## 2 Background
### Deceptive Online Content Identification  
This paper focuses on understanding Americans' perspectives regarding content trustworthiness, which contributes to the development and understanding of techniques to combat deceptive online content. Most techniques designed to combat the spread of deceptive online content require supporting techniques to identify that content. These techniques are the focus of this section.  
To identify deceptive online content, a defining set of criteria is required. [PERSON] et al. [40] suggested that four elements be considered when determining whether content is \"fake news\" or not. The suggested elements were the content's creator/spreader, target victims, social context, and the nature of the content itself. However, without identifying specific facts which are inaccurate and a proving a creator's nefarious intent, the process is still inherently subjective. Most techniques focus on identification using correlating factors, as opposed to direct methods.  
Content identification techniques can be manual, automatic, or a combination of the two. [PERSON] and [PERSON] [41] proposed an approach which identifies content based on its style and the distributing party, while also using network analysis. [PERSON], [PERSON] and [PERSON] [42] also proposed a network analysis technique based on the identification of influential nodes. Others [43; 44] have proposed techniques based on user analysis. [PERSON] [45], alternately, discussed using a machine learning-based automation approach which utilizes manual annotations. [PERSON] et al. [46] developed an algorithm focusing on identifying \"rumors\" which \"many people believe to be true\" despite them drawing on misinformation.  
Techniques based on technologies such as neural networks [47; 48], natural language processing [49] and neural stacking [50] have also been proposed. Other techniques have focused on social science dimensions, such as those considering emotion cognizance [51], social context analysis [40; 52], content and sentiment analysis [40] and signal detection [53].  
Several approaches have already been implemented in practice. Twitter's has introduced a \"Birdwatch\" system which involves manual user review of Twitter posts [54]. Wikipedia has a manually developed news source list which includes reliability assess ment (See [[https://en.wikipedia.org/wiki/Wikipedia:Reliable_sources/Perennial_sources](https://en.wikipedia.org/wiki/Wikipedia:Reliable_sources/Perennial_sources)]([https://en.wikipedia.org/wiki/Wikipedia:Reliable_sources/Perennial_sources](https://en.wikipedia.org/wiki/Wikipedia:Reliable_sources/Perennial_sources)), accessed on 3 November 2022).  
Beyond identifying deceptive content, several taxonomies for more precisely classifying it have been developed. [PERSON], [PERSON] and [PERSON] [55], for example, proposed a system which places content into six categories: \"fabrication\", \"manipulation\", \"propaganda\", \"satire\", \"parody\", and \"advertising\". [PERSON] and [PERSON] [56] proposed a seven category system with the classifications of \"false connection\", \"false context\", \"manipulated content\", \"misleading content\", \"imposter content (genuine sources are impersonated)\", \"fabricated content\", \"satire/parody\". Notably, these taxonomies may support the identification of deceptive content, in addition to classifying already identified content.