The role of perceived minority-group status in the conspiracy beliefs of factual majority groups

Research suggests that minority-group members sometimes are more susceptible to misinformation. Two complementary studies examined the influence of perceived minority status on susceptibility to misinformation and conspiracy beliefs. In study 1 (n = 2140), the perception of belonging to a minority group, rather than factually belonging to it, was most consistently related with an increased susceptibility to COVID-19 misinformation across national samples from the USA, the UK, Germany and Poland. Specifically, perceiving that one belongs to a gender minority group particularly predicted susceptibility to misinformation when participants factually did not belong to it. In pre-registered study 2 (n = 1823), an experiment aiming to manipulate the minority perceptions of men failed to influence conspiracy beliefs in the predicted direction. However, pre-registered correlational analyses showed that men who view themselves as a gender minority were more prone to gender conspiracy beliefs and exhibited a heightened conspiracy mentality. This effect was correlationally mediated by increased feelings of system identity threat, collective narcissism, group relative deprivation and actively open-minded thinking. Especially, the perception of being a minority in terms of power and influence (as compared to numerically) was linked to these outcomes. We discuss limitations and practical implications for countering misinformation.

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].

Improving medical experts' efficiency of misinformation detection: an exploratory study

Fighting medical disinformation in the era of the pandemic is an increasingly important problem. Today, automatic systems for assessing the credibility of medical information do not offer sufficient precision, so human supervision and the involvement of medical expert annotators are required. Our work aims to optimize the utilization of medical experts' time. We also equip them with tools for semi-automatic initial verification of the credibility of the annotated content. We introduce a general framework for filtering medical statements that do not require manual evaluation by medical experts, thus focusing annotation efforts on non-credible medical statements. Our framework is based on the construction of filtering classifiers adapted to narrow thematic categories. This allows medical experts to fact-check and identify over two times more non-credible medical statements in a given time interval without applying any changes to the annotation flow. We verify our results across a broad spectrum of medical topic areas. We perform quantitative, as well as exploratory analysis on our output data. We also point out how those filtering classifiers can be modified to provide experts with different types of feedback without any loss of performance.

Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning

Background

The spread of false medical information on the web is rapidly accelerating. Establishing the credibility of web-based medical information has become a pressing necessity. Machine learning offers a solution that, when properly deployed, can be an effective tool in fighting medical misinformation on the web.

Objective

The aim of this study is to present a comprehensive framework for designing and curating machine learning training data sets for web-based medical information credibility assessment. We show how to construct the annotation process. Our main objective is to support researchers from the medical and computer science communities. We offer guidelines on the preparation of data sets for machine learning models that can fight medical misinformation.

Methods

We begin by providing the annotation protocol for medical experts involved in medical sentence credibility evaluation. The protocol is based on a qualitative study of our experimental data. To address the problem of insufficient initial labels, we propose a preprocessing pipeline for the batch of sentences to be assessed. It consists of representation learning, clustering, and reranking. We call this process active annotation.

Results

We collected more than 10,000 annotations of statements related to selected medical subjects (psychiatry, cholesterol, autism, antibiotics, vaccines, steroids, birth methods, and food allergy testing) for less than US $7000 by employing 9 highly qualified annotators (certified medical professionals), and we release this data set to the general public. We developed an active annotation framework for more efficient annotation of noncredible medical statements. The application of qualitative analysis resulted in a better annotation protocol for our future efforts in data set creation.

Conclusions

The results of the qualitative analysis support our claims of the efficacy of the presented method.

Benford's Distribution in Complex Networks

Many collections of numbers do not have a uniform distribution of the leading digit, but conform to a very particular pattern known as Benford’s distribution. This distribution has been found in numerous areas such as accounting data, voting registers, census data, and even in natural phenomena. Recently it has been reported that Benford’s law applies to online social networks. Here we introduce a set of rigorous tests for adherence to Benford’s law and apply it to verification of this claim, extending the scope of the experiment to various complex networks and to artificial networks created by several popular generative models. Our findings are that neither for real nor for artificial networks there is sufficient evidence for common conformity of network structural properties with Benford’s distribution. We find very weak evidence suggesting that three measures, degree centrality, betweenness centrality and local clustering coefficient, could adhere to Benford’s law for scalefree networks but only for very narrow range of their parameters.