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Predicting Tie Strength with the Facebook API

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. Tie strength.

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Predicting Tie Strength with the Facebook API

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  1. Predicting Tie Strength with the Facebook API • TasosSpiliotopoulos • 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

  2. Tie strength “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.

  3. Tie strength calculation and Facebook • 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

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

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  12. Results • 90 participants (59% male) • 1728Facebook 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 18 predictive variables based on: • privacy preservation • previous literature

  14. Results – regression model of tie strength

  15. Results – tie strength distributions • 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 • 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

  17. Contributions • 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

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  19. Contributions

  20. Contributions • 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

  21. Contributions • 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 Thank you!

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