Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering

On network backbone extraction for modeling online collective behavior

Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation.

Social distance "nudge:" a context aware mHealth intervention in response to COVID pandemics

The impact of the COVID pandemic to our society is unprecedented in our time. As coronavirus mutates, maintaining social distance remains an essential step in defending personal as well as public health. This study conceptualizes the social distance "nudge" and explores the efficacy of mHealth digital intervention, while developing and validating a choice architecture that aims to influence users' behavior in maintaining social distance for their own self-interest. End-user nudging experiments were conducted via a mobile phone app that was developed as a research artifact. The accuracy of social distance nudging was validated in both United States and Japan. Future work will consider behavioral studies to better understand the effectiveness of this digital nudging intervention.

Mapping state-sponsored information operations with multi-view modularity clustering

This paper presents a new computational framework for mapping state-sponsored information operations into distinct strategic units. Utilizing a novel method called multi-view modularity clustering (MVMC), we identify groups of accounts engaged in distinct narrative and network information maneuvers. We then present an analytical pipeline to holistically determine their coordinated and complementary roles within the broader digital campaign. Applying our proposed methodology to disclosed Chinese state-sponsored accounts on Twitter, we discover an overarching operation to protect and manage Chinese international reputation by attacking individual adversaries (Guo Wengui) and collective threats (Hong Kong protestors), while also projecting national strength during global crisis (the COVID-19 pandemic). Psycholinguistic tools quantify variation in narrative maneuvers employing hateful and negative language against critics in contrast to communitarian and positive language to bolster national solidarity. Network analytics further distinguish how groups of accounts used network maneuvers to act as balanced operators, organized masqueraders, and egalitarian echo-chambers. Collectively, this work breaks methodological ground on the interdisciplinary application of unsupervised and multi-view methods for characterizing not just digital campaigns in particular, but also coordinated activity more generally. Moreover, our findings contribute substantive empirical insights around how state-sponsored information operations combine narrative and network maneuvers to achieve interlocking strategic objectives. This bears both theoretical and policy implications for platform regulation and understanding the evolving geopolitical significance of cyberspace.

Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis

BACKGROUND

The onset of the COVID-19 pandemic and the consequent "infodemic" increased concerns about Twitter's role in advancing antivaccination messages, even before a vaccine became available to the public. New computational methods allow for analysis of cross-platform use by tracking links to websites shared over Twitter, which, in turn, can uncover some of the content and dynamics of information sources and agenda-setting processes. Such understanding can advance theory and efforts to reduce misinformation.

OBJECTIVE

Informed by agenda-setting theory, this study aimed to identify the content and temporal patterns of websites shared in vaccine-related tweets posted to COVID-19 conversations on Twitter between February and June 2020.

METHODS

We used triangulation of data analysis methods. Data mining consisted of the screening of around 5 million tweets posted to COVID-19 conversations to identify tweets that related to vaccination and including links to websites shared within these tweets. We further analyzed the content the 20 most-shared external websites using a mixed methods approach.

RESULTS

Of 841,896 vaccination-related tweets identified, 185,994 (22.1%) contained links to specific websites. A wide range of websites were shared, with the 20 most-tweeted websites constituting 14.5% (27,060/185,994) of the shared websites and typically being shared for only 2 to 3 days. Traditional media constituted the majority of these 20 websites, along with other social media and governmental sources. We identified markers of inauthentic propagation for some of these links.

CONCLUSIONS

The topic of vaccination was prevalent in tweets about COVID-19 early in the pandemic. Sharing websites was a common communication strategy, and its "bursty" pattern and inauthentic propagation strategies pose challenges for health promotion efforts. Future studies should consider cross-platform use in dissemination of health information and in counteracting misinformation.

Social media analytics of the Internet of Things

The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This research illuminates the public view of the Internet of Things through a content-based and network analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current COVID-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry.

Social mobilization and polarization can create volatility in COVID-19 pandemic control

During the COVID-19 pandemic, political polarization has emerged as a significant threat that inhibits coordinated action of central and local institutions reducing the efficacy of non-pharmaceutical interventions (NPIs). Yet, it is not well-understood to what extent polarization can affect grass-roots, voluntary social mobilization targeted at mitigating the pandemic spread. Here, we propose a polarized mobilization model amidst the pandemic for demonstrating the differential responses to COVID-19 as mediated by the USA's political landscape. We use a novel dataset and models from time-critical social mobilization competitions, voting records, and a high-resolution county-wise friendship network. Our simulations show that a higher degree of polarization impedes the overall spread of mobilization and leads to a highly-heterogeneous impact among states. Our hypothetical compliance campaign to mitigate COVID-19 spread predicts grass-roots mitigation strategies' success before the dates of actual lockdowns in identically polarized states with more than three times of success rate than oppositely polarized states. Finally, we analyze the coupling of social mobilization leading to unrest and the growth of COVID-19 infections. These findings highlight social mobilization as both a collective precautionary measure and a potential threat to countermeasures, together with a warning message that the emerging polarization can be a significant hurdle of NPIs relying on coordinated action.