Contextual Statement
For my BCM206 Digital Artefact, I have chosen the Researcher mode to investigate the question: “What features of language in recommendation-request posts on r/HistoricalRomance are associated with higher levels of community response?” The project developed from my own use of the subreddit. I noticed that when scouring the subreddit, some users could ask relatively simple questions and receive only a few recommendations, while other requests attracted dozens or more than one hundred responses. My initial thoughts were that highly responded-to posts appeared more humorous, emotional or conversational, but in other cases I could not identify an obvious reason for the difference. This created the starting assumption I want to test rather than simply confirm.
I have begun with a small pilot sample of ten recommendation-request posts published within the last month. I selected five posts with relatively high response counts and five with relatively low response counts, using the number of comments as my initial measure of community response. For each post, I recorded the title, post text, response count and initial observations about the language used. The pilot has already complicated my original assumption. The post with the highest response count used humorous, exaggerated and emotive language, but another post received 94 responses despite being relatively short and unemotional. Conversely, some highly detailed and specific requests received only three or seven responses. This suggests that humour or emotional language may be relevant, but cannot explain response levels on their own.
My next stage will involve developing a coding framework to compare language features across the sample. I will examine title framing, emotional and descriptive language, humour, specificity, personal context, examples of previously read books and direct community language such as greetings or thanks. I will also record contextual factors that emerged from the pilot, including the novelty of the request and how easy it appears for another user to answer. This distinction is important because the project needs to avoid assuming that language alone causes higher response levels. Depending on what this coding reveals, I will expand or revise the sample and use academic research to guide the next stage of the inquiry.
The project will use the Researcher mode’s action, reflection and adaptation process. Each research action will be recorded in a research log, followed by reflection on what the evidence supports or challenges, and an adaptation to the next search, coding decision or version of the research question. Research by Morris, Teevan and Panovich (2010) provides an initial point of comparison because it examines why people ask and answer questions through online social networks and treats social platforms as information-seeking environments as well as spaces for conversation.
I will also use the subject concept of the network society to contextualise r/HistoricalRomance as a niche digital network in which users exchange specialised knowledge about books and genres. The utility of the project is therefore broader than identifying a “best” way to ask for a recommendation. It aims to understand how language, request design and community context may shape participation and the circulation of knowledge within a specialised online community.
References
Morris, MR, Teevan, J & Panovich, K 2010, ‘What do people ask their social networks, and why?’, Proceedings of the 28th international conference on Human factors in computing systems – CHI ’10, pp. 1739–1748.
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