BOT SPAMMER DETECTION IN TWITTER USING TWEET SIMILARITY AND TIME INTERVAL ENTROPY
Abstract: The popularity of
Twitter has attracted spammers to disseminate large amount of spam messages.
Preliminary studies had shown that most spam messages were produced
automatically by bot. Therefore bot spammer detection can reduce the number of
spam messages in Twitter significantly. However, to the best of our knowledge,
few researches have focused in detecting Twitter bot spammer. Thus, this paper
proposes a novel approach to differentiate between bot spammer and legitimate
user accounts using time interval entropy and tweet similarity. Timestamp
collections are utilized to calculate the time interval entropy of each user.
Uni-gram matching-based similarity will be used to calculate tweet similarity.
Datasets are crawled from Twitter containing both normal and spammer accounts.
Experimental results showed that legitimate user may exhibit regular behavior
in posting tweet as bot spammer. Several legitimate users are also detected to
post similar tweets. Therefore it is less optimal to detect bot spammer using
one of those features only. However, combination of both features gives better
classification result. Precision, recall, and f-measure of the proposed method
reached 85,71%, 94,74% and 90% respectively. It outperforms precision, recall,
and f-measure of method which only uses either time interval entropy or tweet
similarity.
Author: Rizal Setya Perdana,
Tri Hadiah Muliawati, Reddy Alexandro
Journal Code: jptkomputergg150004
