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The spread of misinformation, rumors, or conspiracies on social media is not new, and is particularly intense in periods of crisis everywhere regardless of the country being a stable democracy or not. Social media platforms have tried different options to constrain the spread of fake news, however these methods approach the problem from the detection angle rather than prevention. In parallel, much of the existing literature has focused on the detection of fake news after it has been shared and few studies have explored how to create an information system to address the issue prior to its emergence. In this research, we rely on a mock social media platform similar to X where participants spend a certain amount of time and answered a questionnaire. We intended to understand how people react to the information in their feed, faced with messages that vary in content, tone, and veracity. We expose them to different treatment conditions: providing information about which messages stem from verified profiles; labelling certain messages as verified or suspicious; giving a general warning about the presence of non-verified profiles/messages. We assess how these conditions influence participants’ evaluations of (mis)information, their social media behaviour (liking or sharing messages), and their political attitudes.