Misleading information in crises: exploring content-specific indicators for misleading information on Twitter from a user perspective

Abstract

Recent crises like the COVID-19 pandemic provoked an increasing appearance of misleading information, emphasising the need for effective user-centered countermeasures as an important field in HCI research. This work investigates how content-specific user-centered indicators can contribute to an informed approach to misleading information. In a threefold study, we conducted an in-depth content analysis of 2382 German tweets on Twitter (now X) to identify topical (e.g. 5G), formal (e.g. links), and rhetorical (e.g. sarcasm) characteristics through manual coding, followed by a qualitative online survey to evaluate which indicators users already use autonomously to assess a tweet's credibility. Subsequently, in a think-aloud study participants qualitatively evaluated the identified indicators in terms of perceived comprehensibility and usefulness. While a number of indicators were found to be particularly comprehensible and useful (e.g. claim for absolute truth and rhetorical questions), our findings reveal limitations of indicator-based interventions, particularly for people with entrenched conspiracy theory views. We derive four implications for digitally supporting users in dealing with misleading information, especially during crises.