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Crawling Online Social Networks
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-3219-9598
Blekinge Institute of Technology, School of Computing.ORCID iD: 0000-0002-9316-4842
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
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2015 (English)Conference paper (Refereed)
Abstract [en]

Researchers put in tremendous amount of time and effort in order to crawl the information from online social networks. With the variety and the vast amount of information shared on online social networks today, different crawlers have been designed to capture several types of information. We have developed a novel crawler called SINCE. This crawler differs significantly from other existing crawlers in terms of efficiency and crawling depth. We are getting all interactions related to every single post. In addition, are we able to understand interaction dynamics, enabling support for making informed decisions on what content to re-crawl in order to get the most recent snapshot of interactions. Finally we evaluate our crawler against other existing crawlers in terms of completeness and efficiency. Over the last years we have crawled public communities on Facebook, resulting in over 500 million unique Facebook users, 50 million posts, 500 million comments and over 6 billion likes.

Place, publisher, year, edition, pages
2015. 9-16 p.
Keyword [en]
Crawlers;Facebook;Feeds;Informatics;Sampling methods;Silicon compounds;crawling;mining;online social media;online social networks
National Category
Computer Science
URN: urn:nbn:se:bth-10993DOI: 10.1109/ENIC.2015.10OAI: diva2:899462
Second European Network Intelligence Conference (ENIC)
Available from: 2016-02-02 Created: 2015-11-20 Last updated: 2016-02-04Bibliographically approved

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Erlandsson, FredrikBoldt, MartinJohnson, Henric
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Department of Computer Science and EngineeringSchool of Computing
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