## 1 Introduction  
Deceptive online content presents numerous challenges to modern society. It has been blamed for numerous incidents ranging from election interference [1] to a standoff in a pizza parlor [2]. Manipulative online content, in particular, has been blamed for significantly contributing to the U.S. 6 January capital riot [3]. The term \"fake news\" has become commonplace, in some instances referring to deceptive content and in others used to attack content that the speaker disagrees with [4, 5]. While several pronounced examples of the problem have occurred in the United States, the phenomenon is far bigger than just in America. Interference with the British Brexit vote [6, 7], for example, has been alleged. Brazilian president [PERSON] [8] explicitly used the term \"fake news\" in reference to a Reuters report regarding communications between the CIA and Brazilian officials. Disinformation has also been argued to have had a key role in the Russia-Ukraine conflict [9]. Examples of the \"fake news\" and online deceptive and manipulative content phenomena have been documented in at least 20 countries [10]. [PERSON] [11], most pronouncedly, contends that \"fake news\" represents a threat to democracy itself. Numerous responses to deceptive and allegedly deceptive online content have occurred. Cogent Communications disconnected internet backbone service into Russia to prevent \"the Russian government from using the company's networks for cyberattacks and propaganda\" [12]. Russia, conversely, passed a law against so-called \"fake news\", blocked Facebook, limited Twitter access and threatened to \"block\" Wikipedia due to \"false messages' about the [Ukraine] war\" [12]. The Russian \"fake news\" law threatens violators with up to fifteen years of jail time and fines of over a million rubles [13] and has caused some organizations to cease Russian operations, while others have changed to align with the Russian government's dictates [14]. Worldwide, prospective solutions for preventing the spread of misinformation, disinformation and other deceptive content have included techniques such as filtering content [15], content detection and removal [16], limiting access to the internet [17], and content labeling [18]. Problematically, many of these techniques could also be used for preventing the spread of accurate information that is disliked by or critical of various governments. In prior work, the efficacy of online labeling has been assessed [18] and user perceptions of labels have been analyzed [19; 20]. In particular, labels' efficacy have been evaluated based on Americans' income level, party affiliation and online use levels [21] and factors impacting Americans' perception of the trustworthiness of online content have been analyzed based on age, education level and gender [22]. This paper seeks to further the understanding of how labeling can be used to mitigate the spread of fake news--as defined by [PERSON] et al. [23] as \"information, presented as a news story that is factually incorrect and designed to deceive the consumer into believing it is true\". It builds on the aforementioned prior work by focusing on the impact of income, political affiliation and online usage time on the factors that impact Americans' perceptions of online content trustworthiness. A broad consensus, across the studied demographics, that each labeling metric considered should be used at least 'a moderate amount' is shown, suggesting the feasibility of an online content labeling system using these metrics. However, less consistency is shown when considering the higher standards of metric impact and use at the 'a lot' and 'a great deal' levels, particularly between respondents indicating different political affiliations. The level of use, for many labeling metrics, was shown to tend to increase with both average daily internet usage time and with reported income level, though exceptions to this trend were present at the highest and lowest income levels.