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A Method of Machine Learning for Social Bot Detection Combined with Sentiment Analysis

Published: 06 March 2023 Publication History

Abstract

Social Bot exists widely in major social networks. Some maliciously use a social bot to guide public opinion, steal user privacy, and create rumors, which seriously affects the security of social networks. Past approaches mainly extracted large amounts of contents but ignored bots’ text sentiment features, and it is hard to detect social bot just based on contents. This paper proposes a malicious social bot detection method that combines sentiment features in response to this problem. It trains a Bidirectional Long Short-Term Memory model(Bi-LSTM) with an Attention Mechanism to perform sentiment calculation on the online text information of social accounts and analyze the sentiment fluctuations of accounts to get the new sentiment features; Then, it inputs the new features combined with metadata features into different machine learning models for analysis and comparison. Through this method, different machine learning detection models have improved the detection accuracy after combining sentiment features.

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cover image ACM Other conferences
MLNLP '22: Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing
December 2022
406 pages
ISBN:9781450399067
DOI:10.1145/3578741
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 March 2023

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  • (2024)Detection of Sarcasm in Urdu Tweets Using Deep Learning and Transformer Based Hybrid ApproachesIEEE Access10.1109/ACCESS.2024.339385612(61542-61555)Online publication date: 2024

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