Twitter(推特)是一个社交网络及微博客服务的网站。它利用无线网络,有线网络,通信技术,进行即时通讯,是微博客的典型应用。

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主题: TIMME-Twitter Ideology-detection via Multi-task Multi-relational Embedding

摘要: 跨平台帐户匹配在社交网络分析中起着重要作用,并且有利于广泛的应用。但是,现有方法要么严重依赖高质量的用户生成内容(包括用户配置文件),要么遭受数据不足的问题为了解决这一问题,我们提出了一种新颖的框架,该框架同时考虑了本地网络结构和超图结构上的多级图卷积。所提出的方法克服了现有工作的数据不足的问题,并且不必依赖于用户人口统计信息。此外,为了使所提出的方法能够处理大规模社交网络,我们提出了一种两阶段空间调节机制,以在基于网络分区的并行训练和不同社交网络上的帐户匹配中对齐嵌入空间。在两个大型的现实生活社交网络上进行了广泛的实验。实验结果表明,所提出的方法在很大程度上优于最新模型。

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Many online comments on social media platforms are hateful, humorous, or sarcastic. The sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments, which leads to misinterpretations by the existing sentiment analysis models. A lot of research has already been done to detect sarcasm in the text using user-based, topical, and conversational information but not much work has been done to use inter-sentence contextual information for detecting the same. This paper proposes a new state-of-the-art deep learning architecture that uses a novel Bidirectional Inter-Sentence Contextual Attention mechanism (Bi-ISCA) to capture inter-sentence dependencies for detecting sarcasm in the user-generated short text using only the conversational context. The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words & phrases responsible for invoking sarcasm. Bi-ISCA generates state-of-the-art results on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter). To the best of our knowledge, none of the existing state-of-the-art models use an inter-sentence contextual attention mechanism to detect sarcasm in the user-generated short text using only conversational context.

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