Corrigendum: Do you hear the people sing? Comparison of synchronized URL and narrative themes in 2020 and 2023 French protests.

[This corrects the article DOI: 10.3389/fdata.2023.1221744.].

Do you hear the people sing? Comparison of synchronized URL and narrative themes in 2020 and 2023 French protests.

Introduction: France has seen two key protests within the term of President Emmanuel Macron: one in 2020 against Islamophobia, and another in 2023 against the pension reform. During these protests, there is much chatter on online social media platforms like Twitter. Methods: In this study, we aim to analyze the differences between the online chatter of the 2 years through a network-centric view, and in particular the synchrony of users. This study begins by identifying groups of accounts that work together through two methods: temporal synchronicity and narrative similarity. We also apply a bot detection algorithm to identify bots within these networks and analyze the extent of inorganic synchronization within the discourse of these events. Results: Overall, our findings suggest that the synchrony of users in 2020 on Twitter is much higher than that of 2023, and there are more bot activity in 2020 compared to 2023.

Role of masks in mitigating viral spread on networks.

Masks have remained an important mitigation strategy in the fight against COVID-19 due to their ability to prevent the transmission of respiratory droplets between individuals. In this work, we provide a comprehensive quantitative analysis of the impact of mask-wearing. To this end, we propose a novel agent-based model of viral spread on networks where agents may either wear no mask or wear one of several types of masks with different properties (e.g., cloth or surgical). We derive analytical expressions for three key epidemiological quantities: The probability of emergence, the epidemic threshold, and the expected epidemic size. In particular, we show how the aforementioned quantities depend on the structure of the contact network, viral transmission dynamics, and the distribution of the different types of masks within the population. Through extensive simulations, we then investigate the impact of different allocations of masks within the population and tradeoffs between the outward efficiency and inward efficiency of the masks. Interestingly, we find that masks with high outward efficiency and low inward efficiency are most useful for controlling the spread in the early stages of an epidemic, while masks with high inward efficiency but low outward efficiency are most useful in reducing the size of an already large spread. Last, we study whether degree-based mask allocation is more effective in reducing the probability of epidemic as well as epidemic size compared to random allocation. The result echoes the previous findings that mitigation strategies should differ based on the stage of the spreading process, focusing on source control before the epidemic emerges and on self-protection after the emergence.

Localizing COVID-19 Misinformation: A Case Study of Tracking Twitter Pandemic Narratives in Pennsylvania Using Computational Network Science.

The recent COVID-19 outbreak has highlighted the importance of effective communication strategies to control the spread of the virus and debunk misinformation. By using accurate narratives, both online and offline, we can motivate communities to follow preventive measures and shape attitudes toward them. However, the abundance of misinformation stories can lead to vaccine hesitancy, obstructing the timely implementation of preventive measures, such as vaccination. Therefore, it is crucial to create appropriate and community-centered solutions based on regional data analysis to address mis/disinformation narratives and implement effective countermeasures specific to the particular geographic area.In this case study, we have attempted to create a research pipeline to analyze local narratives on social media, particularly Twitter, to identify misinformation spread locally, using the state of Pennsylvania as an example. Our proposed methodology pipeline identifies main communication trends and misinformation stories for the major cities and counties in southwestern PA, aiming to assist local health officials and public health specialists in instantly addressing pandemic communication issues, including misinformation narratives. Additionally, we investigated anti-vax actors' strategies in promoting harmful narratives. Our pipeline includes data collection, Twitter influencer analysis, Louvain clustering, BEND maneuver analysis, bot identification, and vaccine stance detection. Public health organizations and community-centered entities can implement this data-driven approach to health communication to inform their pandemic strategies.

Gender dynamics on Twitter during the 2020 U.S. Democratic presidential primary.

