Varieties of corona news: a cross-national study on the foundations of online misinformation production during the COVID-19 pandemic.

Misinformation in the media is produced by hard-to-gauge thought mechanisms employed by individuals or collectivities. In this paper, we shed light on what the country-specific factors of falsehood production in the context of COVID-19 Pandemic might be. Collecting our evidence from the largest misinformation dataset used in the COVID-19 misinformation literature with close to 11,000 pieces of falsehood, we explore patterns of misinformation production by employing a variety of methodological tools including algorithms for text similarity, clustering, network distances, and other statistical tools. Covering news produced in a span of more than 14 months, our paper also differentiates itself by its use of carefully controlled hand-labeling of topics of falsehood. Findings suggest that country-level factors do not provide the strongest support for predicting outcomes of falsehood, except for one phenomenon: in countries with serious press freedom problems and low human development, the mostly unknown authors of misinformation tend to focus on similar content. In addition, the intensity of discussion on animals, predictions and symptoms as part of fake news is the biggest differentiator between nations; whereas news on conspiracies, medical equipment and risk factors offer the least explanation to differentiate. Based on those findings, we discuss some distinct public health and communication strategies to dispel misinformation in countries with particular characteristics. We also emphasize that a global action plan against misinformation is needed given the highly globalized nature of the online media environment. Supplementary Information: The online version contains supplementary material available at 10.1007/s42001-022-00193-5.

How Science Influencers Polarize Supportive and Skeptical Communities Around Politicized Science: A Cross-Platform and Over-Time Comparison

Contention over COVID-19 is only a recent example of increasing social division around science in the U.S. Many blame these divisions on actors who have strategically sowed doubt and distrust around expert supported positions and policies. However, this overlooks how scientists have fueled narratives of social and political conflict around science. This study explores how science influencers on social media have used group identity language in ways that may perpetuate narratives of intergroup conflict around science. Using computer-assisted content analytic methods, we examine how science influencers' use of group identity language has changed in response to recent events (Trump presidency, COVID-19 pandemic) and across different social media platforms (Twitter, Facebook, Instagram). While there are slight increases in group identity language between 2016 and 2021, different patterns across platforms suggest that science influencers use different platforms to perform multiple roles of engaging diverse audiences, building ingroup solidarity, and defending against outgroup criticism. © 2023 Taylor & Francis Group, LLC.

Cognitive Cascades within Media Ecosystems: Simulating Fragmentation, Selective Exposure and Media Tactics to Investigate Polarization

This work introduces a simple extension to the recent Cognitive Cascades model of Rabb et al. with modeling of multiple media agents, to begin to investigate how the media ecosystem might influence the spread of beliefs (such as beliefs around COVID-19 vaccination). We perform some initial simulations to see how parameters modeling audience fragmentation, selective exposure, and responsiveness of media agents to the beliefs of their subscribers influence polarization. We find that media ecosystem fragmentation and echo-chambers may not in themselves be as polarizing as initially postulated, in the absence of outside fixed media messages that are polarizing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Computational linguistic models for understanding attitude and behavior

Attitudes are often expressed in what people say and write, as well as the content they choose to interact with. With the proliferation of social media and other online content, we are able to understand how people express their attitudes through large-scale linguistic analyses. Further, people's attitudes and behaviors are often intertwined: attitude signals can be predictive of future behaviors, and conversely behavioral patterns can reveal underlying attitudes. This thesis explores the development of computational linguistic models to understand attitudes and behaviors. We surface the attitudes that people hold with respect to social roles (e.g., "professor," "mother") and compare them across different cultures using corpus-statistics models and dependency-based embedding models. Next, we look at how personal traits are predictive of behavior. To this end, we explore how we can incorporate implicit world knowledge into language models by predicting attitudes towards charitable giving. In this same direction, we examine traits, as expressed on social media, that are indicative of people likely to persist in pursuing self-improvement. We leverage linguistic characteristics such as expressed affect, writing style, and latent topics. Finally, we gain insight into how attitude and behavior give insight to each other by predicting attitudes towards philanthropic causes based on engagement behavior with newsletters and personal background information, using text-aware graph representation models. We also show how behavioral traits present in online communities are predictive of resilient attitude during the COVID-19 pandemic. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals' and communities' thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions. © 2020 Tsinghua University Press.

Twitter as research data Tools, costs, skill sets, and lessons learned.

