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Rescooped by Gerrit Bes from Content Curation World
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How To Gradually Grow a Good List of Reliable News Sources on Twitter

How To Gradually Grow a Good List of Reliable News Sources on Twitter | Latest Social Media News | Scoop.it
Sometimes you need to quickly immerse yourself in a new field. You might want to gain expertise or quickly gauge what the current issues are around a particular topic. One way of doing this is by c...

Via Howard Rheingold, Robin Good
Pacific Cove's comment July 31, 2013 3:04 AM
Thank you Steve, You always share very informative articles.
Stephen Dale's comment, July 31, 2013 4:48 AM
Thanks for the kind words :-)
Lucy Wyatt's curator insight, October 10, 2013 9:54 AM

This might be good for students beginning a current research topic.

Rescooped by Gerrit Bes from Content Curation World
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How To Best Curate Twitter User Lists: Research Paper (PDF)

How To Best Curate Twitter User Lists: Research Paper (PDF) | Latest Social Media News | Scoop.it

From the original research paper by Derek Greene, Gavin Sheridan, Barry Smyth, and Padraig Cunningham of the School of Computer Science & Informatics, University College Dublin entitled "Aggregating Content and Network Information
to Curate Twitter User Lists": "The problem of content curation in social media networks is becoming increasingly important, particularly in the context of news curation for media outlets.


In the case of Twitter, curating a list of authoritative users tweeting about a
given news story provides a means of monitoring discussions around that story.

 

However, currently this is a time-consuming manual task. Here we presented a
range of criteria for building topical user lists, based on an initial seed set.

 

By analysing the cohesion of the training data across di erent views of the same
datasets, we demonstrated the strengths and weaknesses of these recommenda-
tion criteria, in the context of a limited availability of Twitter data.

 

To overcome the weaknesses, we proposed the use of SVD rank aggregation. Experiments on a range of Twitter datasets relating to US politics demonstrated that this aggregation process yields more robust recommendations, succeeding in cases where individual content- or network-based criteria perform poorly."

 

http://arxiv.org/pdf/1206.1728.pdf


Via Paulo Simões, Robin Good
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