In online social networks (OSNs), detection of malicious social bots is an important research challenge to provide legitimacy of user profiles and trustworthy service ratings. Further, spam-influential users must be minimal to control the fake information-spread in OSNs. Learning from example patterns using supervised learning may not provide accurate results in cases where existing data items are biased and bot behavior dynamically changes over a period of time. Moreover, deep reinforcement learning provides improved learning by repeated interactions with the environment. However, a typical deep reinforcement leaning algorithm converges slower to find an optimal sequence of actions to reach out a goal state. In this article, we design a particle swarm optimization (PSO) based deep Q-learning algorithm for detecting social spam bots by integrating PSO with Q -value function. In addition, spam-influential users are identified using the proposed spam influence minimization model and it helps in restricting the flow of illegitimate tweets in Twitter network. Further, an influential community detection algorithm has been proposed to reduce the spreading of spam content through influential communities in Twitter network. Experimental results illustrate the efficacy of our proposed algorithms by considering two Twitter datasets and performance metrics such as precision, recall, and modularity.

中文翻译：

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用于检测 Twitter 网络中社交垃圾邮件机器人和垃圾邮件影响用户的深度强化学习的粒子群优化

在在线社交网络 (OSN) 中，检测恶意社交机器人是提供用户配置文件合法性和可信赖服务评级的重要研究挑战。此外，影响垃圾邮件的用户必须最少，以控制 OSN 中的虚假信息传播。在现有数据项存在偏差且机器人行为在一段时间内动态变化的情况下，使用监督学习从示例模式中学习可能无法提供准确的结果。此外，深度强化学习通过与环境的重复交互提供改进的学习。然而，典型的深度强化学习算法收敛速度较慢，无法找到达到目标状态的最佳动作序列。在本文中，问 -值函数。此外，使用提议的垃圾邮件影响最小化模型识别垃圾邮件影响用户，它有助于限制 Twitter 网络中非法推文的流动。此外，还提出了一种有影响力的社区检测算法，以减少垃圾邮件内容通过 Twitter 网络中的有影响力社区的传播。实验结果通过考虑两个 Twitter 数据集和性能指标（例如精度、召回率和模块化）来说明我们提出的算法的有效性。