Scholars increasingly use Twitter data to study the life sciences and politics. However, Twitter data collection tools often pose challenges for scholars who are unfamiliar with their operation. Equally important, although many tools indicate that they offer representative samples of the full Twitter archive, little is known about whether the samples are indeed representative of the targeted population of tweets. This article evaluates such tools in terms of costs, training, and data quality as a means to introduce Twitter data as a research tool. Further, using an analysis of COVID-19 and moral foundations theory as an example, we compared the distributions of moral discussions from two commonly used tools for accessing Twitter data (Twitter's standard APIs and third-party access) to the ground truth, the Twitter full archive. Our results highlight the importance of assessing the comparability of data sources to improve confidence in findings based on Twitter data. We also review the major new features of Twitter's API version 2.

Twitterati on COVID-19 pandemic-environment linkage: Insights from mining one year of tweets.

The COVID-19 pandemic seems to have had positive (although short-lived, e.g., reduction in pollution due to lockdown) as well as negative (e.g., increasing plastic pollution due to use of disposable masks, etc.) impacts on the environment. The pandemic-environment linkage also includes circumstances when regions experienced extreme weather events, such as floods and cyclones, and disaster management became challenging. This study aims to examine the trends in public discourses on Twitter on these interactions between the pandemic and environment. The present study follows the most recent literature on understanding public perceptions - which acknowledges Twitter to be an abundant source of information on public discussions on any global issue, including the pandemic. A Python-based code is developed to extract Twitter data spanning over a year, and analyze the presence of covid-environment related keywords and other attributes. It is found that the Twitterati aggressively viewed the impacts (such as economic slowdown and high mortality) of the pandemic as miniatures of the results of future climate change. The community was also highly concerned about the varying air and plastic pollution levels with the change in lockdown and covid prevention policies. Extreme weather events were a high-frequency topic when they impacted countries such as India, the USA, Australia, the Philippines and Vietnam. This study makes a novel attempt to provide an overview of public discourses on the pandemic-environment linkage and; can be a crucial addition to the literature on assessing public perception of environmental threats through Twitter data mining.

How was the coronavirus vaccine accepted on Twitter? A computational analysis using big data in Japan

Purpose: This study aims to reveal how the COVID-19 vaccine was accepted in the Japanese Twitter-sphere. This study explores how the topics related to the vaccine promotion project changed on Twitter and how the topics that were likely to spread changed during the vaccine promotion project. Design/methodology/approach: The computational social science methodology was adopted. This study collected all tweets containing the word "vaccine” using the Twitter API from March to October 2021 and conducted the following analysis: analyzing frequent words and identifying topics likely to spread through the cosine similarity and Tobit model. Findings: First, vaccine hesitancy–related words were frequently mentioned during the vaccine introduction and dissemination periods and had diffusing power only during the former period. Second, vaccine administration–related words were frequently mentioned and diffused through April to May and had diffusing power throughout the period. The background to these findings is that the sentiment of longing for vaccines outweighed that of hesitancy toward vaccines during this period. Originality/value: This study finds that the timing of the rise in vaccine hesitation sentiment and the timing of the start of vaccine supply were misaligned. This is one of the reasons that Japan, which originally exhibited strong vaccine hesitancy, did not face vaccine hesitancy in the COVID-19 vaccine promotion project. © 2022, Emerald Publishing Limited.

The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study.

Background: Unlike past pandemics, COVID-19 is different to the extent that there is an unprecedented surge in both peer-reviewed and preprint research publications, and important scientific conversations about it are rampant on online social networks, even among laypeople. Clearly, this new phenomenon of scientific discourse is not well understood in that we do not know the diffusion patterns of peer-reviewed publications vis-à-vis preprints and what makes them viral. Objective: This paper aimed to examine how the emotionality of messages about preprint and peer-reviewed publications shapes their diffusion through online social networks in order to inform health science communicators' and policy makers' decisions on how to promote reliable sharing of crucial pandemic science on social media. Methods: We collected a large sample of Twitter discussions of early (January to May 2020) COVID-19 medical research outputs, which were tracked by Altmetric, in both preprint servers and peer-reviewed journals, and conducted statistical analyses to examine emotional valence, specific emotions, and the role of scientists as content creators in influencing the retweet rate. Results: Our large-scale analyses (n=243,567) revealed that scientific publication tweets with positive emotions were transmitted faster than those with negative emotions, especially for messages about preprints. Our results also showed that scientists' participation in social media as content creators could accentuate the positive emotion effects on the sharing of peer-reviewed publications. Conclusions: Clear communication of critical science is crucial in the nascent stage of a pandemic. By revealing the emotional dynamics in the social media sharing of COVID-19 scientific outputs, our study offers scientists and policy makers an avenue to shape the discussion and diffusion of emerging scientific publications through manipulation of the emotionality of tweets. Scientists could use emotional language to promote the diffusion of more reliable peer-reviewed articles, while avoiding using too much positive emotional language in social media messages about preprints if they think that it is too early to widely communicate the preprint (not peer reviewed) data to the public.

