Volume 11 - Volume 11
Detecting Spam Bots on Social Network
Abstract
Bots have made an appearance on social media in a variety of ways. Twitter, for instance, has been
particularly hard hit, with bots accounting for a shockingly large number of its users. These bots are
used for nefarious purposes such as disseminating false information about politicians and inflating
celebrity expectations. Furthermore, these bots have the potential to skew the results of
conventional social media research. With the multiple increases in the size, speed, and style of user
knowledge in online social networks, new methods of grouping and evaluating such massive
knowledge are being explored. Getting rid of malicious social bots from a social media site is
crucial. The most widely used methods for identifying fraudulent social bots focus on the
quantitative measures of their actions. Social bots simply mimic these choices, leading to a low level
of study accuracy. Transformation clickstream sequences and semi-supervised clustering were used
to develop a new technique for detecting malicious social bots. This method considers not only the
probability of user activity clickstreams being moved, but also the behavior's time characteristic.
The detection accuracy for various kinds of malware social bots by the detection technique assisted
transfer probability of user activity clickstreams will increase by a mean of 12.8 percent, as per
results from our research on real online social network sites, compared to the detection method
funded estimate of user behaviour.
Paper Details
PaperID: 1719
Author's Name: A. Gnanasekar, T. Thangam, S. Afraah Mariam, K. Deepika and S. Dhivya Shree
Volume: Volume 11
Issues: Volume 11
Keywords: Detecting Spam Bots on Social Network
Year: 2021
Month: April
Pages: 850-860