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Predicting Tie Strength with the Facebook API Tasos Spiliotopoulos Madeira-ITI, University of Madeira, Portugal / Harokopio University, Greece Diogo Pereira.

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Presentation on theme: "Predicting Tie Strength with the Facebook API Tasos Spiliotopoulos Madeira-ITI, University of Madeira, Portugal / Harokopio University, Greece Diogo Pereira."— Presentation transcript:

1 Predicting Tie Strength with the Facebook API Tasos Spiliotopoulos Madeira-ITI, University of Madeira, Portugal / Harokopio University, Greece Diogo Pereira University of Madeira, Portugal Ian Oakley Ulsan National Institute of Science and Technology, Republic of Korea 18th Panhellenic Conference on Informatics (PCI 2014), 2-4 October 2014, Athens, Greece 1

2 “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 Ties Strong ties. Weak ties. Tie strength 2

3 Gilbert & Karahalios: a browser script that crawled Facebook web pages Panovich et al: Facebook’s “Download Your Data” feature Burke & Kraut: Facebook server logs Others: Publicly available datasets Asynchronous calculation Non-standard tools and technologies Tie strength calculation and Facebook 3

4 90 participants 1728 friendships rated 18 variables collected via the Facebook API Study description 4

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11 Study description 11

12 Results participants (59% male) 1728 Facebook friendships Mean 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 week

13 Results – data collected predictive variables based on: privacy preservation previous literature

14 Results – regression model of tie strength 14

15 Results – tie strength distributions 15 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.

16 Results – classification % accuracy in differentiating between strong and weak ties, χ2 (1, N = 3456) = , p < % accuracy in differentiating between very strong and weaker ties, χ2 (1, N = 3456) = , p < 0.001

17 Assessing tie strength calculation in real time Enables automated friend characterization -> friend grouping, customized feeds, adaptive privacy controls, friend recommendations, content recommendations, more efficient information seeking Enables more sophisticated social network analysis Contributions 17

18 Contributions 18

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20 Assessing tie strength calculation in real time Enables automated friend characterization -> friend grouping, customized feeds, adaptive privacy controls, friend recommendations, content recommendations, more efficient information seeking Enables more sophisticated social network analysis Better understanding of tie strength A model of tie strength Weights of the predictor variables Insights for computational social science studies Contributions 20

21 Assessing tie strength calculation in real time Enables automated friend characterization -> friend grouping, customized feeds, adaptive privacy controls, friend recommendations, content recommendations, more efficient information seeking Enables more sophisticated social network analysis Better understanding of tie strength A model of tie strength Weights of the predictor variables Insights for computational social science studies Contributions 21 Thank you!


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