计算机科学 ›› 2019, Vol. 46 ›› Issue (11): 202-208.doi: 10.11896/jsjkx.180901617
韩晴晴, 张艳梅, 牛娃
HAN Qing-qing, ZHANG Yan-mei, NIU Wa
摘要: 在迅速发展的互联网时代,微博产生了大量的信息,但是在微博话题等地带存在着较多水军,水军在一定程度上影响了普通用户了解某人或者某事的真实情况。因此,为了高效、准确地识别水军,针对水军样本数量少、非水军样本数量庞大等问题,综合考虑使用半监督协同训练算法。该算法通过研究微博用户的多个特征并对其进行综合分析,重新定义了6个属性特征值,包括账户关注度、每日发表微博数、微博影响力等。依据算法的特点,将6个属性特征值分为两个属性集,每个属性集对应一个视图,每个视图利用Scikit-Learn 机器学习库中的7种分类方法训练出分类器,以对微博用户进行水军识别,最后在爬取的微博用户数据集上进行实验。实验结果表明,两个视图在分别使用朴素贝叶斯算法、逻辑回归算法训练分类器时,分类结果的准确率、召回率、精度和F1-measure值都较高。因此,综合分析微博用户特征并且使用符合实际情况的半监督协同训练算法,能够准确、高效、快速地识别微博水军。
中图分类号:
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