Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

A retweet prediction method of micro-blog big data users based on Map/Reduce

Authors
Yuelong Zhao, Meng Fang
Corresponding Author
Yuelong Zhao
Available Online November 2016.
DOI
https://doi.org/10.2991/aest-16.2016.99How to use a DOI?
Keywords
micro-blog; big data; hadoop; map/reduce; retweet prediction.
Abstract
In all kinds of social network software, retweet is a common behavior and a important mechanism for information dissemination. Especially retweet prediction of micro-blog users is very important to deep research. However traditional method can't be effectively applied to big data. To solve this problem, in this paper, first study the most relevant features of retweet, such as proximity social network, retweet activity, etc. Second, use Map/Reduce programming framework to achieve the extraction of feature set and an improved random forests algorithm. Third, gives a distributed approach based on Hadoop platform and use this algorithm for parallel retweet prediction of user's concern edge. Finally, do some experiment by using real data sets of Sina's Micro-blog. Experiments show that this distributed approach based on hadoop platform is better than traditional design, it can effectively predict retweet of Micro-blog user in less time.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-257-2
ISSN
1951-6851
DOI
https://doi.org/10.2991/aest-16.2016.99How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yuelong Zhao
AU  - Meng Fang
PY  - 2016/11
DA  - 2016/11
TI  - A retweet prediction method of micro-blog big data users based on Map/Reduce
BT  - 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.99
DO  - https://doi.org/10.2991/aest-16.2016.99
ID  - Zhao2016/11
ER  -