Computational linguistic models for understanding attitude and behavior

Attitudes are often expressed in what people say and write, as well as the content they choose to interact with. With the proliferation of social media and other online content, we are able to understand how people express their attitudes through large-scale linguistic analyses. Further, people's attitudes and behaviors are often intertwined: attitude signals can be predictive of future behaviors, and conversely behavioral patterns can reveal underlying attitudes. This thesis explores the development of computational linguistic models to understand attitudes and behaviors. We surface the attitudes that people hold with respect to social roles (e.g., "professor," "mother") and compare them across different cultures using corpus-statistics models and dependency-based embedding models. Next, we look at how personal traits are predictive of behavior. To this end, we explore how we can incorporate implicit world knowledge into language models by predicting attitudes towards charitable giving. In this same direction, we examine traits, as expressed on social media, that are indicative of people likely to persist in pursuing self-improvement. We leverage linguistic characteristics such as expressed affect, writing style, and latent topics. Finally, we gain insight into how attitude and behavior give insight to each other by predicting attitudes towards philanthropic causes based on engagement behavior with newsletters and personal background information, using text-aware graph representation models. We also show how behavioral traits present in online communities are predictive of resilient attitude during the COVID-19 pandemic. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

The voice of few, the opinions of many: evidence of social biases in Twitter COVID-19 fake news sharing.

Online platforms play a relevant role in the creation and diffusion of false or misleading news. Concerningly, the COVID-19 pandemic is shaping a communication network which reflects the emergence of collective attention towards a topic that rapidly gained universal interest. Here, we characterize the dynamics of this network on Twitter, analysing how unreliable content distributes among its users. We find that a minority of accounts is responsible for the majority of the misinformation circulating online, and identify two categories of users: a few active ones, playing the role of 'creators', and a majority playing the role of 'consumers'. The relative proportion of these groups (approx. 14% creators-86% consumers) appears stable over time: consumers are mostly exposed to the opinions of a vocal minority of creators (which are the origin of 82% of fake content in our data), that could be mistakenly understood as representative of the majority of users. The corresponding pressure from a perceived majority is identified as a potential driver of the ongoing COVID-19 infodemic.

Health Journalists' Social Media Sourcing During the Early Outbreak of the Public Health Emergency

Although journalists' social media sourcing can empower non-elite sources and diversify public discussions, counterarguments maintain that social media sourcing relies on a small group of elites and reinforces social division. To contribute to that debate, we examined how health journalists from the mainstream news organizations in the U.S. used Twitter's @mention for sourcing during the first three months of the COVID-19 outbreak. Using a sample of public Twitter posts published by the journalists, we formed co-@mentioned networks (i.e., two sources were connected if @mentioned in the same post) to examine the structure of the networks and identify important sourcing informants. Among the results, elite sources (e.g., health journalists and health experts in the public sector) and influential users (i.e., verified users with a large number of followers and who post frequently) dominated the sourcing repertoire. Moreover, the networks were fragmented because the sources were clustered into several close-knit subgroups. Analyzing exponential random graph models to examine the formation mechanism of the networks revealed that, as the pandemic's severity increased, influential users played a more salient role in the sourcing repertoire, and a homogeneous cluster consisting of journalists and news organizations emerged.

Is gender-based violence a confluence of culture? Empirical evidence from social media.

