**1. Introduction**

In 2004, Keyes posited that the modern era was one of "post-truth" [1]. He noted, referring to interpersonal activities, that "deception has become commonplace at all levels of contemporary life" and highlighted the numerous lies that are told frequently in society [1].

Twelve years later, in 2016, the British referendum on European Union membership (see [2]) and the U.S. presidential election brought the concept of deceptive online content into the public consciousness. In the UK, Brexit was fueled by an army of Twitterbots [3], illegal profiling using online data [4], foreign online content influence [4] and "hyperpartisan" content [3]. In the U.S., so-called "fake news" stories circulated on Facebook and other social media [5]. Grinberg, et al. say that approximately 6% of all news, during this period, was fake (which they identified based on the journalistic practices, or lack thereof, of the distributing site)—however, less than 1% of the population received 80% of the fake news content [6]. On Twitter, Bovet and Makse [7] found that 25% of tweets, during this period, were "fake or extremely biased news", based on linking to websites that they identified as "fake and extremely biased".

Over the intervening five years, the term "fake news" grew in usage [8] and changed in meaning [9]. Initially, the term was used for "describing the threat of disinformation online"; however, this shifted "to a more normalized and broad usage of the term in relation to attacks on legacy news media" [9]. Despite the change in meaning being temporally connected to the 2016 U.S. presidential election, Cunha, et al. [8] have shown that this change was prevalent in at least 20 countries. In some cases, modern uses of the term have little to do with a story's accuracy and instead seek to "discount and discredit ideologically uncongenial media sources" [10]. Tong, et al. [11] showed that a "weaponization of fake news" had occurred.

**Citation:** Straub, J.; Spradling, M.; Fedor, B. Assessment of Factors Impacting the Perception of Online Content Trustworthiness by Age, Education and Gender. *Societies* **2022**, *12*, 61. https://doi.org/10.3390/ soc12020061

Academic Editors: Eugène Loos and Loredana Ivan

Received: 1 February 2022 Accepted: 24 March 2022 Published: 31 March 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

<sup>1</sup> Department of Computer Science, North Dakota State University, Fargo, ND 58105, USA; robert.fedor@ndsu.edu

Lee [12] argues that "fake news" is a "sinister force" that presents a threat to democracy itself. Given the concern that deceptive content has raised, a variety of techniques for mitigating and responding to it have been proposed. These have ranged from filtering content [13], to content detection and removal [14], to limiting access to the internet [15], to content labeling [16]. Labeling is perhaps the most democratic of these proposals, as it leaves the decision to read or not to the information consumer. It also benefits from not limiting speech in a way that may run afoul of the United States' First Amendment, which (in addition to its free speech benefits) may make its implementation more feasible. Other approaches, though, may be more effective at preventing the problems caused by fake news, albeit at the considerable expense of impairing speech freedom.

To assess the prospective impact of different forms of solutions and what solutions may be effective, understanding how individuals make content trustworthiness decisions is critical. This article focuses on intentionally deceptive online news content presented in textual form (potentially with supporting media, such as pictures), in particular. This is content that purports to be a news article via using the presentation typically used for news articles, but which has goals other than the accurate presentation of the information, as it is understood by the author (mirroring the definition presented in [17]). This work seeks to determine which characteristics individuals rely upon in assessing news-style article trustworthiness and whether the weight given to these characteristics varies by the age, educational level or gender of the individual. The characteristics studied in this paper were first proposed by Fuhr, et al. [18]. This content is of particular interest due to its prominence and ability to rapidly spread via social media and other channels. The data analyzed herein will inform analysis regarding whether content labeling can be effective (or not) or if alternate solutions are better to pursue.

This paper continues with a review, in Section 2, of prior work that this work builds on. Section 3 describes the study that was carried out. Next, in Section 4, the impact of an article's title, article, publisher and other related details on trustworthiness is assessed. Following this, Section 5 assesses the trustworthiness impact of other article characteristics, such as the number of opinion statements present and reading level. In Section 6, the implications of the analysis presented in Sections 4 and 5 are discussed. Finally, Section 7 discusses key conclusions and needed future work.
