Leveraging artificial intelligence to identify the psychological factors associated with conspiracy theory beliefs online.

Given the profound societal impact of conspiracy theories, probing the psychological factors associated with their spread is paramount. Most research lacks large-scale behavioral outcomes, leaving factors related to actual online support for conspiracy theories uncertain. We bridge this gap by combining the psychological self-reports of 2506 Twitter (currently X) users with machine-learning classification of whether the textual data from their 7.7 million social media engagements throughout the pandemic supported six common COVID-19 conspiracy theories. We assess demographic factors, political alignment, factors derived from theory of reasoned action, and individual psychological differences. Here, we show that being older, self-identifying as very left or right on the political spectrum, and believing in false information constitute the most consistent risk factors; denialist tendencies, confidence in one's ability to spot misinformation, and political conservativism are positively associated with support for one conspiracy theory. Combining artificial intelligence analyses of big behavioral data with self-report surveys can effectively identify and validate risk factors for phenomena evident in large-scale online behaviors.

Psychological interventions countering misinformation in social media: A scoping review.

Introduction: The rise of social media users and the explosive growth in misinformation shared across social media platforms have become a serious threat to democratic discourse and public health. The mentioned implications have increased the demand for misinformation detection and intervention. To contribute to this challenge, we are presenting a systematic scoping review of psychological interventions countering misinformation in social media. The review was conducted to (i) identify and map evidence on psychological interventions countering misinformation, (ii) compare the viability of the interventions on social media, and (iii) provide guidelines for the development of effective interventions. Methods: A systematic search in three bibliographic databases (PubMed, Embase, and Scopus) and additional searches in Google Scholar and reference lists were conducted. Results: 3,561 records were identified, 75 of which met the eligibility criteria for the inclusion in the final review. The psychological interventions identified during the review can be classified into three categories distinguished by Kozyreva et al.: Boosting, Technocognition, and Nudging, and then into 15 types within these. Most of the studied interventions were not implemented and tested in a real social media environment but under strictly controlled settings or online crowdsourcing platforms. The presented feasibility assessment of implementation insights expressed qualitatively and with numerical scoring could guide the development of future interventions that can be successfully implemented on social media platforms. Discussion: The review provides the basis for further research on psychological interventions counteracting misinformation. Future research on interventions should aim to combine effective Technocognition and Nudging in the user experience of online services. Systematic review registration: [https://figshare.com/], identifier [https://doi.org/10.6084/m9.figshare.14649432.v2].