The Twitter social network for each of the top five U.S. Democratic presidential candidates in 2020 was analyzed to determine if there were any differences in the treatment of the candidates. This data set was collected from discussions of the presidential primary between December 2019 through April 2020. It was then separated into five sets,  one for each candidate. We found that the most discussed candidates, President Biden and Senator Sanders, received by far the most engagement from verified users and news agencies even before the Iowa caucuses, which was ultimately won by Mayor Buttigieg. The most popular candidates were also generally targeted more frequently by bots, trolls, and other aggressive users. However, the abusive language targeting the top two female candidates, Senators Warren and Klobuchar, included slightly more gendered and sexist language compared with the other candidates. Additionally, sexist slurs that ordinarily describe women were used more frequently than male slurs in all candidate data sets. Our results indicate that there may still be an undercurrent of sexist stereotypes permeating the social media conversation surrounding female U.S. presidential candidates.

Investigating coordinated account creation using burst detection and network analysis.

Democracies around the world face the threat of manipulation of their electorates via coordinated online influence campaigns. Researchers have responded by developing valuable methods for finding automated accounts and identifying false information, but these valiant efforts often fall into a cat-and-mouse game with perpetrators who constantly change their behavior. This has forced several researchers to go beyond the detection of individual malicious actors by instead identifying the coordinated activity that propels potent information operations. In this vein, we provide rigorous quantitative evidence for the notion that sudden increases in Twitter account creations may provide early warnings of online information operations. Analysis of fourteen months of tweets discussing the 2020 U.S. elections revealed that accounts created during bursts exhibited more similar behavior, showed more agreement on mail-in voting and mask wearing, and were more likely to be bots and share links to low-credibility sites. In concert with other techniques for detecting nefarious activity, social media platforms could temporarily limit the influence of accounts created during these bursts. Given the advantages of combining multiple anti-misinformation methods, we join others in presenting a case for the need to develop more integrable methods for countering online influence campaigns. Supplementary Information: The online version contains supplementary material available at 10.1186/s40537-023-00695-7.

A combined synchronization index for evaluating collective action social media.

Social media has provided a citizen voice, giving rise to grassroots collective action, where users deploy a concerted effort to disseminate online narratives and even carry out offline protests. Sometimes these collective action are aided by inorganic synchronization, which arise from bot actors. It is thus important to identify the synchronicity of emerging discourse on social media and the indications of organic/inorganic activity within the conversations. This provides a way of profiling an event for possibility of offline protests and violence. In this study, we build on past definitions of synchronous activity on social media- simultaneous user action-and develop a Combined Synchronization Index (CSI) which adopts a hierarchical approach in measuring user synchronicity. We apply this index on six political and social activism events on Twitter and analyzed three action types: synchronicity by hashtag, URL and @mentions.The CSI provides an overall quantification of synchronization across all action types within an event, which allows ranking of a spectrum of synchronicity across the six events. Human users have higher synchronous scores than bot users in most events; and bots and humans exhibits the most synchronized activities across all events as compared to other pairs (i.e., bot-bot and human-human). We further rely on the harmony and dissonance of CSI-Network scores with network centrality metrics to observe the presence of organic/inorganic synchronization. We hope this work aids in investigating synchronized action within social media in a collective manner.

Coordinating Narratives Framework for cross-platform analysis in the 2021 US Capitol riots.

Coordinated disinformation campaigns are used to influence social media users, potentially leading to offline violence. In this study, we introduce a general methodology to uncover coordinated messaging through an analysis of user posts on Parler. The proposed Coordinating Narratives Framework constructs a user-to-user coordination graph, which is induced by a user-to-text graph and a text-to-text similarity graph. The text-to-text graph is constructed based on the textual similarity of Parler and Twitter posts. We study three influential groups of users in the 6 January 2020 Capitol riots and detect networks of coordinated user clusters that post similar textual content in support of disinformation narratives related to the U.S. 2020 elections. We further extend our methodology to Twitter tweets to identify authors that share the same disinformation messaging as the aforementioned Parler user groups.

Cross-platform information spread during the January 6th capitol riots.

