Conspiracy theories on Twitter: emerging motifs and temporal dynamics during the COVID-19 pandemic

Abstract

The COVID-19 pandemic resulted in an upsurge in the spread of diverse conspiracy theories (CTs) with real-life impact. However, the dynamics of user engagement remain under-researched. In the present study, we leverage Twitter data across 11 months in 2020 from the timelines of 109 CT posters and a comparison group (non-CT group) of equal size. Within this approach, we used word embeddings to distinguish non-CT content from CT-related content as well as analysed which element of CT content emerged in the pandemic. Subsequently, we applied time series analyses on the aggregate and individual level to investigate whether there is a difference between CT posters and non-CT posters in non-CT tweets as well as the temporal dynamics of CT tweets. In this regard, we provide a description of the aggregate and individual series, conducted a STL decomposition in trends, seasons, and errors, as well as an autocorrelation analysis, and applied generalised additive mixed models to analyse nonlinear trends and their differences across users. The narrative motifs, characterised by word embeddings, address pandemic-specific motifs alongside broader motifs and can be related to several psychological needs (epistemic, existential, or social). Overall, the comparison of the CT group and non-CT group showed a substantially higher level of overall COVID-19-related tweets in the non-CT group and higher level of random fluctuations. Focussing on conspiracy tweets, we found a slight positive trend but, more importantly, an increase in users in 2020. Moreover, the aggregate series of CT content revealed two breaks in 2020 and a significant albeit weak positive trend since June. On the individual level, the series showed strong differences in temporal dynamics and a high degree of randomness and day-specific sensitivity. The results stress the importance of Twitter as a means of communication during the pandemic and illustrate that these beliefs travel very fast and are quickly endorsed.

COVID-19 Modeling: A Review

“…Examples are COVID-19 outbreak, case forecasting, medical diagnostics, contact tracing, risk, transmission, uncertainty, anomalies, complexities, classification, variation, prediction, and drug development [44]. Below, we briefly illustrate their applications in several popular COVID- 19 Machine learning for COVID-19 outbreak prediction and risk assessment. Typical classifiers like ANN, SVM, decision trees, random forest, regression trees, least absolute shrinkage and selection operator (LASSO), and self-organizing maps have been applied to forecast COVID-19 spread and outbreak and their coverage, patterns, growth, and trends; estimate and forecast the confirmed, recovered and death case numbers, and the transmission and mortality rates; and cluster infected cases and groups, etc.…”

“…-Characterizing the symptoms of coronavirus infections and COVID-19 , e.g., by pretrained neural networks, e.g., [236]; with more discussion in Section 7. Transformer-based NLP neural models, and their derivatives [78]; -Characterizing the COVID-19 infodemic, e.g., by NLP and text mining including misinformation identification [93,243,178,19], enhancing epidemic modeling using social media data [137], and analyzing the COVID-19 research progress and topic evolution [316]; -Other tasks such as analyzing the influence and effect of countermeasures, e.g., the effect of quarantine policies on outbreak using DNNs; with more discussion in Section 8.…”

“…Social media data, news feeds, Q/A, cross references, fact-check, etc. [19,62,4,93,150,178,243] on sociopolitical systems Descriptive analytics, sociopolitical methods, survey analysis, etc. Social and political data, case data, surveys, questionnaires, sociopolitical events, etc.…”

“…In contrast to the existing reviews and surveys such as [145,56], this review aims to be more comprehensive than the other references by covering cross-disciplinary problems and techniques for modeling COVID-19; analytics and learning-focused where we show statistical machine learning, model-based machine learning, deep learning, and other AI and data science techniques have been widely applied in modeling COVID-19; specific to modeling COVID-19, therefore, the domain-specific research on its epidemiology, medicine, vaccine, biology and pathology etc. is excluded; in addition, classic typical methods applied in these areas but not (widely) used in COVID-19 are also excluded; unique in critical analysis and summarizing the challenges of the COVID- 19 disease, data and modeling by model-, domain-and data-driven approaches; structural and critical by categorizing, comparing, criticizing and generalizing typical modeling methods in different disciplines and areas for modeling COVID-19; and…”

Counteracting conspiracy ideas as a measure of increasing propensity for COVID-19 vaccine uptake in Russian society

“…Some scientists, scholars, and noted political figures, including Sucharit Bhakdi [ 11 , 12 ], Marc Siegel [ 13 ], Slavoj Žižek [ 14 ], and Klaus Martin Schwab [ 15 ], believe that the growth of COVID-19-related conspiracy theories against COVID-19 vaccination may be accounted for by allegedly inconsistent public policy. However, most researchers note that the enormous surge of conspiracy ideas may be well explained by unprecedented levels of globalisation [ 16 , 17 ], digitisation [ 17 ], cultural unification [ 18 ], diminished levels of information quality/controllability in pandemic times [ 19 ], “bloggification” (the emergence of an almost endless amount of sources of information and centres of ideological influence on the Internet: personal blogs, independent media, independent health care experts, etc.) [ 20 ], and distrust towards the public policy [ 21 ].…”