AoIR Flashpoint Symposium, Urbino, 24. June 2019
Felix Victor Münch, Ben Thies, Cornelius Puschmann, Axel Bruns
We present first results of a successful experiment with an adapted version of a network sampling method, the so-called rank degree method, which we modified to create a sample of the most influential accounts in the German-speaking Twitter follow network.
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Walking Through Twitter: Sampling a Language-Based Follow Network
1. Walking
Through Twitter
Sampling a Language-Based Follow Network
Felix Victor Münch1
, Ben Thies1
, Cornelius Puschmann1
, Axel Bruns2
1
Leibniz Institute for Media Research | Hans-Bredow-Institut (HBI), Germany
2
Digital Media Research Centre (DMRC), Queensland University of Technology (QUT), Australia
AoIR Flashpoint Symposium, Urbino, 24. June 2019
2. Problem
‘I can’t get no population …’
● follow/friend networks of
Online Social Networks (OSNs)
arguably main predictor for
content exposure (despite
sponsored content and
algorithmic sorting of
timelines)
● APIs too restrictive for
collection of comprehensive
follow networks
3. Opportunities
Possible Research Questions
● “Are there filter bubbles/echo
chambers?”
● “Are there social and/or topical
communities/issue publics?”
● “Who can spread which content
most efficiently?”
● “Can we predict the spread of
(fake) news?”
● “Who let the bots out? And
where?”
5. The Australian Twittersphere
Bruns, A., Moon, B., Münch, F. V., & Sadkowsky, T. (2017). The Australian Twittersphere in 2016:
mapping the follower/followee network. Social Media + Society, 3(4).
https://doi.org/10.1177/2056305117748162
Münch, F. V. (2019). Measuring the Networked Public – Exploring Network Science Methods
for Large Scale Online Media Studies. Queensland University of Technology.
https://doi.org/10.5204/thesis.eprints.125543
7. Our adaptation of the ‘rank degree’ method
Based on: Salamanos, N., Voudigari, E., & Yannakoudakis, E. J. (2017). Deterministic graph exploration for efficient graph sampling. Social
Network Analysis and Mining, 7(1), 24. https://doi.org/10.1007/s13278-017-0441-6
8. Our adaptation of the ‘rank degree’ method
Main adaptations (amongst others) mostly due to API restrictions:
● Undirected → Directed
● Fewer walkers (200)
● Walkers do not collapse when ending up on the same node
● Last 5000 friends only
● ‘Degree’ stays constant and is equal to follower count reported by Twitter API
● Only collect edges to accounts with German as their interface language (not possible
anymore due to API changes ¯_(ツ)_/¯ … we work on a solution.)
12. Coverage
Ranked distribution of the percentage of ‘friends’ of accounts in a random sample
(n=1000, filtered for having at least 2 ‘friends’) found in our influencer sample (excl. nodes
with in-degree 0) and a random sample of the same size (181k accounts)
13. Reach
Ranked distribution of the percentage of accounts reached in a random sample (n=1000,
filtered for having at least 2 ‘friends’) by accounts in our influencer sample (excl. nodes
with in-degree 0) and by accounts in a random sample of the same size (181k accounts)
15. Community detection with
infomap algorithm
3-core of our sample network;
coloured by communities detected
with the infomap community
detection algorithm;
node size represents Page Rank
17. Tagged Community graph Community graph of communities
in the 3-core of our sample with
over 300 accounts, at least 80 active
accounts during the examined time
frame, and edges with a weight of at
least 150; edge width represents
weight; edge direction follows
clockwise curvature; edges coloured
by source node; node size represents
the number of accounts in each
community
18. Outlook
● Adaption to API changes
● Bootstrapping the seed pool
● Other language-based spheres
● Topical mining
● Other community detection methods
● Bot detection
20. Interesting 🤔
Münch, F. V. (2019).
Measuring the Networked Public – Exploring Network
Science Methods for Large Scale Online Media Studies.
Queensland University of Technology.
https://doi.org/10.5204/thesis.eprints.125543