Faster than the speed of print: Reconciling 'big data' social media analysis and academic scholarship

By Axel Bruns. 2013

79 citations recorded by the source corpus.

The promise of ‘big data’ has generated a significant deal of interest in the development of new approaches to research in the humanities and social sciences, as well as a range of important critical interventions which warn of an unquestioned rush to ‘big data’. Drawing on the experiences made in developing innovative ‘big data’ approaches to social media research, this paper examines some of the repercussions for the scholarly research and publication practices of those researchers who do pursue the path of ‘big data’–centric investigation in their work. As researchers import the tools and methods of highly quantitative, statistical analysis from the ‘hard’ sciences into computational, digital humanities research, must they also subscribe to the language and assumptions underlying such ‘scientificity’? If so, how does this affect the choices made in gathering, processing, analysing, and disseminating the outcomes of digital humanities research? In particular, is there a need to rethink the forms and formats of publishing scholarly work in order to enable the rigorous scrutiny and replicability of research outcomes?

Subfields: Artificial Intelligence, General Social Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In General Social Sciences

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “…develop baselines and guidelines (Tufekci, 2014; Weller and Kinder-Kurlanda, 2015), to ﬁnd common ground regarding methodological approaches (Counts et al., 2014), and to better document home-grown tools and methodologies, as well as data provenance (Bruns, 2013; Weller and Kinder-Kurlanda, 2015).”
Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines (2015, background): “Shaw, Burgess, Crawford, and Bruns (2013) also developed a typology of sense making practices in the context of Queensland’s floods in Australia, including flood preparations, the current situation on the ground, and individuals’ roles in the subsequent clean-up effort.”
Uncovering the Challenges in Collection, Sharing and Documentation: The Hidden Data of Social Media Research? (2021, methodology): “The desire to share data was pointed out by participants themselves, who described how they had experienced social media researchers not to be ‘guarding’ their data in the way that researchers from other disciplines usually would.”
Accepting the challenges of social media research (2015, unknown): “Bruns (2013) calls this “opportunistic data gathering”. Despite the critique of Twitter’s data access strategy, within the social media research landscape Twitter is among the platforms that allow comparatively easy and stable access to at least some sorts of data. This may be one of the reasons why”
Social media analytics - Challenges in topic discovery, data collection, and data preparation (2018, background): “In social sciences veracity is the main criterion for the assessment of big data (Bruns, 2013; King, 2011; Lin, 2015; Mahrt & Scharkow, 2013; Shah, Cappella, & Neuman, 2015).”