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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Understanding the variations in trading price (volatility), and its response to external information is a well-studied topic in finance. In this study, we focus on volatility predictions for a relatively new asset class of cryptocurrencies (in particular, Bitcoin) using deep learning representations of public social media data from Twitter. For the field work, we extracted semantic information and user interaction statistics from over 30 million Bitcoin-related tweets, in conjunction with 15-minute intraday price data over a 144-day horizon. Using this data, we built several deep learning architectures that utilized a combination of the gathered information. For all architectures, we conducted ablation studies to assess the influence of each component and feature set in our model. We found statistical evidences for the hypotheses that: (i) temporal convolutional networks perform significantly better than both autoregressive and other deep learning-based models in the literature, and (ii) the tweet author meta-information, even detached from the tweet itself, is a better predictor than the semantic content and tweet volume statistics.

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