Abstract
Online social media has become of great importance in the recent past. It has proven to be a medium for connectivity as well as effortless publicity. Given the vast scope of activities that can be performed with the help of social media, it can also be misused. An anomaly has no concrete definition but the best description of an anomaly would be to identify it as the one that exhibits abnormal behavior. In this paper two events have been taken into consideration, namely black lives matter and demonetization, that took great popularity over social media platforms in recent years and received mixed emotions and behavioural patterns from the public. The response to these events have been observed on twitter and an anomaly in the trend has been predicted in the course of a selected time frame using a proposed system model to accomplish the process efficiently. The deep learning model uses a Gated Recurrent Unit classifier along with a domain ontology to enhance the process of prediction. A comparative graph of other unsupervised training models have also been included to display the efficiency accomplished through the system designed. The overall accuracy of the trained system for demonetization is observed to be 95.43% and black lives matter is seen to be 96.18%.
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Manaswini, S., Deepak, G., Santhanavijayan, A. (2021). Knowledge Driven Paradigm for Anomaly Detection from Tweets Using Gated Recurrent Units. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_14
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