18/08/2026
When I think about the internet, I am reminded of the profound concept of ‘emergent property’. The emergent property posits that more often than not, simple things evolve to become complex interacting systems, detached from what it’s individually made of. Isn’t that precisely what the internet is? At its barest form it started from lines of code, but eventually it grew to gather the consciousnesses of billions. It curates social groups, behaviour, and ideologies in comical opposites. From activism to radicalism, from earnest education to ribald ridicule, from unitedly mourning tragedy… to what’s funny and trendy. The internet is humanity’s little microcosm, implanted with a little bit of our chaos.
The internet, conveniently, is also a goldmine for the social scientist. According to Kamila (2020): “The Internet is a collection of inexhaustible social and cultural data,”. It presents a hub for observational data about interactions, attitudes, opinions and virtual reactions to real-world events to be collected (Alenzi, 2020). In recent years, technological advancements in data processing and collection have revolutionized the research potential of ‘secondary data’ (Johnston, 2014). Online, open data repositories like Kaggle have invigorated the field of data science by improving accessibility and learning opportunities (Chow, 2019; Bojer & Meldgaard, 2021).
In Social Anthropology, we learnt about the movement of anthropologists doing field work at ‘home’, i.e. the insider of a society studying the mechanisms of their own community allowing them to give a holistic, nuanced account of phenomena. Incidentally, this ‘Social Science at home’ is NOT AT ALL similar to that concept, except for sharing the phrase ‘at home’. If I have to draw on anthropology as a comparison, the closest would be the movement of ‘Digital Anthropology’: where researchers dive deep into an online community much like field researchers would in real life.
In her book ‘Mining Social Media’, Lam Thuy Vo (2019) described the emerging trend of scouring digital data to conduct studies on social behavior. She outlined several pioneering techniques one can employ to do so. HTML, API and Java, for instance, predominantly govern the framework of many social media contents from images to videos. Through accessing these frameworks, researchers can collect insights into a piece of content’s viewership, engagement, and comments, all of which serve as invaluable, naturally occurring behavioural data. The potential of such ‘digital research’ is not lost to academics.
The approach of mining of social media saw valid usage as early as 2010, where Chew and Eysenbach (2010) employed content analysis models on Twitter posts to investigate public perception to the 2009 H1N1 pandemic. In an ironic turn of events, digital data mining saw a boom during the Covid-19 pandemic, when social distancing regulations necessitated the shift of social research onto virtual environments. Similar methods again emerged to analyse public perception through Twitter of the Covid-19 pandemic (Ntompras et al, 2022).
Which is to say, academic research is no longer bound to the lab. As a bolder argument, academic research may not even be bound to the institution anymore… the implications of this are worthy of an entire conference.
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