The basic problem in today’s On-line Social
Networks (OSNs) is to give users the ability to control the
messages posted on their own private space to avoid that
unwanted content is displayed. Up to now social networks
provide little support to this requirement. So in this paper, we
propose a system allowing OSN users to have a direct control
on the messages posted on their walls. We are using here a
flexible rule-based system, that allows users to customize the
filtering criteria to be applied to their walls, and a Machine
Learning based soft classifier automatically labeling messages
in support of content-based filtering.
Seema Kanhaiya : G.H.Raisoni Institute of Engineering & Technology, Pune
Savitribai Phule University of Pune, Dist-Pune, State-Maharashtra, India.
Zip-412207
Tejal Jadhav : G.H.Raisoni Institute of Engineering & Technology, Pune
Savitribai Phule University of Pune, Dist-Pune, State-Maharashtra, India.
Zip-412207
Ankita Patil : G.H.Raisoni Institute of Engineering & Technology, Pune
Savitribai Phule University of Pune, Dist-Pune, State-Maharashtra, India.
Zip-412207
Shilpa Birajdar : G.H.Raisoni Institute of Engineering & Technology, Pune
Savitribai Phule University of Pune, Dist-Pune, State-Maharashtra, India.
Zip-412207
On-line Social Networks
Filtered Wall
Machine Learning
Short Text Classifier
In this paper, we have presented a system to filter
undesired messages from OSN walls. The system
develops a ML soft classifier to implement customizable
content-dependent FRs. In particular, we aim at
investigating a tool able to automatically recommend
trust values for those contacts user does not individually
identified. We do consider that such a tool should
propose expectation assessment based on users
procedures, performances, and reputation in OSN, which
might involve enhancing OSN with assessment methods.
Though, the propose of these assessment based tools is
difficult by several concern s, like the suggestions an
assessment system might have on users’ confidentiality
and/or the restrictions on what it is possible to audit in
present OSNs. An introduction work in this direction has
been prepared in the context of expectation values used
for OSN access control purposes. We would like to
remark that the system proposed in this paper represents
just the core set of functionalities needed to provide a
sophisticated tool for OSN message filtering. Still if we
have balanced our system with an online associate to set
FR thresholds, the improvement of an absolute system
effortlessly exploitable by average OSN users is a wide
topic which is out of the scope of the present paper.
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