Tasos Spiliotopoulos MadeiraITI University of Madeira Portugal Harokopio University Greece Diogo Pereira University of Madeira Portugal Ian Oakley Ulsan National Institute of Science and ID: 532696
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Predicting Tie Strength with the Facebook API
Tasos
SpiliotopoulosMadeira-ITI, University of Madeira, Portugal / Harokopio University, GreeceDiogo PereiraUniversity of Madeira, Portugal Ian OakleyUlsan National Institute of Science and Technology, Republic of Korea
18th
Panhellenic
Conference on Informatics (PCI 2014
), 2-4 October 2014, Athens, Greece
1Slide2
“a (probably) linear combination of the amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocal services which characterize a tie
” Mark Granovetter (1973) in The Strength of Weak TiesStrong ties.Weak ties.Tie strength2Slide3
Gilbert & Karahalios: a browser script that crawled Facebook web pages Panovich
et al: Facebook’s “Download Your Data” featureBurke & Kraut: Facebook server logsOthers: Publicly available datasetsAsynchronous calculationNon-standard tools and technologies Tie strength calculation and Facebook3Slide4
90 participants1728 friendships rated18 variables collected via the Facebook API
Study description4Slide5
Study description
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Study description6Slide7
Study description7Slide8
Study description8Slide9
Study description9Slide10
Study description10Slide11
Study description11Slide12
Results12
90 participants (59% male)1728 Facebook friendshipsMean age: 26.9 years (SD = 8.7) From 11 countries (85.6% from Portugal)Mean number of Facebook friends: 355 (SD = 218.9, range = 28 – 872)Using Facebook for an average of 13.4 (SD = 15.1) hours per weekSlide13
Results – data collected13
18 predictive variables based on:
privacy preservationprevious literatureSlide14
Results – regression model of tie strength
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Results – tie strength distributions
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The model underestimates tie strength (mean: 0.29 vs 0.13, median: 0.21 vs 0.1), but that’s common.19.7% of friendships rated by the participants were set to zero.Slide16
Results – classification16
65.9% accuracy in differentiating between strong and weak ties, χ2 (1, N = 3456) = 135.08, p < 0.001 86.3% accuracy in differentiating between very strong and weaker ties, χ2 (1, N = 3456) = 107.83, p < 0.001 Slide17
Assessing tie strength calculation in real timeEnables automated friend characterization -> friend grouping, customized feeds, adaptive privacy controls, friend recommendations, content recommendations, more efficient information seeking
Enables more sophisticated social network analysisContributions17Slide18
Contributions18Slide19
Contributions19Slide20
Assessing tie strength calculation in real timeEnables automated friend characterization -> friend grouping, customized feeds, adaptive privacy controls, friend recommendations, content recommendations, more efficient information seeking
Enables more sophisticated social network analysisBetter understanding of tie strengthA model of tie strengthWeights of the predictor variablesInsights for computational social science studiesContributions20Slide21
Assessing tie strength calculation in real timeEnables automated friend characterization -> friend grouping, customized feeds, adaptive privacy controls, friend recommendations, content recommendations, more efficient information seeking
Enables more sophisticated social network analysisBetter understanding of tie strengthA model of tie strengthWeights of the predictor variablesInsights for computational social science studiesContributions21Thank you!