Ideological Biases and Motivations in Identifying and Moderating Misinformation

Social media companies are increasingly relying on users instead of professional fact-checkers for content moderation, yet many question individuals’ ability to identify misinformation in an accurate and unbiased manner. In this study, we identify the conditions under which social media users correctly identify false versus true information, and their willingness to participate in the moderation of such content on social media. We focus on ideological alignment as a main predictor of judgements of content veracity, and hypothesise that individuals will be more susceptible to false pro-attitudinal claims and more skeptical of true counter-attitudinal claims. We additionally study the effect of motivations to moderate content (such as reporting it to a social media platform), and hypothesise that individuals will be more likely to moderate counter-attitudinal claims, and less likely to moderate pro-attitudinal ones even in cases where they identify these claims as false. Finally, we study willingness to amplify the message in the claim, and hypothesise that individuals will be more likely to share claims that are pro-attitudinal and those they believe to be accurate. For claims rated as inaccurate, we also hypothesise that individuals will be more willing to share pro- versus counter-attitudinal ones. We use survey data from a representative sample of 6,000 individuals across four countries with varying political and media systems: Spain, the Netherlands, Romania, and Lithuania. Respondents are asked to rate the accuracy of eight claims, balanced by veracity (true/false), ideology (conservative/liberal), and on two globally salient topics (immigration and climate change). They are then asked to indicate whether they would take different types of actions if they encountered each of the claims on social media, including moderation (reporting a post) and amplification (sharing it). Through this study, we aim to shed light on the benefits - and potential harms - of crowdsourcing content moderation to limit the spread of misinformation on both sides of the ideological spectrum.