Gender-based violence (GBV) has been plaguing our society for long back. The severity of GBV has spurred research around understanding the causes and factors leading to GBV. Understanding factors and causes leading to GBV is helpful in planning and executing efficient policies to curb GBV. Past researches have claimed a country's culture to be one of the driving reasons behind GBV. The culture of a country consists of cultural norms, societal rules, gender-based stereotypes, and social taboos which provoke GBV. These claims are supported by theoretical or small-scale survey-based research that suffers from under-representation and biases. With the advent of social media and, more importantly, location-tagged social media, huge ethnographic data are available, creating a platform for many sociological research. In this article, we also utilize huge social media data to verify the claim of confluence between GBV and the culture of a country. We first curate GBV content from different countries by collecting a large amount of data from Twitter. In order to explore the relationship between a country's culture and GBV content, we performed correlation analyses between a country's culture and its GBV content. The correlation results are further re-validated using graph-based methods. Through the findings of this research, we observed that countries with similar cultures also show similarity in GBV content, thus reconfirming the relationship between GBV and culture.

Understanding COVID-19 Vaccine Reaction Through Comparative Analysis on Twitter

Although multiple COVID-19 vaccines have been available for several months now, vaccine hesitancy continues to be at high levels in the United States. In part, the issue has also become politicized, especially since the presidential election in November. Understanding vaccine hesitancy during this period in the context of social media, including Twitter, can provide valuable guidance both to computational social scientists and policy makers. Rather than studying a single Twitter corpus, this paper takes a novel view of the problem by comparatively studying two Twitter datasets collected between two different time periods (one before the election, and the other, a few months after) using the same, carefully controlled data collection and filtering methodology. Our results show that there was a significant shift in discussion from politics to COVID-19 vaccines from fall of 2020 to spring of 2021. By using clustering and machine learning-based methods in conjunction with sampling and qualitative analysis, we uncover several fine-grained reasons for vaccine hesitancy, some of which have become more (or less) important over time. Our results also underscore the intense polarization and politicization of this issue over the last year. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Sentiment Analysis on COVID-19 News Videos Using Machine Learning Techniques

Coronavirus disease (COVID-19) has affected all walks of human life most adversely, from entertainment to education. The whole world is confronting this deadly virus, and no country in this world remains untouched during this pandemic. From the early days of reporting this virus from many parts of the world, many news videos on the same got uploaded in various online platforms such as YouTube, Dailymotion, and Vimeo. Even though the content of many of those videos was unauthentic, people watched them and expressed their views and opinions as comments. Analysing these comments can unearth the patterns hidden in them to study people’s responses to videos on COVID-19. This paper proposes a sentiment analysis approach on people’s response towards such videos, using text mining and machine learning. This work implements different machine learning algorithms to classify people’s sentiments and also uses text mining principles for finding out several latent themes, from the comments collected from YouTube. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Joint theme and event based rating model for identifying relevant influencers on Twitter: COVID-19 case study

The continuous proliferation of social media platforms and the exponential increase in users’ engagement are impacting social behavior and leading to various challenges, including the detection and identification of key influencers. In fact the opinions of these influencers are at the core of decision-making strategies, and are leading trends on the virtual social media landscape. Moreover, influencers might play a crucial role when it comes to misinformation and conspiracy during sensitive, controversial and trending events. However, due to the dynamic and unrestricted nature of social media, and diversity of targeted topics and audiences, identifying and ranking key influencers that are impactful, credible, and knowledgeable about their specialist topic or event remains an evolving and open research paradigm. In this paper, we address the aforementioned problem by proposing a novel influence rating and ranking scheme to identify key and highly influential users for a certain event over Twitter using a mixed theme/event based approach while considering historical data and profile reputation. We further apply our approach to a global pandemic case study, the novel Coronavirus, and conduct performance analysis. The presented experimental results and theoretical analysis explore the relevance of our proposed scheme for identifying and ranking reputable and theme/event related influencers.

Moral Emotions Shape the Virality of COVID-19 Misinformation on Social Media

While false rumors pose a threat to the successful overcoming of the COVID-19 pandemic, an understanding of how rumors diffuse in online social networks is - even for non-crisis situations - still in its infancy. Here we analyze a large sample consisting of COVID-19 rumor cascades from Twitter that have been fact-checked by third-party organizations. The data comprises N = 10,610 rumor cascades that have been retweeted more than 24 million times. We investigate whether COVID-19 misinformation spreads more viral than the truth and whether the differences in the diffusion of true vs. false rumors can be explained by the moral emotions they carry. We observe that, on average, COVID-19 misinformation is more likely to go viral than truthful information. However, the veracity effect is moderated by moral emotions: false rumors are more viral than the truth if the source tweets embed a high number of other-condemning emotion words, whereas a higher number of self-conscious emotion words is linked to a less viral spread. The effects are pronounced both for health misinformation and false political rumors. These findings offer insights into how true vs. false rumors spread and highlight the importance of considering emotions from the moral emotion families in social media content. © 2022 Owner/Author.

