A Network Report

BCM206 - Assessment 2

BCM206 Network Report – Ella Cooley, Lara Ballantine, Regan Anderson, Joel Rainbow and Elayna Moxey

Group Digital Artefact Introductions:

Joel’s digital artefact is a podcast series, “Curiosity in Rising”, which usually focuses on spirituality and lifestyle topics. The podcast aims to inspire listeners to adopt an open-minded approach to various aspects of life. Guests are often featured and discussions on topics such as mindfulness, emotional maturity, and the importance of curiosity occur.

Lara’s digital artefact is a TikTok series called the Freelance Files. This artefact is designed for those looking to transition from a 9-5 job to a freelance career. This series will explore the essentials of freelancing, from building confidence to understanding what freelance life entails, offering guidance and reassurance along the way.

Regan’s digital artefact is a podcast called First and Last Names, a practical application of the growing notion that anyone can start a podcast. First and Last Names revolves around stories of our lives, testing if everyday conversations are entertaining and long enough to sustain an audience or if successful podcasting is created through a false persona.

Elayna’s digital artefact, @life.with.layna, is a series of mini vlogs posted to TikTok that showcase the realistic lifestyle of a first year University student. Elayna’s vlogs aim to oppose the unrealistic standards within the lifestyle niche. Having gained an audience for this project, Elayna plans to expand further on these issues within her content in the following weeks.

Ella’s digital artefact, @coolsrodshop, focuses on documenting and sharing the experiences of hot rod enthusiast’s in Australia. Through Instagram, TikTok, and YouTube Shorts, it captures car events and community interactions through short vlog-style videos. Its aim is to engage and inform viewers and preserve and grow hot rod culture in Australia.

Editing + Audio

Throughout our networking experience, our group encountered various challenges and events that facilitated valuable discussions and connections. One such event included our shared difficulties surrounding audio and video editing tools. As each of our projects share a focus on producing, discussing and/or analysing elements within the ‘lifestyle’ niche, we also shared a mutual value for high quality content. As all members within our group struggled with this, we began discussing differing solutions and editing tools, such as apps like CapCut, applied within our own DA’s that could potentially resolve certain issues others within our network were facing. 

A discussion in our network chat in which technical difficulties were discussed

By utilising and applying information provided by others within the network, our DA’s inherently shifted from a centralised network, to decentralised. Yoo (2019) suggests that due to centralised topologies having a strong reliance on its central node, this networking format is the most vulnerable and prone to failure. Comparatively, decentralised topologies are more resilient, and thus more reliable, when networking and exchanging relevant information. As digital networking media’s are inherently exchange-oriented, creating a decentralised structure that highlights the stable flow of information to each of our projects emphasises the effectiveness and functionality of network topologies (Stalder 2005).

Visibility on the Algorithm

Due to the high prevalence of social media in all of our projects, visibility on the algorithm is an important factor in the success and appreciation of our digital artefacts. The algorithm is “tailored to satisfy the specific interests of long-tail and individual users’ (Zhang & Liu 2021 ) and this can be applied to all social media platforms. One event which demonstrates our struggle with the algorithm are bans and reviews placed on short form videos across DA’s.  Tiktok gave little reasoning for these so as a group we worked to figure out what they were for and how to avoid these in the future. One digital artefact received the title ‘not eligible for recommendation on the For You feed’, which was not a category previously known to the group, whilst other battled generic community guideline violations. Through group research we discovered that this could be due to either the content being unsafe, or not different or diverse enough. This was detrimental to engagement as the For You page is the main source of views and any video ‘under review’ will be less likely to be promoted. By collaborating with each other to solve problems, the network was able to move to a decentralised model and hence work more efficiently to overcome issues with the algorithms with our varied knowledge of different platforms.

Conclusion

Through collaborative efforts in editing, audio production, and optimising algorithmic visibility, our network transitioned from a centralised structure to a more decentralised model. By pooling our individual expertise and working together to overcome challenges, we can now improve the quality of our digital content while efficiently navigating platform-specific obstacles. Looking ahead, the continued growth of our projects will rely on ongoing adaptation and collective learning, ensuring they remain relevant and impactful with a relevant social utility, it will be helpful for us all to continue providing feedback and shared frustrations and solutions as we continue our DA's.

References:

For You feed Eligibility Standards 2024, Tiktok.com, viewed 15 September 2024, <https://www.tiktok.com/community-guidelines/en/fyf-standards >.

Stalder, F n.d., Open Cultures and the Nature of Networks, viewed 15 September 2024, <https://felix.openflows.com/pdf/Notebook_eng.pdf>.

Wall, T 2024, Centralised and Decentralised Networks, vodcast 16 August, viewed 13 September 2024, <https://youtu.be/13DhWOK5ZU4>

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Yoo, Christopher S., Paul Baran, Network Theory, and the Past, Present, and Future of Internet (2018). Colorado Technology Law Journal, Vol. 17, P. 161, 2018, U of Penn, Inst for Law & Econ Research Paper No. 18-38 < http://dx.doi.org/10.2139/ssrn.3317642>

Zhang, M. and Liu, Y., 2021. A commentary of TikTok recommendation algorithms in MIT Technology Review 2021. Fundamental Research, 1(6), pp.846-847.