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'Big Data'

Identifying MOOC Learners on Social Media Platforms

We start the first paper session at WebSci 2016 with a paper by Guanliang Chen that examines learner engagement with Massively Open Online Courses (MOOCs). These generate a great deal of data about learner engagement during the MOOC itself, but there's very little information about learners before and after this experience. Can we use external social Web data to identify and profile these learners, in order to better customise the learning experience for them?

Web Science and Biases in Big Data

It's a cool morning in Germany, and I'm in Hannover for the opening of the 2016 Web Science conference, where later today my colleague Katrin Weller and I will present our paper calling for more efforts to preserve social media content as a first draft of the present. But we start with an opening keynote by Yahoo!'s Ricardo Baeza-Yates, on Data and Algorithmic Bias in the Web.

Ricardo begins by pointing out that all data have a built-in bias; additional bias is added in the data processing and interpretation. For instance, some researchers working with Twitter data then extrapolate across entire populations, although Twitter's demographics are not representative for the wider public. There are even biases in the process of measuring for bias.

Twitter in Germany: A Big Data Perspective (GAU 2015)

Georg-August-Universität Göttingen

Twitter in Germany: A Big Data Perspective

Axel Bruns

  • 3 June 2015 – Georg-August-Universität Göttingen

Social Media News Audiences and the Quantified Journalist (ICA 2015)

International Communication Association conference 2015

Social Media News Audiences and the Quantified Journalist

Tim Highfield and Axel Bruns

Four New Chapters on the Challenges of Doing Twitter Research

One more post before I head home from the AoIR 2015 conference in Phoenix: during the conference, I also received my author’s copy of Hashtag Publics, an excellent new collection edited by Nathan Rambukkana. In this collection, Jean Burgess and I published an updated version of our paper from the ECPR conference in Reykjavík, which conceptualises (some) hashtag communities as ad hoc publics – and Theresa Sauter and I also have a chapter in the book that explores the #auspol hashtag for Australian politics.

Axel Bruns and Jean Burgess. “Twitter Hashtags from Ad Hoc to Calculated Publics.” In Hashtag Publics: The Power and Politics of Discursive Networks, ed. Nathan Rambukkana. New York: Peter Lang, 2015. 13-28.

Theresa Sauter and Axel Bruns. “#auspol: The Hashtag as Community, Event, and Material Object for Engaging with Australian Politics.” In Hashtag Publics: The Power and Politics of Discursive Networks, ed. Nathan Rambukkana. New York: Peter Lang, 2015. 47-60.

Moving beyond First-Person Platform Studies

Finally in this AoIR 2015 session, we move on to Greg Elmer, one of the editors of Compromised Data: From Social Media to Big Data. His contribution is focussed on the practice of collecting data from social media sites, some of which is done using some very simple Web scraping tools (as Edward Snowden did at the NSA, apparently).

Reverse-Engineering Social Media Platforms

The next speaker in the Compromised Data session at AoIR 2015 is Robert Gehl, whose focus is on the effects of corporate social media. There is a conflict between the critiques of proprietary social media spaces and the obvious pleasures of using social media; what do we do about this?


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