Deep Learning-Based Sentiment Analysis on COVID-19 News Videos

Coronavirus disease (COVID-19) has adversely affected all walks of human life. The whole world is confronting this deadly virus, and no country in this world remains untouched during this pandemic. There are several online news videos related to COVID-19 that are shared on various online platforms such as YouTube, DailyMotion, and Vimeo. There were several arguments on the genuineness of the contents, people watch them, share them, and most importantly express their views and opinions as comments on those platforms. Analyzing these comments can unearth the patterns hidden in them to study people's responses to videos on COVID-19. This paper proposes a deep learning-based sentiment analysis approach to people's response toward online COVID-19 video news. This work implements different deep learning approaches such as LSTM, Bi-LSTM, CNN, and GRU to classify sentiment from the comments collected from YouTube. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

La agenda de los grupos de interés frente a la COVID-19: el rastro digital en Twitter

Los grupos de interés están teniendo un rol fundamental en la resolución de los problemas generados por la pandemia COVID-19 porque abordan múltiples asuntos y representan a los grupos sociales en torno a un reto social sin precedentes. Múltiples organizaciones expresan cada día las preocupaciones y demandas de sus constituyentes, forjando de esta manera una agenda en la que se debate cómo hacer frente a los múltiples efectos de la pandemia en diversos sectores. Sin embargo, dada la dinámica de atención propia de la situación de emergencia y la diversidad de los temas, es difícil seguir la diversidad y complejidad de múltiples actores y asuntos. Este artículo describe la agenda de los grupos de interés en España a partir de las publicaciones en la red social Twitter de las 140 organizaciones más activas entre marzo de 2018 y marzo de 2021. Mediante la clasificación automática de texto es posible concluir que la atención agregada por tipos de grupos de interés a los principales asuntos de la agenda varía poco a partir del estallido de la crisis provocada por la pandemia. La atención a las dimensiones sanitarias, sociopolíticas y económicas relacionadas con la COVID-19 sigue patrones similares entre los diferentes tipos de grupos y es transversal a los asuntos de la agenda. Estos resultados demuestran que los grupos de interés continúan ejerciendo sus funciones de representación de intereses sin alterar significativamente su comportamiento en respuesta a la crisis.Alternate : Interest groups are playing a fundamental role in solving the problems generated by the COVID-19 pandemic, as they address multiple issues and represent social groups around an unprecedented social challenge. Multiple organizations express their constituents’concerns and demands every day, thus forging an agenda in which they debate how to deal with the multiple effects of the pandemic in various sectors. However, given the dynamics of attention inherent to the emergency situation and the diversity of issues, it is difficult to follow the diversity and complexity of multiple actors and issues. This article describes the agenda of interest groups in Spain, based on the publications on Twitter by the 140 most active organizations between March 2018 and March 2021. Using automated text classification, it is possible to conclude that the aggregate attention by types of interest groups to the main items on the agenda vary little after the outbreak of the crisis caused by the pandemic. Attention to the health, socio-political and economic dimensions related to COVID-19 follows similar patterns among the different types of groups and is transversal to the issues on the agenda. These results show that interest groups continue to carry out their interest representation function without significantly altering their behaviour in response to the crisis.

Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text

Understanding who blames or supports whom in news text is a critical research question in computational social science. Traditional methods and datasets for sentiment analysis are, however, not suitable for the domain of political text as they do not consider the direction of sentiments expressed between entities. In this paper, we propose a novel NLP task of identifying directed sentiment relationship between political entities from a given news document, which we call directed sentiment extraction. From a million-scale news corpus, we construct a dataset of news sentences where sentiment relations of political entities are manually annotated. We present a simple but effective approach for utilizing a pretrained transformer, which infers the target class by predicting multiple question-answering tasks and combining the outcomes. We demonstrate the utility of our proposed method for social science research questions by analyzing positive and negative opinions between political entities in two major events: 2016 U.S. presidential election and COVID-19. The newly proposed problem, data, and method will facilitate future studies on interdisciplinary NLP methods and applications. © 2021 Association for Computational Linguistics