JCP 2018 Vol.13(7): 761-770 ISSN: 1796-203X
doi: 10.17706/jcp.13.7.761-770
doi: 10.17706/jcp.13.7.761-770
Raad Bin Tareaf, Philipp Berger, Patrick Hennig, Sebastian Koall, Jan Kohstall, Christoph Meinel
Hasso Plattner Institute, University of Potsdam, Potsdam, Germany.
Abstract—In this paper, we present our experiences in analyzing Twitter data. The analysis has shown that information diffuses over time through the Twitter network in certain patterns. Furthermore, it has shown those friend relationships significantly influence the information propagation speed on Twitter. Since it was launched in 2006, the microblogging service grew tremendously. Tweets are sent by users all around the world. Results show that there are two major patterns. While these patterns accommodate us to understand the diffusion of information through Twitter in an even better plan, the analysis of friend networks provides information on who influences the network, concerning the number of re-tweets and the time between a tweet and its re-tweets. The approaches have been evaluated both technically, based on how certain a topic matches one of the patterns and how prominent friends are compared to other users, and conceptually, based on existing, well-known approaches in measuring the speed and scale of information diffusion on Twitter.
Index Terms—Friends network, pattern detection, propagation speed, twitter analysis.
Abstract—In this paper, we present our experiences in analyzing Twitter data. The analysis has shown that information diffuses over time through the Twitter network in certain patterns. Furthermore, it has shown those friend relationships significantly influence the information propagation speed on Twitter. Since it was launched in 2006, the microblogging service grew tremendously. Tweets are sent by users all around the world. Results show that there are two major patterns. While these patterns accommodate us to understand the diffusion of information through Twitter in an even better plan, the analysis of friend networks provides information on who influences the network, concerning the number of re-tweets and the time between a tweet and its re-tweets. The approaches have been evaluated both technically, based on how certain a topic matches one of the patterns and how prominent friends are compared to other users, and conceptually, based on existing, well-known approaches in measuring the speed and scale of information diffusion on Twitter.
Index Terms—Friends network, pattern detection, propagation speed, twitter analysis.
Cite: Raad Bin Tareaf, Philipp Berger, Patrick Hennig, Sebastian Koall, Jan Kohstall, Christoph Meinel, "Information Propagation Speed and Patterns in Social Networks: A Case Study Analysis of German Tweets," Journal of Computers vol. 13, no. 7, pp. 761-770 , 2018.
General Information
ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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