Social media has become an integral component of the modern information system. An average person typically has multiple accounts across different platforms. At the same time, the rise of social media facilitates the spread of online mis/disinformation narratives within and across these platforms. In this study, we characterize the coordinated information dissemination of information laden with mis- and disinformation narratives within and across two platforms, Parler and Twitter, during the online discourse surrounding the January 6th 2021 Capitol Riots event. Through the use of username similarity, we discover joint theme endorsements between both platforms. Using anomalously high volume of shared-link matches of external websites and YouTube videos, we discover separate information consumption habits between both platforms, with very few common sources of information between users of the different platforms. However, through analyzing the similarity of the texts with Locality Sensitive Hashing of constructed text vectors, we identify similar narratives between the platforms despite separate consumption of external websites, highlighting the similarities and differences of information spread within and between the two social media environments.

How disinformation operations against Russian opposition leader Alexei Navalny influence the international audience on Twitter.

Previous research dedicated a lot of effort to investigation of the activities of the Internet Research Agency, a Russia-based troll factory, as well as other information operations. However, those studies are mostly focused on the 2016 U.S. presidential election, Brexit, and other major international political events. In this study, we have attempted to analyze how narratives about a domestic issue in Russia are used by malicious actors to promote harmful discourses globally and persuade an international audience on Twitter. We have identified bot and troll activities related to the Twitter discussions of a Russian opposition leader Alexei Navalny using social network analysis and bot detection. We have also implemented the BEND framework to find persuasion maneuvers that are used by bots in conversations about Navalny and found attempts to manipulate the opinion of the international audience on Twitter. Our findings have demonstrated that there is a significant presence of bot activities in information operations against Alexei Navalny as one of the leaders of the Russian opposition. We have observed how the Russian domestic issue is framed in the context of Russian confrontation with the West and how it is used to promote hostile narratives either against Navalny, an opposition movement, or democratic values. Many agents that we have identified pretend to be English speakers, who exhibit hostile attitudes towards Navalny and the Western democracies, express skepticism and distort the facts, promote a lack of trust in the democratic institutions as well as spread disinformation and conspiracy theories.

Adolescent-Adult Social Networks and Experiences of Violence Among Black Youth in Neighborhoods With High Levels of Community Violence.

PURPOSE: Adult support is inversely linked to health-affecting risk behaviors. This study aimed to describe adolescent-adult support network structure and quality, and to analyze associations among network properties, strength of emotional and instrumental support, and violence involvement among predominantly Black youth residing in neighborhoods with high levels of community violence. METHODS: One hundred six youth from urban neighborhoods with high levels of community violence in Pittsburgh, PA completed egocentric social network surveys describing adult supports, measures of support across contexts, and past 30-day violence perpetration, victimization, and witnessing. Forty youth-identified adults completed complementary social network surveys. Poisson regression examined associations among strength of social support, adults' violence experiences, and youths' violence experiences. RESULTS: Mean youth participant age was 16.7 years, 56% self-identified as female, and 84% as Black or African-American. Youth and adult participants reported high levels of violence exposure and involvement. Youth identified a mean of 4.8 adult supports. Identifying at least one immediate family member in their network was inversely related to violence perpetration (adjusted incidence rate ratio [aIRR] 0.44, 95% confidence interval [CI] 0.22-0.89), victimization (aIRR 0.42, 95% CI 0.25-0.72), and witnessing (aIRR 0.48, 95% CI 0.35-0.64). The percent of adult supports involved in violence was directly associated with violence perpetration (aIRR 1.81, 95% CI 1.07-3.07), victimization (aIRR 1.95, 95% CI 1.09-3.45), and witnessing (aIRR 1.85, 95% CI 1.25-2.73). Few associations emerged between the structure of youth-reported adolescent-adult social networks and violence. DISCUSSION: Network-based interventions combined with healing-centered services attuned to violence experiences among Black youth and their adult supports may offer opportunities to leverage youths' existing adult support network and reduce violence.

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.

Duped by Bots: Why Some are Better than Others at Detecting Fake Social Media Personas.

