Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated.
The omnipresent COVID-19 pandemic gave rise to a parallel spreading of misinformation, also referred to as an Infodemic. Consequently, social media have become targets for the application of social bots, that is, algorithms that mimic human behaviour. Their ability to exert influence on social media can be exploited by amplifying misinformation, rumours, or conspiracy theories which might be harmful to society and the mastery of the pandemic. By applying social bot detection and content analysis techniques, this study aims to determine the extent to which social bots interfere with COVID- 19 d…
QAnon is an umbrella conspiracy theory that encompasses a wide spectrum of people. The COVID-19 pandemic has helped raise the QAnon conspiracy theory to a wide-spreading movement, especially in the US. Here, we study users' dynamics on Twitter related to the QAnon movement (i.e., pro-/anti-QAnon and less-leaning users) in the context of the COVID-19 infodemic and the topics involved using a simple network-based approach. We found that pro- and anti-leaning users show different population dynamics and that late less-leaning users were mostly anti-QAnon. These trends might have been affected by …
An infodemic is an emerging phenomenon caused by an overabundance of information online. This proliferation of information makes it difficult for the public to distinguish trustworthy news and credible information from untrustworthy sites and non-credible sources. The perils of an infodemic debuted with the outbreak of the COVID-19 pandemic and bots (i.e., automated accounts controlled by a set of algorithms) that are suspected of spreading the infodemic. Although previous research has revealed that bots played a central role in spreading misinformation during major political events, how bots …
Indraprastha Institute of Information Technology（英迪拉·甘地信息技术学院）
Following the wave of misinterpreted, manipulated and malicious information growing on the Internet, the misinformation surrounding COVID-19 has become a paramount issue. In the context of the current COVID-19 pandemic, social media posts and platforms are at risk of rumors and misinformation in the face of the serious uncertainty surrounding the virus itself. At the same time, the uncertainty and new nature of COVID-19 means that other unconfirmed information that may appear "rumored" may be an important indicator of the behavior and impact of this new virus. Twitter, in particular, has taken…
While the COVID-19 pandemic continues its global devastation, numerous accompanying challenges emerge. One important challenge we face is to efficiently and effectively use recently gathered data and find computational tools to combat the COVID-19 infodemic, a typical information overloading problem. Novel coronavirus presents many questions without ready answers; its uncertainty and our eagerness in search of solutions offer a fertile environment for infodemic. It is thus necessary to combat the infodemic and make a concerted effort to confront COVID-19 and mitigate its negative impact in all…
Identifying and characterizing disinformation in political discourse on social media is critical to ensure the integrity of elections and democratic processes around the world. Persistent manipulation of social media has resulted in increased concerns regarding the 2020 U.S. Presidential Election, due to its potential to influence individual opinions and social dynamics. In this work, we focus on the identification of distorted facts, in the form of unreliable and conspiratorial narratives in election-related tweets, to characterize discourse manipulation prior to the election. We apply a dete…
This paper presents TrollHunter, an automated reasoning mechanism we used to hunt for trolls on Twitter during the COVID-19 pandemic in 2020. Trolls, poised to disrupt the online discourse and spread disinformation, quickly seized the absence of a credible response to COVID-19 and created a COVID-19 infodemic by promulgating dubious content on Twitter. To counter the COVID-19 infodemic, the TrollHunter leverages a unique linguistic analysis of a multi-dimensional set of Twitter content features to detect whether or not a tweet was meant to troll. TrollHunter achieved 98.5% accuracy, 75.4% prec…
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…
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…
In late 2019, the gravest pandemic in a century began spreading across the world. A state of uncertainty related to what has become known as SARS-CoV-2 has since fueled conspiracy narratives on social media about the origin, transmission and medical treatment of and vaccination against the resulting disease, COVID-19. Using social media intelligence to monitor and understand the proliferation of conspiracy narratives is one way to analyze the distribution of misinformation on the pandemic. We analyzed more than 9.5M German language tweets about COVID-19. The results show that only about 0.6% o…
University of Münster（明斯特大学）；University of Wolverhampton（伍尔弗汉普顿大学）；Birmingham City University（伯明翰城市大学）
