Classification of Twitter Accounts into Targeting Accounts and Non-Targeting Accounts
Classification of Twitter Accounts into Targeting Accounts and Non-Targeting Accounts
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DOI：
10.1145/2914586.2914639
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Hikaru Takemura;Keishi Tajima
中科院分区：
文献类型：
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作者：
Hikaru Takemura;Keishi Tajima
在本文中，我们提出了一种方法，将Twitter帐户分类为非目标帐户，向公众发布消息，和目标帐户，向特定的人发布消息。例如，发布一般新闻信息的账户是非定向账户，而向特定组织的成员发布公告的账户是定向账户。一个发布非常具体的小主题信息的账户，以及一个用于与朋友交流的账户，也是针对账户。我们的方法找到了给定账户的大多数关注者所共有的一些属性，并计算具有这种一致性的用户集与来自给定用户范围的随机样本偏离了多少（例如，某个国家的所有Twitter用户的集合）。如果它偏离很大，则该帐户是目标帐户。我们使用两种类型的追随者属性：（1）他们的元数据中的术语和（2）他们的追随者。我们的实验结果表明，我们的方法之一，计算两个分数的基础上，这两种类型的属性，并结合使用SVM，达到准确率0.944，并优于基线。
In this paper, we propose a method for classifying Twitter accounts into non-targeting accounts, which post messages to the general public, and targeting accounts, which post messages to specific people. For example, an account posting general news information is a non-targeting account, while an account posting announcements to members of a specific organization is a targeting account. An account posting information on very specific minor topic, and an account used for communication with friends, are also targeting accounts. Our method finds some properties that are common to most followers of a given account, and calculate how much a user set with such a consistency deviates from a random sample from the given universe of users (e.g., the set of all Twitter users in some country). If it largely deviates, the account is a targeting account. We use two types of properties of followers: (1) terms in their metadata and (2) their followees. The result of our experiment shows that one of our methods, which computes two scores based on these two types of properties and combines them using SVM, achieves the accuracy 0.944, and outperforms the baselines.