Last week I attended the RC33 conference of the ISA in Sydney, Australia. I participated in two events: a session on social media and a panel session on computational social science, both organized and chaired by Robert Ackland. In the session on social media I talked about my experiences working with Twitter data and the problems, solutions and opportunities involved in using these data. Below are the slides.
The panel session on computational social science we discussed about what it is, what we can do with it as well as computational social science in the era of Big Data. As for the latter part, I do think we gain using the Big Data, although we must acknowledge their limitations. However, I also think that Small Data still has the preference for now. Whereas the use of Big Data particularly involves the analyses of large systems, but still results in fairly descriptive analysis, small data allows for the analysis of specific cases. The benefit of the use of specific cases is that particularly social media data, that are limited when downloaded from SNS’s API’s, can be augmented / enriched by added additional data. If you read our work on social media and web campaigning in general, these analyses always use additional data about parties and their candidates. This way we can move beyond the descriptive analyses of social media.
Of course, computational social science is much more than using large amounts of data. Simulating behavior according to specific rules is also part of it. Still, computer simulation has been around for some decades already and – from my perspective – they still are not widely used. At least not being published about in academic communication journals. An exception in the field I am interested in (political campaigning) is Gulati et al. on modelling voting behavior.