A Selfie is Worth a Thousand Words: Mining Personal Patterns behind User Selfie-posting Behaviours

Privacy violations in online social networks (OSNs) often arise as a result of users sharing information with unintended audiences. One reason for this is that although OSN capa- bilities for creating and managing social groups can make it easier to be selective about recipients of a given post, they do not provide enough guidance to the users to make informed sharing decisions. We present Privacy Dynamics, an adaptive architecture that learns privacy norms for different audience groups based on users' sharing behaviours. Our architecture is underpinned by a formal model inspired by social identity theory, a social psychology framework for analysing group processes and intergroup relations. Our formal model comprises two main concepts, the group membership as a Social Identity (SI) map and privacy norms as a set of conflict rules. In our approach a privacy norm is specified in terms of the information objects that should be prevented from flowing between two conflicting social identity groups. We implement our formal model by using inductive logic programming (ILP), which automatically learns privacy norms. We evaluate the performance of our learning approach using synthesised data representing the sharing behaviour of social network users.

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Data & Analytics

A Selfie is Worth a Thousand Words
Mining Personal Patterns behind User Selfie‐posting Behaviours
Tianlang Chen, Yuxiao Chen, Jiebo Luo
University of Rochester

1) Posting selfies on social media
platforms is extremely popular in
the social media era
2) As a typical online social
behaviour, people's selfie‐
posting behaviour is influenced
by people's interest, personality,
preference, activity and tendency,
and it can in turn reflect and
predict these personal patterns
Our goal in this paper:
Exploring the interrelation between the selfie‐
posting behaviour and other personal patterns

Framework of this paper:
1) Unsupervised WeChat Image Classification
2) User Characterization
3) Selfie‐posting Behaviour (SPB) Prediction By User Feature
4) Personal Patterns Extraction and Visualization
5) Correlation Between High‐level Person Attributes and SPB
6) High‐level Person Attribute Prediction By SPB
Overall Framework

Unsupervised Image Classification
Methodology Performance

Unsupervised Image Classification
Category names and numbers
Using Silhouette Coefficient
to determine a proper
cluster number of K‐means
Evaluation accuracy is 88.5%

Unsupervised Image Classification
Pearson correlation
between categories

User Charactorization
• User‘s frequency feature (F)
– A 46‐dimensional vector that reflects the frequency of
a user to post different categories other than selfie
• User‘s inertia feature (I)
– Reflect user's tendency to upload images of a given
category in a Moment
• User‘s singleness feature (S)
– Reflect user's tendency to upload images of a single
category in a given Moment

Unsupervised Image Classification
Names/numbers of
selfie subcategories
Performance
Four advanced selfie
tendency definition
• Group selfie tendency
• Outdoor selfie tendency
• Holding an object selfie tendency
• Face mask selfie tendency

Selfie‐posting Behaviour Prediction
Basic selfie‐posting behaviour prediction
Advanced selfie tendency prediction
Using Text information

Personal Patterns Extraction and Visualization
Comparison between selfie‐posting addict
and selfie‐posting nonaddict

Personal Patterns Extraction and Visualization
Comparison between “a series of selfie”
lover and “just one selfie” lover

Personal Patterns Extraction and Visualization
Comparison between “It’s all selfies’ lover
and “selfie and more” lover

Personal Patterns Extraction and Visualization
Comparison of different sets of users with
special selfie tendencies

Correlation Between High‐level Personal
Attributes and SPB
• High‐level Person Attributes Extraction
– We cluster users into five types by Non‐negative Matrix Factorization
(NMF)
– For each user, we extract six high‐level attributes based on the
clustering result
Six attributes and the categories included

Correlation Between High‐level Person
Attributes and SPB
• User Type Description based on High‐level Person Attributes
– For a user, the value of an attribute is defined as the sum of frequency
of all categories this attribute includes
– For a user type, the value of an attribute is defined as the mean
attribute value of all users this user type includes
Radar plots of attributes of five user types clustered by NMF

Correlation Between High‐level Person
Attributes and SPB
Mean values of seven selfie‐posting measures among five user types
Ranking of high‐level attributes for selfie‐posting behaviours

High‐level Person Attribute Prediction
Using user selfie‐posting behaviours feature to predict
their high‐level attributes
selfie‐posting behaviour has the potential to indicate
whether a user has preference toward certain attributes

Thanks
Q & A
Visual Intelligence & Social Multimedia Analytics

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