ABSTRACT Political conversations have become a ubiquitous part of social media. When users interact and engage in discussions, there are usually two mediums available to them; textual conversations and platform-specific interactions such as like, share (Facebook) or retweet (Twitter). Major social media platforms do not facilitate users with negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Thus, detecting implicit negative links is an important and a challenging task. In this work, we propose an unsupervised framework utilising positive interactions, sentiment cues, and socially balanced triads for detecting implicit negative links. We also present an online variant of it for streaming data tasks. We show the effectiveness of both frameworks with experiments on two annotated datasets of politician Twitter accounts. Our experiments show that the proposed frameworks outperform other well-known and proposed baselines. To illustrate the detected implicit negative links' contribution, we compare the community detection accuracies using unsigned and signed networks. Experimental results using detected negative links show superiority on the three datasets where the camps are known a priori. We also present qualitative evaluations of polarisation patterns between communities which are only possible in the presence of negative links.

中文翻译：

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通过矩阵三因式对在线政治网络进行隐式负链接检测

摘要政治对话已成为社交媒体中无处不在的一部分。当用户互动和参与讨论时，通常有两种媒介可供他们使用；文本对话和特定于平台的交互，例如点赞、分享 (Facebook) 或转推 (Twitter)。主要社交媒体平台不会为用户提供负面交互选项。然而，许多政治网络分析任务不仅依赖于正关联，还依赖于负关联。因此，检测隐含的负面链接是一项重要且具有挑战性的任务。在这项工作中，我们提出了一个无监督框架，利用积极的互动、情感线索和社会平衡的三合会来检测隐含的消极联系。我们还为流式数据任务提供了它的在线变体。我们通过对政治家 Twitter 帐户的两个带注释的数据集的实验来展示这两个框架的有效性。我们的实验表明，所提出的框架优于其他众所周知的和提出的基线。为了说明检测到的隐式负链接的贡献，我们比较了使用无符号和有符号网络的社区检测精度。使用检测到的负链接的实验结果显示了在先验已知营地的三个数据集上的优越性。我们还对社区之间的极化模式进行了定性评估，这只有在存在负链接的情况下才有可能。贡献，我们比较了使用无符号和有符号网络的社区检测精度。使用检测到的负链接的实验结果显示了在先验已知营地的三个数据集上的优越性。我们还对社区之间的极化模式进行了定性评估，这只有在存在负链接的情况下才有可能。贡献，我们比较了使用无符号和有符号网络的社区检测精度。使用检测到的负链接的实验结果显示了在先验已知营地的三个数据集上的优越性。我们还对社区之间的极化模式进行了定性评估，这只有在存在负链接的情况下才有可能。