Presentation at RC33 conference on social media

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.

The transformation from data journalism to computational journalism

Some time now journalism used to be a traditional profession of people investigating issues, talking to sources, writing it down and publishing it. But then the Internet came and journalists when onto the Net, emailing and surfing the Web as a new way to contact sources gather information and disseminate the news.
Ultimately, this evolved into a new type of journalist, one that collects data freely available on the Net and aggregates this in such a way it reveals new insights. This is called data journalism. Now there appears even another type of journalist, one that computes: computational journalism. I would suggest that computational journalism is an extension of data(-driven) journalism. Of course data as such are meaningles and only through some filtering – aggregation, comparisons etc – sense can be made of the large amounts of data, possibly made easier through the use of visualizations.
Data and computational journalism especially used in investigative journalism has been around for quite some time already. However, it received a great push through the use of APIs and the increased accessibility of databases through the Internet in general. The data repository of the Guardian is a good example of the latter. Still, analyzing data and visualizing the findings to convey the message of the journalist can be quite tricky. A source on creative data visualisation or visualisations gone wrong can be found at Flowing Data.
The video below is a lecture on computational journalism’s agenda Journalism and Media Studies Centre of Hong Kong University

Media Research Seminar: Computational Journalism: Mapping the Research Agenda from JMSC HKU on Vimeo.