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 …
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 …
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 Exeter（埃克塞特大学）；University of Kansas（堪萨斯大学）
Monitoring social discourse about COVID-19 vaccines is key to understanding how large populations perceive vaccination campaigns. We focus on 4765 unique popular tweets in English or Italian about COVID-19 vaccines between 12/2020 and 03/2021. One popular English tweet was liked up to 495,000 times, stressing how popular tweets affected cognitively massive populations. We investigate both text and multimedia in tweets, building a knowledge graph of syntactic/semantic associations in messages including visual features and indicating how online users framed social discourse mostly around the log…
University of Sheffield（谢菲尔德大学）；University of Duisburg-Essen（杜伊斯堡-埃森大学）
The spreading COVID-19 misinformation over social media already draws the attention of many researchers. According to Google Scholar, about 26000 COVID-19 related misinformation studies have been published to date. Most of these studies focusing on 1) detect and/or 2) analysing the characteristics of COVID-19 related misinformation. However, the study of the social behaviours related to misinformation is often neglected. In this paper, we introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation (e.g. comment or question to the misinformation). The…
This paper presents a multi-platform computational pipeline geared to identify social media posts discussing (known) conspiracy theories. We use 189 conspiracy claims collected by Snopes, and find 66k posts and 277k comments on Reddit, and 379k tweets discussing them. Then, we study how conspiracies are discussed on different Web communities and which ones are particularly influential in driving the discussion about them. Our analysis sheds light on how conspiracy theories are discussed and spread online, while highlighting multiple challenges in mitigating them.
Many governments have managed to control their COVID-19 outbreak with a simple message: keep the effective '$R$ number' $R<1$ to prevent widespread contagion and flatten the curve. This raises the question whether a similar policy could control dangerous online 'infodemics' of information, misinformation and disinformation. Here we show, using multi-platform data from the COVID-19 infodemic, that its online spreading instead encompasses a different dynamical regime where communities and users within and across independent platforms, sporadically form temporary active links on similar timescale…
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…
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…
TIB – Leibniz Information Centre for Science and Technology（莱布尼茨科学技术信息中心）；L3S Research Center, Leibniz University Hannover（汉诺威莱布尼茨大学L3S研究中心）
Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifically, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow…
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…
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 …
Timely access to accurate information is crucial during the COVID-19 pandemic. Prompted by key stakeholders' cautioning against an "infodemic", we study information sharing on Twitter from January through May 2020. We observe an overall surge in the volume of general as well as COVID-19-related tweets around peak lockdown in March/April 2020. With respect to engagement (retweets and likes), accounts related to healthcare, science, government and politics received by far the largest boosts, whereas accounts related to religion and sports saw a relative decrease in engagement. While the threat o…
The QAnon conspiracy posits that Satan-worshiping Democrats operate a covert child sex-trafficking operation, which Donald Trump is destined to expose and annihilate. Emblematic of the ease with which political misconceptions can spread through social media, QAnon originated in late 2017 and rapidly grew to shape the political beliefs of millions. To illuminate the process by which a conspiracy theory spreads, we report two computational studies examining the social network structure and semantic content of tweets produced by users central to the early QAnon network on Twitter. Using data mine…
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…
During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus wi…
COVID-19 vaccine hesitancy has increased concerns about vaccine uptake required to overcome the pandemic and protect public health. A critical factor associated with anti-vaccine attitudes is the information shared on social media. In this work, we investigate misinformation communities and narratives that can contribute to COVID-19 vaccine hesitancy. During the pandemic, anti-science and political misinformation/conspiracies have been rampant on social media. Therefore, we investigate misinformation and conspiracy groups and their characteristic behaviours in Twitter data collected on COVID-1…
