Information network or social network?: the structure of the twitter follow graph

By Seth A. Myers, Aneesh Sharma, Pankaj Gupta, Jimmy J. Lin. 2014

409 citations recorded by the source corpus.

In this paper, we provide a characterization of the topological features of the Twitter follow graph, analyzing properties such as degree distributions, connected components, shortest path lengths, clustering coefficients, and degree assortativity. For each of these properties, we compare and contrast with available data from other social networks. These analyses provide a set of authoritative statistics that the community can reference. In addition, we use these data to investigate an often-posed question: Is Twitter a social network or an information network? The "follow" relationship in Twitter is primarily about information consumption, yet many follows are built on social ties. Not surprisingly, we find that the Twitter follow graph exhibits structural characteristics of both an information network and a social network. Going beyond descriptive characterizations, we hypothesize that from an individual user's perspective, Twitter starts off more like an information network, but evolves to behave more like a social network. We provide preliminary evidence that may serve as a formal model of how a hybrid network like Twitter evolves.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics, Social Sciences & Humanities

How researchers see this paper

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

In Computer Networks and Communications

1. Sharad Goel
2. Jimmy J. Lin
3. Jian Tang
4. Qiaozhu Mei
5. James H. Fowler
6. Aneesh Sharma
7. Jessica Su
8. E. Moro

In Statistical and Nonlinear Physics

1. Jian Tang
2. Meng Qu
3. Aneesh Sharma
4. Sharad Goel
5. Jimmy J. Lin
6. Hai Liang
7. Qiaozhu Mei
8. James H. Fowler

In Social Sciences & Humanities

1. Sharad Goel
2. Jimmy J. Lin
3. Jian Tang
4. Qiaozhu Mei
5. James H. Fowler
6. Mingzhe Wang
7. Jun Yan
8. Meng Qu

Citations in the corpus

• LINE: Large-scale Information Network Embedding (2015, background): “For example, the Twitter followee-follower network contains 175 million active users and around twenty billion edges in 2012 [14].”
• GraphJet: Real-Time Content Recommendations at Twitter (2016, background): “Such a memory allocation strategy is sensible because many aspects of Twitter graphs follow power-law distributions [31].”
• The Effect of Recommendations on Network Structure (2016, background): “2 This phenomenon, which has been observed previously [22], indicates that a user’s number of followers is not a good proxy for its reach, and in particular, that popular users might grow slower than preferential attachment suggests.”
• Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science (2015, background): “Although most real world social networks have a power law exponent between 2 and 3, previous studies found very inconsistent results on Twitter follower-followee network [5, 6].”
• Rapid assessment of disaster damage using social media activity (2016, background): “Such openness leads to a network topology characterized by a large number of accounts followed by an average user, placing Twitter somewhere in between a purely social network and a purely informational network ( 30 ).”