A Motif-Based Approach for Identifying Controversy
A Motif-Based Approach for Identifying Controversy
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基于主题的争议识别方法
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
C. Lucchese
中科院分区：
文献类型：
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作者：
Mauro Coletto;Venkata Rama Kiran Garimella;A. Gionis;C. Lucchese
在社交媒体上讨论的话题中，有些引发了争议。最近的一些研究主要关注社交媒体中争议的识别问题，这些研究大多基于文本内容的分析或依赖于全球网络结构。由于难以理解自然语言和调查全局网络结构，这些方法具有很强的局限性。在这项工作中，我们表明，通过利用网络母题，即用户交互的本地模式，可以检测社交媒体中的争议。所建议的方法允许对用户讨论及其随时间的演变进行独立于语言、细粒度和高效的计算分析。利用基序模式的监督模型可以达到85%的准确率，与基线结构，基于传播和时间网络特征相比，提高了7%。
Among the topics discussed in Social Media, some lead to controversy. A number of recent studies have focused on the problem of identifying controversy in social media mostly based on the analysis of textual content or rely on global network structure. Such approaches have strong limitations due to the difficulty of understanding natural language, and of investigating the global network structure. In this work we show that it is possible to detect controversy in social media by exploiting network motifs, that is, local patterns of user interaction. The proposed approach allows for a language-independent and fine-grained and efficient-to-compute analysis of user discussions and their evolution over time. The supervised model exploiting motif patterns can achieve 85% accuracy, with an improvement of 7% compared to baseline structural, propagation-based and temporal network features.