social Media are increasingly being used as information source, Web Search Engines, like Google or Yahoo!, still represent a powerful and trustworthy resource for finding information…
This paper proposes a novel Ontology-based and user-focused Automatic Text Summarization (OATS) system, in the setting where the goal is to automatically generate text summarization from unstructured text by extracting sentences containing the information that aligns to the user's focus. OATS consists of two modules: ontology-based topic identification and user-focused text summarization; it first utilizes an ontology-based approach to identify relevant documents to user's interest, and then takes advantage of the answers extracted from a question answering model using questions specified from…
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…
Recently there has been a formidable work which has been put up from the people who are working in the frontlines such as hospitals, clinics, and labs alongside researchers and scientists who are also putting tremendous efforts in the fight against COVID-19 pandemic. Due to the preposterous spread of the virus, the integration of the artificial intelligence has taken a considerable part in the health sector, by implementing the fundamentals of Automatic Speech Recognition (ASR) and deep learning algorithms. In this paper, we illustrate the importance of speech signal processing in the extracti…
Taibah University（泰巴大学）；The University of Jordan（约旦大学）
The coronavirus pandemic has spread over the past two years in our highly connected and information-dense society. Nonetheless, disseminating accurate and up-to-date information on the spread of this pandemic remains a challenge. In this context, opting for a solution based on conversational artificial intelligence, also known under the name of the chatbot, is proving to be an unavoidable solution, especially since it has already shown its effectiveness in fighting the coronavirus crisis in several countries. This work proposes to design and implement a smart chatbot on the theme of COVID-19, …
University of Augsburg（奥格斯堡大学）；University of Tokyo（东京大学）；Huazhong University of Science and Technology（华中科技大学）；Central Hospital of Wuhan（武汉市中心医院）；Wuhan Union Hospital（武汉协和医院）；Children’s Hospital of Chongqing Medical University（重庆医科大学附属儿童医院）；Imperial College London（帝国理工学院）
The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise th…
