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The purpose of this paper is to demonstrate the utility of systemic functional linguistics (SFL) as an underlying model to examine the similarities/differences between spoken and written peer-to-peer (P2P) communication.

An embedded mixed methods experimental design with linguistically standardized experimental stimuli was used to expose the basic linguistic differences between P2P communications that can be attributed to communication medium (spoken/written) and product type (hedonic/utilitarian).

The findings show, empirically, that consumer’s spoken language is not linguistically equivalent to that of written language. This confirms that the capability of language to convey semantic meaning in spoken communication differs from written communication. This study extends the characteristics that differentiate hedonic from utilitarian products to include lexical density (i.e. hedonic) vs lexical sparsity (i.e. utilitarian).

The findings of this study are not wholly relevant to other forms of consumer communication (e.g. viral marketing). This research used a few SFL resources.

This research shows that marketers should ideally apply a semantic approach to the analysis of communications, given that communication meaning can vary across channels. Marketers may also want to focus on specific feedback channels (e.g. review site vs telephone) depending on the depth of product’s details that need to be captured. This study also offers metrics that advertisers could use to classify media and to characterize consumer segments.

This research shows the relevance of SFL for understanding P2P communications and has potential applications to other marketing communications.

The purpose of this paper is to examine how upstream social marketing can benefit from using social media commentary to identify cognitive biases. Using reactions to leading media/news publications/articles related to climate and energy policy in Australia, this paper aims to understand underlying community cognitive biases and their reasonings.

Social listening was used to gather community commentary about climate and energy policy in Australia. This allowed the coding of natural language data to determine underlying cognitive biases inherent in the community. In all, 2,700 Facebook comments were collected from 27 news articles dated between January 2018 and March 2020 using exportcomments.com. Team coding was used to ensure consistency in interpretation.

Nine key cognitive bias were noted, including, pessimism, just-world, confirmation, optimum, curse of knowledge, Dunning–Kruger, self-serving, concision and converge biases. Additionally, the authors report on the interactive nature of these biases. Right-leaning audiences are perceived to be willfully uninformed and motivated by self-interest; centric audiences want solutions based on common-sense for the common good; and left-leaning supporters of progressive climate change policy are typically pessimistic about the future of climate and energy policy in Australia. Impacts of powerful media organization shaping biases are also explored.

Through a greater understanding of the types of cognitive biases, policy-makers are able to better design and execute influential upstream social marketing campaigns.

The study demonstrates that observing cognitive biases through social listening can assist upstream social marketing understand community biases and underlying reasonings towards climate and energy policy.