Day 1, Keynote. Economic Complexity http://dx.doi.org/10.1145/2631775.2631812. visualisation tools to support knowledge flow and prediction. Practical space as opposed to fundamental research. What do we know more based on the work? Seems to be that we can predict the economics of the future from the actuality of the past. And that the knowledge a country possess can be estimated by the stuff it exports. This might be hard to process stuff or easy unrefined stuff – work suggest knowledge lays with the former. DataViva.info uses D3:d Pantheon:f project is also a cultural project using similar machinery. Very nice idea to use how many languages a wikipedia article is in, to define its cultural worth.
Seems to be about approximation too, that while not accurate analysis, approximation enables better prediction. [hl: Approximate analysis produces more accurate useful predictions.] immersion.media.mit.edu a personal analysis of email.
A Study of Age Gaps between Online Friends http://dx.doi.org/10.1145/2631775.2631800. Not tied back to social consequences or to a rational why this stuff exists. But this seems to not be pursued here.
An Author-Reader Influence Model for Detecting Topic-based Influencers in Social Media http://dx.doi.org/10.1145/2631775.2631804.Not directly my area and so not much here for me.
‘Am I More Similar to My Followers or Followees? Homophily Effect in Directed ..http://dx.doi.org/10.1145/2631775.2631828
Co-following on Twitter http://dx.doi.org/10.1145/2631775.2631820
A Linked Data Approach to Care Coordination http://dx.doi.org/10.1145/2631775.2631807
The Wisdom of Ad-Hoc Crowds http://dx.doi.org/10.1145/2631775.2631813. Content quality is lower in social media (than traditional) but there is more of it. Ricardo finds the long tail more interesting as that is where we will find diversity. Personalisation vs contextualisation, cont. about content so we share what we do not who we are. All of us are in the long tail so any service has to care – see WSDM 2009 paper. Diversity, Serendipity, Cold start problem. GET THE SLIDES.
Online Popularity and Topical Interests through the Lens of Instagram – http://dx.doi.org/10.1145/2631775.2631808 – Clustering in tags etc the visual representation reminded me of our work maybe we could think about gaze clustering, saccade clustering, or scan-path clustering… as opposed to common scan-path.
Understanding and Controlling the Filter Bubble through Interactive Visualization: A User Study – http://dx.doi.org/10.1145/2631775.2631811
MADMICA privacy aware decentralised online http://madmica.usask.ca/
‘The filter bubble’ is a term popularised by Eli Pariser.
How you post is who you are: characterising Google+ status updates across social groups http://dx.doi.org/10.1145/2631775.2631822 interesting to see that Indian groups tweet about family, and happiness – while Western is work home and money.
A Taxonomy of Microtasks on the Web http://dx.doi.org/10.1145/2631775.2631819 interesting for COPE? Abstract: Nowadays, a substantial number of people are turning to crowdsourcing, in order to resolve tasks that require human intervention. Despite a considerable amount of research done in the field of crowdsourcing, existing works fall short when it comes to classifying typically crowdsourced tasks. Understanding the dynamics of the tasks that are crowdsourced and the workers’ behaviour, plays a vital role in efficient task-design. In this paper, we propose a two-level classification scheme for tasks, based on an extensive study of 1000 workers on CrowdFlower. In addition, we present insights into certain aspects of crowd behaviour; the task affinity of workers, effort exerted by workers to complete tasks of various types, and their satisfaction with the monetary incentives.