Dynamic targeting in an online social medium

Peter Laflin, Alexander Vassilios Mantzaris, Peter Grindrod, Fiona Ainley, Amanda Otley, Desmond J. Higham

Research output: Contribution to conferenceProceedingpeer-review

81 Downloads (Pure)


Online human interactions take place within a dynamic hi-
erarchy, where social in
uence is determined by qualities such as status,
eloquence, trustworthiness, authority and persuasiveness. In this work,
we consider topic-based Twitter interaction networks, and address the
task of identifying in
uential players. Our motivation is the strong desire
of many commerical entities to increase their social media presence by
engaging positively with pivotal bloggers and tweeters. After discussing
some of the issues involved in extracting useful interaction data from
a Twitter feed, we dene the concept of an active node subnetwork se-
quence. This provides a time-dependent, topic-based, summary of rel-
evant Twitter activity. For these types of transient interactions, it has
been argued that the
ow of information, and hence the in
uence of a
node, is highly dependent on the timing of the links. Some nodes with
relatively small bandwidth may turn out to be key players because of
their prescience and their ability to instigate follow-on network activity.
To simulate a commercial application, we build an active node subnet-
work sequence based on key words in the area of travel and holidays.
We then compare a range of network centrality measures, including a
recently proposed version that accounts for the arrow of time, with re-
spect to their ability to rank important nodes in this dynamic setting.
The centrality rankings use only connectivity information (who Tweeted
whom, when), but if we post-process the results by examining account
details, we nd that the time-respecting, dynamic, approach, which looks
at the follow-on
ow of information, is less likely to be `misled' by ac-
counts that appear to generate large numbers of automatic Tweets with
the aim of pushing out web links. We then benchmark these algorith-
mically derived rankings against independent feedback from ve social
media experts who judge Twitter accounts as part of their professional
duties. We nd that the dynamic centrality measures add value to the
expert view, and indeed can be hard to distinguish from an expert in
terms of who they place in the top ten. We also highlight areas where
the algorithmic approach can be rened and improved.
Original languageEnglish
Publication statusPublished - 5 Dec 2012
EventSocInfo 2012 - Lausanne, Switzerland
Duration: 5 Dec 20127 Dec 2012


ConferenceSocInfo 2012


  • dynamic targeting
  • online social medium
  • twitter interaction networks
  • topic-based


Dive into the research topics of 'Dynamic targeting in an online social medium'. Together they form a unique fingerprint.

Cite this