Paper Info
Title
How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter.
Abstract
As it becomes common for Internet users to use hashtags when posting and searching information on social media, it is important to understand who builds a hashtag network and how information is circulated within the network. This article focused on unlocking the potential of the #AlphaGo hashtag network by addressing the following questions. First, the current study examined whether traditional opinion leadership (i.e., the influentials hypothesis) or grassroot participation by the public (i.e., the interpersonal hypothesis) drove dissemination of information in the hashtag network. Second, several unique patterns of information distribution by key users were identified. Finally, the association between attributes of key users who exerted great influence on information distribution (i.e., the number of followers and follows) and their central status in the network was tested. To answer the proffered research questions, a social network analysis was conducted using a large-scale hashtag network data set from Twitter (n=21,870). The results showed that the leading actors in the network were actively receiving information from their followers rather than serving as intermediaries between the original information sources and the public. Moreover, the leading actors played several roles (i.e., conversation starters, influencers, and active engagers) in the network. Furthermore, the number of their follows and followers were significantly associated with their central status in the hashtag network. Based on the results, the current research explained how the information was exchanged in the hashtag network by proposing the reciprocal model of information flow.
Year
DOI
Venue
2017
10.1089/cyber.2016.0572
CYBERPSYCHOLOGY BEHAVIOR AND SOCIAL NETWORKING
Keywords
Field
DocType
Twitter,hashtag network,artificial intelligence,AlphaGo,social network analysis,the reciprocal model of information flow
Network on,Information flow (information theory),World Wide Web,Interpersonal communication,Social media,Social network analysis,Psychology,Dissemination,Opinion leadership,Multimedia,The Internet
Journal
Volume
Issue
ISSN
20.0
12
2152-2715
Citations 
PageRank 
References 
0
0.34
6
Authors
1
Name
Order
Citations
PageRank
Jin Young Kim149781.76