OBJECTIVE: We examine individuals' ability to detect social bots among Twitter personas, along with participant and persona features associated with that ability. BACKGROUND: Social media users need to distinguish bots from human users. We develop and demonstrate a methodology for assessing those abilities, with a simulated social media task. METHOD: We analyze performance from a signal detection theory perspective, using a task that asked lay participants whether each of 50 Twitter personas was a human or social bot. We used the agreement of two machine learning models to estimate the probability of each persona being a bot. We estimated the probability of participants indicating that a persona was a bot with a generalized linear mixed-effects model using participant characteristics (social media experience, analytical reasoning, and political views) and stimulus characteristics (bot indicator score and political tone) as regressors. RESULTS: On average, participants had modest sensitivity (d') and a criterion that favored responding "human." Exploratory analyses found greater sensitivity for participants (a) with less self-reported social media experience, (b) greater analytical reasoning ability, and (c) who were evaluating personas with opposing political views. Some patterns varied with participants' political identity. CONCLUSIONS: Individuals have limited ability to detect social bots, with greater aversion to mistaking bots for humans than vice versa. Greater social media experience and myside bias appeared to reduce performance, as did less analytical reasoning ability. APPLICATION: These patterns suggest the need for interventions, especially when users feel most familiar with social media.

Social-Cyber Maneuvers During the COVID-19 Vaccine Initial Rollout: Content Analysis of Tweets.

BACKGROUND: During the time surrounding the approval and initial distribution of Pfizer-BioNTech's COVID-19 vaccine, large numbers of social media users took to using their platforms to voice opinions on the vaccine. They formed pro- and anti-vaccination groups toward the purpose of influencing behaviors to vaccinate or not to vaccinate. The methods of persuasion and manipulation for convincing audiences online can be characterized under a framework for social-cyber maneuvers known as the BEND maneuvers. Previous studies have been conducted on the spread of COVID-19 vaccine disinformation. However, these previous studies lacked comparative analyses over time on both community stances and the competing techniques of manipulating both the narrative and network structure to persuade target audiences. OBJECTIVE: This study aimed to understand community response to vaccination by dividing Twitter data from the initial Pfizer-BioNTech COVID-19 vaccine rollout into pro-vaccine and anti-vaccine stances, identifying key actors and groups, and evaluating how the different communities use social-cyber maneuvers, or BEND maneuvers, to influence their target audiences and the network as a whole. METHODS: COVID-19 Twitter vaccine data were collected using the Twitter application programming interface (API) for 1-week periods before, during, and 6 weeks after the initial Pfizer-BioNTech rollout (December 2020 to January 2021). Bot identifications and linguistic cues were derived for users and tweets, respectively, to use as metrics for evaluating social-cyber maneuvers. Organization Risk Analyzer (ORA)-PRO software was then used to separate the vaccine data into pro-vaccine and anti-vaccine communities and to facilitate identification of key actors, groups, and BEND maneuvers for a comparative analysis between each community and the entire network. RESULTS: Both the pro-vaccine and anti-vaccine communities used combinations of the 16 BEND maneuvers to persuade their target audiences of their particular stances. Our analysis showed how each side attempted to build its own community while simultaneously narrowing and neglecting the opposing community. Pro-vaccine users primarily used positive maneuvers such as excite and explain messages to encourage vaccination and backed leaders within their group. In contrast, anti-vaccine users relied on negative maneuvers to dismay and distort messages with narratives on side effects and death and attempted to neutralize the effectiveness of the leaders within the pro-vaccine community. Furthermore, nuking through platform policies showed to be effective in reducing the size of the anti-vaccine online community and the quantity of anti-vaccine messages. CONCLUSIONS: Social media continues to be a domain for manipulating beliefs and ideas. These conversations can ultimately lead to real-world actions such as to vaccinate or not to vaccinate against COVID-19. Moreover, social media policies should be further explored as an effective means for curbing disinformation and misinformation online.

Operation gridlock: opposite sides, opposite strategies.

