Why I chose my topic
I developed my digital artefact around a subreddit I had used for more than two years: r/HistoricalRomance. I wanted to figure out why some book recommendation requests attracted long discussions and multiple comments while others received little to no responses. My research question became: “What language features in recommendation-request posts on r/HistoricalRomance are associated with higher levels of community response?”
At first, I thought this was mainly a question about effective or ‘more engaging’ wording. Looking back, I was definitely treating Reddit engagement as a problem to solve rather than an opportunity to understand how a networked community works. That difference became important when I received feedback that my project needed to connect more directly to BCM206 concepts. I could describe what I was seeing while using Reddit, but I wasn’t really explaining why it mattered in the context of future networks.
Rethinking my approach after feedback
Rather than just adding academic references to my analysis, I had to rethink how the concepts could change how I looked at the problem. Goldhaber’s (1997) attention economy gave me a reason to examine humour, emotional language and unusual titles. For example, in a busy online feed, a question first has to stand out to other users. I used this to guide my first pilot, comparing five high-response requests with five low-response requests.
One expressive post had attracted 139 comments, which initially supported my assumption that attention-grabbing or humorous wording would be most successful. But a much plainer, ‘less engaging’ request had also received 94 comments, while other expressive posts had far fewer. This forced me to separate the ideas of ‘attracting attention’ and ‘encouraging someone to answer’.
Castells’ (2000) network society framework helped me think beyond individual posts. Online communities allow knowledge to move between people who are connected by a shared interest rather than physical location. In this case, Reddit’s value depends on members being willing to offer their experience to strangers. I had to understand both if wording stood out, and if it helped another reader recognise that they had something useful to contribute.
This was definitely the biggest change in my research. The theory changed the questions I had to ask the data.
What surprised me about the results
My pilot also challenged my assumptions about specificity. I had expected narrow and ultra-specific requests with lots of conditions to receive fewer replies because fewer books would qualify. To investigate properly, I expanded the project using Apify, which ‘scraped’ Reddit using my specifications to collect 350 recent posts from the subreddit and filtered them to 169 genuine recommendation requests. This program was a bit of a gamechanger as it allowed me to increase the dataset massively. Prior to this digital artefact, I kind of assumed that this level of data analysis was only possible with special or expensive software.
I then compared factors including title style, humour, emotion, direct community language, examples and level of specificity. Unexpectedly, highly specific requests averaged 44.3 comments, compared with 33.1 for less specific requests. Posts directly addressing the community also tended to attract more comments.
I interpreted this through Raban and Harper’s (2008) work on motivations for answering questions online. Their research suggests that people contribute for social and personal reasons, not just because information is requested. My findings suggested that the details I saw as ‘too specific’ can actually act as an invitation. A reader sees a familiar trope, character type or book and thinks, “I know exactly what to recommend.” I couldn’t prove that motivation from comment counts, but it offered a more useful explanation than my original assumption that shorter, broader requests would always be easier to answer and therefore would attract a larger number of people to provide recommendations.
This also changed my understanding of the attention economy. Being noticed may be necessary for participation, but it does not guarantee it. The ability to activate a community’s shared knowledge seemed just as important.
Challenges with the research process
The practical side of the project was more difficult than I expected. Scraping, filtering and coding posts required me to learn new tools and make decisions about what could actually be used as meaningful evidence. I used generative AI to help code language features, but I learned really quickly that it could (and did) make several mistakes that a human generally wouldn’t make, which made it tricky to identify.
For example, in the first pass through, the way AI detected whether or not a post author had mentioned another book or author was super rigid and relied too heavily on formatting. A later audit identified more than 40 missed references, which I corrected before interpreting the results. This made me more worried about treating AI output as objective, especially when trying to analyse language features which can be so subjective. The categories were still my decisions, and I still had to check all of them to see whether they represented what users had actually written.
There were also limitations that were out of the scope of the artefact. Comment counts measured volume, not whether recommendations were useful. Posting time, visibility, and popular tropes might have influenced results, while terms like ‘humour’ were subjective and open to interpretation. I had to describe patterns as associations, rather than claim certain wording caused more comments.
I think I handled these problems better as the project developed, although I spent too long refining categories and not enough time examining the replies themselves. Next time, I would set a clearer limit on the coding stage and include a smaller qualitative analysis of response quality.
What I learned and what I’d do differently
Publishing my findings in a blog post, supported by a visual summary and dataset, made me consider the difference between producing research and making it understandable to others. I had to turn the findings into insights without presenting them as universal rules.
However, I could have done more to test whether the final artefact actually helped other students learn. Making work available online is not the same as receiving feedback or having a conversation about it. Although feedback on my earlier work influenced my direction, seeking a response to the final findings would have strengthened this part of the project.
Overall, I’m happy that I didn’t force the data to confirm my initial assumptions. The feedback about not incorporating academic concepts showed that my initial approach wasn’t super strong, but responding to it helped me see Reddit as a network where attention, social motivation and shared knowledge all come together. I finished with stronger research and digital literacy skills, but also a better understanding of how to use theory to question my own assumptions, rather than just confirming ideas I already have.
Reference List
Castells, M 1996, The rise of the network society, Blackwell, Oxford.
Goldhaber, MH 1997, ‘The attention economy and the net’, First Monday, vol. 2, no. 4.
Raban, DR & Harper, FM 2008, ‘Motivations for answering questions online’, in D Caspi & T Azran (eds), New Media and Innovative Technologies – Industry and Society, Burda Center Publishing.
AI Acknowledgement
Generative AI, including ChatGPT, was used to assist with the structure, formatting and refinement in this report. My insights throughout the reflection, including the evaluation of my digital artefact process and the conclusions drawn, are my own.
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