Social Media Analytics with a Pinch of Semantics

Using semantics to solve problems (not solving problems of semantics).

SM for businesses:

• Analytics.
• How to measure success?

SM silos impeding progress.
In-house social platforms increasing, so even more so.

SIOC to integrate online community information.
SIOC + FOAF + SKOS.

FB Graph.
People are likeaholics.  Their 'likes' become meaningless, so you need to take this into account when making recommendations.
Browse your data and understand user actions.

Behaviour analysis.

Bottom-up analysis.
Can handle unexpected or emerging behaviours.

• Community members classified into roles.
• Identify unknown roles.
• Cope with role changes over time.
• Clustering to identify emerging roles.

eg. focussed novice; mixed novice; distributed expert; ...
Spectrum across users you can or can't do without.

Extending an ontology built on SIOC.

Encoding rules in ontologies with SPIN.

Three categories of features:

• Social features (people you follow, people follow you, ...)
• Content features (what you're posting, keywords, ...)
• Topical/semantic features

Which behaviour categories you need to cater for more than others?  How roles impact activity in online community.

Consistently see that you need some sort of stable mixture of behaviours for activities in forums to increase.

==> Don't know what's causing which.

What is a healthy community?

Use behaviour analysis to guess what's going to happen to community. Eg.

• Churn rate.
• User count.
• Seeds/non-seeds prop (how many / if people reply to you).
• Clustering.

Unexpected: the fewer focused experts in the community, the more posts received a reply.
(But quality of answers?)

Community types (Little work in this space)

Muller, M. (CHI 2012) community types in IBM Connections:

• Communities of Practice
• Teams
• Technical support
• ..
• .. (see slides..)

Need an ontology and inference engine of community types.
Wants an automated process to tell you what type of community it is - it might be something it wasn't set up for.
Then you could determine what sort of patterns you would expect to find.
Noone has done this yet.

Measurements of value and satisfaction

Answers different across communities.  They ran it on IBM Connections - corporate community.

Most of this work is for managers of communities - see what's happening and help to predict what might be coming next.

Can classify users based on Maslow's Hierarchy of Needs?
Mapping the hierarchy to social media communities.
~90% users happily staying at the lower levels of the 'needs hierarchy'.

Behaviour evolution patterns

What paths they follow over time.
eg. people who become moderators eventually.

Engagement analysis

What's the best way to write a tweet so that people care about it?
Which posts are likely to generate more attention?

Getting bored of people finding patterns in individual datasets.  What can be generalised to other communities?

So experimented with 7 datasets and looked at how results differed across:

• community types.
• randomness (vs. topicality) of datasets.
• related experiments.

And people use different features.

Semantic sentiment analysis in social media

Too much research going on, especially on twitter.

Extract semantic concepts from tweets; likely sentiment for a concept.
Tweetnator.
Semantics increases accuracy by 6.5% for negative sentiment; 4.8% for positive sentiment.

OUSocial.
Students don't use in-house networks because they already use facebook groups etc. Want to analyse what's happening on them.

Upcoming

Reel Lives (inc. Ed.)
Fragmented digital selves.
Want to automate compilations of media (photos, messages) posted online.

Changing energy consumption behaviour.
Providing information is not enough.

Social Eco feedback technology.