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

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s41060-021-00298-6.

Will COVID-19-related economic worries superimpose health worries, reducing nonpharmaceutical intervention acceptance in Germany? A prospective pre-registered study.

Nonpharmaceutical interventions (NPI) such as stay-at-home orders aim at curbing the spread of the novel coronavirus, SARS-COV-2. In March 2020, a large proportion of the German population supported such interventions. In this article, we analyse whether the support for NPI dwindle with economic worries superimposing virus-related worries in the months to follow. We test seven pre-registered1 hypotheses using data from the German COSMO survey (Betsch, Wieler, Habersaat, et al. 2020), which regularly monitors behavioural and psychological factors related to the pandemic. The present article covers the period from March 24, 2020 to July 7, 2020 (Ntotal  = 13,094), and, in addition, includes a validation study providing evidence for the reliability and validity of the corresponding COSMO measures (N = 612). Results revealed that virus-related worries decreased over time, whereas economic worries remained largely constant. Moreover, the acceptance of NPIs considerably decreased over time. Virus-related worries were positively associated with acceptance of NPIs, whereas this relationship was negative regarding economic worries (albeit smaller and less consistent). Unexpectedly, no interactions between virus-related worries and economic worries were found. We conclude that individual differences in virus-related and economic threat perceptions related to COVID-19 play an important role in the acceptance of NPIs.

Mining Twitter to detect hotspots in psychology

For identifying psychological hotspot topics, a mere focus on bibliometric data suffers from a publication delay. To overcome this issue, we introduce Twitter mining of ongoing online communication among scientists for the detection of psychological research topics. Specifically, we collected the entire 69,963 tweets posted between August 2007 and July 2020 from 139 accounts of psychology professors, departments, and research institutes from the German-speaking countries, as well as sections of the German Psychological Society (DGPs). To examine whether Twitter topics are hotspots in terms of indicating future publication trends, 346,361 references in the PSYNDEX database were extracted. For determining the additional value of our approach in contrast to traditional conference analysis, we gathered all available conference programs of the DGPs and its sections since 2010 and compared dates of topic emergence. Results revealed 21 topics addressing societal issues (e.g., COVID-19), methodology (e.g., machine learning), scientific research (e.g., replication crisis), and different areas of psychological research. Ten topics indicated an increasing publication trend, particularly topics related to methodology or scientific transparency. Seven Twitter topics emerged earlier on Twitter than at conferences. A total of four topics could be expected neither by bibliometric forecasting nor conference contents: "methodological issues in meta-analyses", "playfulness", "preregistration", and "mobile brain/body imaging". Taken together, Twitter mining is a worthwhile endeavor for identifying psychological hotspot topics, especially regarding societal issues, novel research methods, and research transparency in psychology. In order to get the most comprehensive picture of research hotspots, Twitter mining is recommended in addition to bibliometric analyses of publication trends and monitoring of conference topics. (PsycInfo Database Record (c) 2021 APA, all rights reserved)