Recent crises like the COVID-19 pandemic provoked an increasing appearance of misleading information, emphasizing 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 2,382 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.