Twitter and other social media platforms are important tools for competing groups to push their preferred messaging and respond to opposing views. Special attention has been paid to the role these tools play in times of emergency and important public decision-making events such as during the current COVID-19 pandemic. Here, we analyze the Pro- and Anti-Protest sides of the Twitter discussion surrounding the first few weeks of the anti-lockdown protests in the United States. We find that these opposing groups mirror the partisan divide regarding the protests in their use of specific phrases and in their sharing of external links. We then compare the users in each group and their actions and find that the Pro-Protest side acts more proactively, is more centrally organized, engages with the opposing side less, and appears to rely more on bot-like or troll-like users. In contrast, the Anti-Protest side is more reactive, has a larger presence of verified account activity (both as actors and targets), and appears to have been more successful in spreading its message in terms of both tweet volume and in attracting more regular type users. Our work provides insights into the organization of opposing sides of the Twitter debate and discussions over responses to the COVID-19 emergency and helps set the stage for further work in this area.

Assessment of Hospital Characteristics and Interhospital Transfer Patterns of Adults With Emergency General Surgery Conditions.

Importance: Although patients with emergency general surgery (EGS) conditions frequently undergo interhospital transfers, the transfer patterns and associated factors are not well understood. Objective: To examine whether patients with EGS conditions are consistently directed to hospitals with more resources and better outcomes. Design, Setting, and Participants: This cohort study performed a network analysis of interhospital transfers among adults with EGS conditions from January 1 to December 31, 2016. The analysis used all-payer claims data from the 2016 Healthcare Cost and Utilization Project state inpatient and emergency department databases in 8 states. A total of 728 hospitals involving 85 415 transfers of 80 307 patients were included. Patients were eligible for inclusion if they were 18 years or older and had an acute care hospital encounter with a diagnosis of an EGS condition as defined by the American Association for the Surgery of Trauma. Data were analyzed from January 1, 2020, to June 17, 2021. Exposures: Hospital-level measures of size (total bed capacity), resources (intensive care unit [ICU] bed capacity, teaching status, trauma center designation, and presence of trauma and/or surgical critical care fellowships), EGS volume (annual EGS encounters), and EGS outcomes (risk-adjusted failure to rescue and in-hospital mortality). Main Outcomes and Measures: The main outcome was hospital-level centrality ratio, defined as the normalized number of incoming transfers divided by the number of outgoing transfers. A higher centrality ratio indicated more incoming transfers per outgoing transfer. Multivariable regression analysis was used to test the hypothesis that a higher hospital centrality ratio would be associated with more resources, higher volume, and better outcomes. Results: Among 80 307 total patients, the median age was 63 years (interquartile range [IQR], 50-75 years); 52.1% of patients were male and 78.8% were White. The median number of outgoing and incoming transfers per hospital were 106 (IQR, 61-157) and 36 (IQR, 8-137), respectively. A higher log-transformed centrality ratio was associated with more resources, such as higher ICU capacity (eg, >25 beds vs 0-10 beds: ß = 1.67 [95% CI, 1.16-2.17]; P < .001), and higher EGS volume (eg, quartile 4 [highest] vs quartile 1 [lowest]: ß = 0.78 [95% CI, 0-1.57]; P = .01). However, a higher log-transformed centrality ratio was not associated with better outcomes, such as lower in-hospital mortality (eg, quartile 4 [highest] vs quartile 1 [lowest]: ß = 0.30 [95% CI, -0.09 to 0.68]; P = .83) and lower failure to rescue (eg, quartile 4 [highest] vs quartile 1 [lowest]: ß = -0.50 [95% CI, -1.13 to 0.12]; P = .27). Conclusions and Relevance: In this study, EGS transfers were directed to high-volume hospitals with more resources but were not necessarily directed to hospitals with better clinical outcomes. Optimizing transfer destination in the interhospital transfer network has the potential to improve EGS outcomes.

Disrupted Care Continuity: Testing Associations between Social Networks and Transition Success for Children with Autism.

