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首页> 外文会议>International Conference on Social, Cultural, and Behavioral Modeling Prediction and Behavior Representation in Modeling and Simulation >Finding Organizational Accounts Based on Structural and Behavioral Factors on Twitter
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Finding Organizational Accounts Based on Structural and Behavioral Factors on Twitter

机译：根据Twitter上的结构和行为因素查找组织账户

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摘要

Various socio-political organizations, from activist groups to propaganda campaigners, create accounts on Twitter to reach out, influence and gain followers. In order to analyze the impact of these organizational accounts, the first step is to identify them. In this paper, we develop and experiment with a set of network-based, behavioral, temporal and spatial characteristics in these accounts, independent of domain or language, to identify features that can be useful in detecting organizational accounts. In order to assess this model, we experimented with a microblog corpus comprised of over 7 million tweets from 150,000 Twitter users in Bangladesh, tweeted between June and October 2016. We sampled 31,139 accounts using cold-start heuristics to locate and label nearly 200 organizational accounts, distributed as 68 NGOs, 62 news outlets, 35 political groups, and 17 public intellectual and iconic figures. The remaining accounts were labeled as individuals. Next, we developed a set of features and experimented with a set of linear and non-linear classifiers. The highest performing sparse logistic regression classifier achieved an accuracy of 68.2% precision and 64.4% recall leading to a 66.2% F1-score in detecting less than 1% rare organizational accounts using a set of content- and language-independent features.
机译：从活动团体到宣传活动人员的各种社会政治组织，在Twitter上创建账户，以达到，影响和获得追随者。为了分析这些组织账户的影响，第一步是识别它们。在本文中，我们在这些账户中开发和实验这些基于网络，行为，时间和空间特征，独立于域或语言，以识别在检测组织账户方面可以有用的功能。为了评估该模型，我们尝试了由孟加拉国150,000名推特用户组成的微博语言，在2016年6月和10月之间推文。我们使用冷启动启发式算31,139个帐户来定位和标记近200个组织账户，分发为68个非政府组织，62个新闻网点，35个政治团体和17个公共知识分子和标志性人物。其余账户被标记为个人。接下来，我们开发了一组特征，并用一组线性和非线性分类器进行了实验。最高性能稀疏的逻辑回归分类器实现了68.2％精度的精度，64.4％的召回导致66.2％的F1分数使用一组内容和语言 - 独立的功能检测少于1％的稀有组织账户。

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