Perception of online social networks

Travis Green*, Aaron Quigley

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)


This paper examines data derived from an application on that investigates the relations among members of their online social network. It confirms that online social networks are more often used to maintain weak connections but that a subset of users focus on strong connections, determines that connection intensity to both connected people predicts perceptual accuracy, and shows that intra-group connections are perceived more accurately. Surprisingly, a user's sex does not influence accuracy, and one's number of friends only mildly correlates with accuracy indicating a flexible underlying cognitive structure. Users' reports of significantly increased numbers of weak connections indicate increased diversity of information flow to users. In addition the approach and dataset represent a candidate "ground truth" for other proximity metrics. Finally, implications in epidemiology, information transmission, network analysis, human behavior, economics, and neuroscience are summarized. Over a period of two weeks, 14,051 responses were gathered from 166 participants, approximately 80 per participant, which overlapped on 588 edges representing 1341 responses, approximately 10% of the total. Participants were primarily university-age students from English-speaking countries, and included 84 males and 82 females. Responses represent a random sampling of each participant's online connections, representing 953,969 possible connections, with the average participant having 483 friends. Offline research has indicated that people maintain approximately 8-10 strong connections from an average of 150-250 friends. These data indicate that people maintain online approximately 40 strong ties and 185 weak ties over an average of 483 friends. Average inter-group accuracy was below the guessing rate at 0.32, while accuracy on intra-group connections converged to the guessing rate, 0.5, as group size increased.

Original languageEnglish
Title of host publicationMining and Analyzing Social Networks
Number of pages16
Publication statusPublished - 31 May 2010

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


  • Node proximity
  • Social network analysis
  • Social network perception


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