The COVID-19 pandemic has resulted in a slew of misinformation, often described as an "infodemic". Whereas previous research has focused on the propagation of unreliable sources as a main vehicle of misinformation, the present study focuses on exploring the role of scientists whose views oppose the scientific consensus. Using Nobelists in Physiology and Medicine as a proxy for scientific consensus, we analyze two separate datasets: 15.8K tweets by 13.1K unique users on COVID-19 vaccines specifically, and 208K tweets by 151K unique users on COVID-19 broadly which mention the Nobelist names. Our…
In the age of social media, disasters and epidemics usher not only a devastation and affliction in the physical world, but also prompt a deluge of information, opinions, prognoses and advice to billions of internet users. The coronavirus epidemic of 2019-2020, or COVID-19, is no exception, with the World Health Organization warning of a possible "infodemic" of fake news. In this study, we examine the alternative narratives around the coronavirus outbreak through advertisements promoted on Facebook, the largest social media platform in the US. Using the new Facebook Ads Library, we discover adv…
Vaccines are promising tools to control the spread of COVID-19. An effective vaccination campaign requires government policies and community engagement, sharing experiences for social support, and voicing concerns to vaccine safety and efficiency. The increasing use of online social platforms allows us to trace large-scale communication and infer public opinion in real-time. We collected more than 100 million vaccine-related tweets posted by 8 million users and used the Latent Dirichlet Allocation model to perform automated topic modeling of tweet texts during the vaccination campaign in Japan…
Since the onset of the COVID-19 pandemic, vaccines have been an important topic in public discourse. The discussions around vaccines are polarized as some see them as an important measure to end the pandemic, and others are hesitant or find them harmful. This study investigates posts related to COVID-19 vaccines on Twitter and focuses on those which have a negative stance toward vaccines. A dataset of 16,713,238 English tweets related to COVID-19 vaccines was collected covering the period from March 1, 2020, to July 31, 2021. We used the Scikit-learn Python library to apply a support vector ma…
Indian Institute of Technology, Hyderabad（印度理工学院海得拉巴分校）；USC Information Sciences Institute（南加州大学信息科学研究所）
The problem of detecting bots, automated social media accounts governed by software but disguising as human users, has strong implications. For example, bots have been used to sway political elections by distorting online discourse, to manipulate the stock market, or to push anti-vaccine conspiracy theories that caused health epidemics. Most techniques proposed to date detect bots at the account level, by processing large amount of social media posts, and leveraging information from network structure, temporal dynamics, sentiment analysis, etc. In this paper, we propose a deep neural network b…
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…
Understanding how different online communities engage with COVID-19 misinformation is critical for public health response, as misinformation confined to a small, isolated community of users poses a different public health risk than misinformation being consumed by a large population spanning many diverse communities. Here we take a longitudinal approach that leverages tools from network science to study COVID-19 misinformation on Twitter. Our approach provides a means to examine the breadth of misinformation engagement using modest data needs and computational resources. We identify influentia…
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…
First identified in Wuhan, China, in December 2019, the outbreak of COVID-19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO). Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID-19. We first broadly search and investigate ~2,000 news publishers, from which 60 are identified with extreme [high or low…
As COVID-19 sweeps the globe, outcomes depend on effective relationships between the public and decision-makers. In the UK there were uncivil tweets to MPs about perceived UK tardiness to go into lockdown. The pandemic has led to increased attention on ministers with a role in the crisis. However, generally this surge has been civil. Prime minister Boris Johnson's severe illness with COVID-19 resulted in an unusual peak of supportive responses on Twitter. Those who receive more COVID-19 mentions in their replies tend to receive less abuse (significant negative correlation). Following Mr Johnso…
Massive amounts of misinformation have been observed to spread in uncontrolled fashion across social media. Examples include rumors, hoaxes, fake news, and conspiracy theories. At the same time, several journalistic organizations devote significant efforts to high-quality fact checking of online claims. The resulting information cascades contain instances of both accurate and inaccurate information, unfold over multiple time scales, and often reach audiences of considerable size. All these factors pose challenges for the study of the social dynamics of online news sharing. Here we introduce Ho…
