#mytweet via Instagram: Exploring user behaviour across multiple social networks

By Bang Hui Lim, Dongyuan Lu, Tao Chen, Min-Yen Kan. 2015

66 citations recorded by the source corpus.

We study how users of multiple online social networks (OSNs) employ and share information by studying a common user pool that use six OSNs - Flickr, Google+, Instagram, Tumblr, TWitter, and YouTube. We analyze the temporal and topical signature of users' sharing behaviour, showing how they exhibit distinct behaviorial patterns on different networks. We also examine cross-sharing (i.e., the act of user broadcasting their activity to multiple OSNs near-simultaneously), a previously-unstudied behaviour and demonstrate how certain OSNs play the roles of originating source and destination sinks.

Subfields: Communication, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. Alexandra Olteanu
2. Carlos Castillo
3. Emre Kiciman
4. Fernando Diaz
5. Muhammad Imran
6. Gabriella Kazai

In Statistical and Nonlinear Physics

1. Alexandra Olteanu
2. Carlos Castillo
3. Emre Kiciman
4. Fernando Diaz
5. Muhammad Imran

Citations in the corpus

• A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2018, background): “…representativeness of online social and behavioral data, covering major classes of social data biases such as population biases (distinct platforms are characterized by different demographic composition of users) [16, 24], or behavioral biases (users behave differently in different contexts) [23].”