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Register a new userCovid-19 infodemic reveals new tipping point epidemiology and a revised $R$ formula
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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 timescales to the viral spreading. This allows material that might have died out, to evolve and even mutate. This has enabled niche networks that were already successfully spreading hate and anti-vaccination material, to rapidly become global super-spreaders of narratives featuring fake COVID-19 treatments, anti-Asian sentiment and conspiracy theories. We derive new tools that incorporate these coupled social-viral dynamics, including an online $R$, to help prevent infodemic spreading at all scales: from spreading across platforms (e.g. Facebook, 4Chan) to spreading within a given subpopulation, or community, or topic. By accounting for similar social and viral timescales, the same mathematical theory also offers a quantitative description of other unconventional infection profiles such as rumors spreading in financial markets and colds spreading in schools.
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How information consumption affects behaviour is an open and widely debated research question. A popular hypothesis states that the so-called infodemic has a substantial impact on orienting individual decisions. A competing hypothesis stresses that exposure to vast amounts of even contradictory information has little effect on personal choices. The COVID-19 pandemic offered an opportunity to investigate this relationship, analysing the interplay between COVID-19 related information circulation and the propensity of users to get vaccinated. We analyse the vaccine infodemics on Twitter and Facebook by looking at 146M contents produced by 20M accounts between 1 January 2020 and 30 April 2021. We find that vaccine-related news triggered huge interest through social media, affecting attention patterns and the modality in which information was spreading. However, we observe that such a tumultuous information landscape translated only in minimal variations in overall vaccine acceptance as measured by Facebooks daily COVID-19 Trends and Impact Survey (previously known as COVID-19 World Symptoms Survey) on a sample of 1.6M users. Notably, the observation period includes the European Medicines Agency (EMA) investigations over blood clots cases potentially related to vaccinations, a series of events that could have eroded trust in vaccination campaigns. We conclude the paper by investigating the numerical correlation between various infodemics indices and vaccine acceptance, observing strong compatibility with a null model. This finding supports the hypothesis that altered information consumption patterns are not a reliable predictor of collective behavioural change. Instead, wider attention on social media seems to resolve in polarisation, with the vaccine-prone and the vaccine-hesitant maintaining their positions.
A generalisation of the Susceptible-Infectious model is made to include a time-dependent transmission rate, which leads to a close analytical expression in terms of a logistic function. The solution can be applied to any continuous function chosen to describe the evolution of the transmission rate with time. Taking inspiration from real data of the Covid-19, for the case of cumulative confirmed positives and deaths, we propose an exponentially decaying transmission rate with two free parameters, one for its initial amplitude and another one for its decaying rate. The resultant time-dependent SI model, which under extra conditions recovers the standard Gompertz functional form, is then compared with data from selected countries and its parameters fit using Bayesian inference. We make predictions about the asymptotic number of confirmed positives and deaths, and discuss the possible evolution of the disease in each country in terms of our parametrisation of the transmission rate.
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 behaved during the infodemic is unclear. In this paper, we examined the roles of bots in the case of the COVID-19 infodemic and the diffusion of non-credible information such as 5G and Bill Gates conspiracy theories and content related to Trump and WHO by analyzing retweet networks and retweeted items. We show the segregated topology of their retweet networks, which indicates that right-wing self-media accounts and conspiracy theorists may lead to this opinion cleavage, while malicious bots might favor amplification of the diffusion of non-credible information. Although the basic influence of information diffusion could be larger in human users than bots, the effects of bots are non-negligible under an infodemic situation.
We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction numbers $R_0$ for each social media platform. Moreover, we characterize information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors amplification.
The massive spread of false information on social media has become a global risk especially in a global pandemic situation like COVID-19. False information detection has thus become a surging research topic in recent months. NLP4IF-2021 shared task on fighting the COVID-19 infodemic has been organised to strengthen the research in false information detection where the participants are asked to predict seven different binary labels regarding false information in a tweet. The shared task has been organised in three languages; Arabic, Bulgarian and English. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves a 0.707 mean F1 score in Arabic, 0.578 mean F1 score in Bulgarian and 0.864 mean F1 score in English ranking 4th place in all the languages.
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