| Abstract |
Facebook has become a major communication
channel for internet users. Unfortunately, with its great popularity
and a great number of users, spams are also increasing. A number
of Facebook services do not require spam detection, whereas
the group usage does. Group users are generally those who are
interested in the same topics or purposes. Members usually share
the contents of interest in the group. These characteristics enable
detection of unwanted posts, referred to as spam that annoys
others. It should be noted that some spam may jeopardize the
group, for example, by malicious URLs. The objective of this
article is to present the design concept for detecting spam in
closed groups by using the combination of text features and
social features, which comprised 11 features for classifying spam
by applying Random Forest machine learning algorithm on
1,200 labeled posts. The result indicated 98% of spam detection
efficiency. Additionally, from the feature importance, the number
of likes, one of the social features, was found to be the most
effective for spam detection. |
Publication
Journal Publication
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