Online social networks, as popular media and communications tools with their own extensive uses, play key roles in public opinion polls, politics, economy, and even governance. An important issue regarding these networks is the use of multiple sources of publishing or re-publishing news and propositions that can influence audiences depending on the level of trust in these sources between users. Therefore, estimating the level of trust in social networks between users can predict the extent of social networks’ impact on news and different publication and re-publication sources, and correspondingly provide effective strategies in news dissemination, advertisements, and other diverse contents for trustees. Therefore, trust is introduced and interpreted in the present study. A large portion of interactions in social networks is based on sending and receiving texts employing natural language processing techniques. A Hidden Markov Model (HMM) was designed via an efficient model, namely SenseTrust, to estimate the level of trust between users in social networks.

中文翻译：

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SenseTrust：社交网络中基于情感的信任模型

在线社交网络作为具有广泛用途的流行媒体和通信工具，在民意调查、政治、经济甚至治理中发挥着关键作用。关于这些网络的一个重要问题是使用多个发布或重新发布新闻和主张的来源，这些来源可以根据用户之间对这些来源的信任程度影响受众。因此，估计用户之间对社交网络的信任程度可以预测社交网络对新闻和不同发布和再发布来源的影响程度，并相应地为受托人提供新闻传播、广告等多样化内容的有效策略。因此，在本研究中引入并解释了信任。社交网络中的很大一部分交互基于使用自然语言处理技术发送和接收文本。隐马尔可夫模型 (HMM) 是通过有效模型 SenseTrust 设计的，用于估计社交网络中用户之间的信任水平。