Children with autism situated in lower income families often receive intensive educational interventions as their primary form of treatment, due to financial barriers for community interventions. However, the continuity of care can be disrupted by school transitions. The quality of social relationships during the transition to a new school among parents, school staff and community providers, called the team-around-the-child (TAC), can potentially buffer a child with autism from the adverse effects caused by care disruptions. Qualities of social relationships, including trust and collaborative problem solving, can be measured using social network analysis. This study investigates if two different types of TAC relationships, defined as (1) the level of trust among team members and (2) the degree of collaborative problem solving among team members, are associated with perceived successful transitions for children with autism from lower income families. Findings suggested that TAC trust is significantly associated with the outcome of transition success for children with autism immediately post-transition.

"The coronavirus is a bioweapon": classifying coronavirus stories on fact-checking sites.

The 2020 coronavirus pandemic has heightened the need to flag coronavirus-related misinformation, and fact-checking groups have taken to verifying misinformation on the Internet. We explore stories reported by fact-checking groups PolitiFact, Poynter and Snopes from January to June 2020. We characterise these stories into six clusters, then analyse temporal trends of story validity and the level of agreement across sites. The sites present the same stories 78% of the time, with the highest agreement between Poynter and PolitiFact. We further break down the story clusters into more granular story types by proposing a unique automated method, which can be used to classify diverse story sources in both fact-checked stories and tweets. Our results show story type classification performs best when trained on the same medium, with contextualised BERT vector representations outperforming a Bag-Of-Words classifier.

Active, aggressive, but to little avail: characterizing bot activity during the 2020 Singaporean elections.

Digital disinformation presents a challenging problem for democracies worldwide, especially in times of crisis like the COVID-19 pandemic. In countries like Singapore, legislative efforts to quell fake news constitute relatively new and understudied contexts for understanding local information operations. This paper presents a social cybersecurity analysis of the 2020 Singaporean elections, which took place at the height of the pandemic and after the recent passage of an anti-fake news law. Harnessing a dataset of 240,000 tweets about the elections, we found that 26.99% of participating accounts were likely to be bots, responsible for a larger proportion of bot tweets than the election in 2015. Textual analysis further showed that the detected bots used simpler and more abusive second-person language, as well as hashtags related to COVID-19 and voter activity-pointing to aggressive tactics potentially fuelling online hostility and questioning the legitimacy of the polls. Finally, bots were associated with larger, less dense, and less echo chamber-like communities, suggesting efforts to participate in larger, mainstream conversations. However, despite their distinct narrative and network maneuvers, bots generally did not hold significant influence throughout the social network. Hence, although intersecting concerns of political conflict during a global pandemic may promptly raise the possibility of online interference, we quantify both the efforts and limits of bot-fueled disinformation in the 2020 Singaporean elections. We conclude with several implications for digital disinformation in times of crisis, in the Asia-Pacific and beyond.

Best Practices for Modeling Egocentric Social Network Data and Health Outcomes.

OBJECTIVE/AIM: We describe best practices for modeling egocentric networks and health outcomes using a five-step guide. BACKGROUND: Social network analysis (SNA) is common in social science fields and has more recently been used to study health-related topics including obesity, violence, substance use, health organizational behavior, and healthcare utilization. SNA, alone or in conjunction with spatial analysis, can be used to uniquely evaluate the impact of the physical or built environment on health. The environment can shape the presence, quality, and function of social relationships with spatial and network processes interacting to affect health outcomes. While there are some common measures frequently used in modeling the impact of social networks on health outcomes, there is no standard approach to social network modeling in health research, which impacts rigor and reproducibility. METHODS: We provide an overview of social network concepts and terminology focused on egocentric network data. Egocentric, or personal networks, take the perspective of an individual who identifies their own connections (alters) and also the relationships between alters. RESULTS: We describe best practices for modeling egocentric networks and health outcomes according to the following five-step guide: (1) model selection, (2) social network exposure variable and selection considerations, (3) covariate selection related to sociodemographic and health characteristics, (4) covariate selection related to social network characteristics, and (5) analytic considerations. We also present an example of SNA. CONCLUSIONS: SNA provides a powerful repertoire of techniques to examine how relationships impact attitudes, experiences, and behaviors-and subsequently health.