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摘要: 以微博为代表的社交媒体在为公众提供信息共享平台的同时, 也为谣言提供了可乘之机.开展微博中谣言的识别和清理方法研究, 对维护社会的安全稳定有着重要的现实意义.本文针对新浪微博平台中谣言识别的问题, 提出了一种基于评论异常度的微博谣言识别方法.首先采用D-S理论实现微博评论异常度的计算方法; 然后利用评论异常度与微博的内容特征、传播特征、用户特征对微博进行抽象表示; 最后再利用SVM (Support vector machine)构建一个基于评论异常度的谣言识别模型, 实现对新浪微博中谣言微博的识别.实验表明, 本文提出的谣言识别模型对新浪微博中谣言识别具有较好的效果, 谣言微博识别的F1值达到了96.2 %, 相较于现有文献的最好结果提高了1.3 %.Abstract: Microblog plays an important role in social network service, while providing an information communication platform for users, it also provides a loophole for rumors. It is of great practical significance to automatically detect and clean up rumors in microblogs for the security and stability of society. In this paper, a rumor detecting method based on comment abnormality is presented. Firstly, we use D-S theory to implement the calculation method of comment abnormality. And then, we combine the comment abnormality, text features, propagation features and user characteristics to abstractly represent Sina Weibo. Finally, we use SVM (Support vector machine) to build a rumor detecting model based on comment abnormality. The experimental results show that the rumor detecting model proposed can effectively improve the detecting performance. And the F-measure of the rumor detecting is up to 96.2 %, which is up by 1.3 % compared with the best value in other literatures.
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Key words:
- Rumor detecting /
- Sina Weibo /
- comment abnormality /
- D-S theory /
- SVM
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图 1 谣言微博与普通微博的评论数对比
Fig. 1 Comparison of the number of comments between rumor Weibo and ordinary Weibos
图 2 谣言微博与普通微博的评论文本对比
Fig. 2 Comparison of the comment texts between rumor Weibo and ordinary Weibo
图 4 用户的普通微博与谣言微博平均评论数对比
Fig. 4 Comparison of the average number of comments between rumor Weibo and ordinary Weibo for some users
图 10 谣言微博与普通微博评论异常度分布对比
Fig. 10 Comparison of the comment abnormality distribution between rumor Weibos and ordinary Weibos
表 1 微博谣言识别基础特征体系
Table 1 The basic feature system of Weibo rumor detecting
特征种类 特征名称 特征描述 微博内容特征 $Length$ 微博文本的长度 $Has\_Multimedia$ 微博文本中是否含有多媒体信息, 如图片、视频和外部链接 $Emotion\_Tendency$ 微博的情感倾向, 分为正向情感和负向情感 $Number\_of\_@$ 微博文本中的@数量 $Number\_of\_topics$ 微博文本参与的话题数量 微博传播特征 $Time\_Span$ 微博发布时间与用户注册时间的间隔天数 $Client\_Type$ 发布微博的客户端类型, 包括移动客户端和非移动客户端 $Participation$ 网民参与度, 评论数和转发数两者之和与评论数、转发数和点赞数三者之和的比值 微博用户特征 $Has\_Verify$ 用户是否为认证用户 $Has\_Description$ 用户是否有自述信息 $Influence$ 用户影响力, 用户粉丝数与用户粉丝数和关注数两者之和的比值 $Register\_Time$ 用户的注册时间 $Number\_of\_blogs$ 用户的微博数量 
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表 2 谣言微博评论的区分性词集
Table 2 The identified word sets of rumor Weibo comments
类别 词集 $Zwords$ 造谣, 举报, 谣言, 辟谣, 不实, 传谣 $Gwords$ 不是, [蜡烛], 国家, 政府, [微笑], 知道, 真相, 新闻, 脑子, 真的, 祈祷, 呵呵, 智障, 没有, 恶心, 消息, 是不是, 口德, 真是, 相信, 素质, 打死, 事实, 智商, 抵制, 他妈的, 怒骂, 证据, 老百姓, [吃惊], 新浪, 不要脸, 证实, 脑残, [拜拜], 垃圾, 可怕, 小心, 尼玛, 传播, 暴力, 难道, 神经病, 法律, 公道, 记者, 媒体, 赶紧, 去死吧, 真假, 可能, 删除, 网警, 乱说, 不信, 打脸, 假新闻, 眼球, 国人, 键盘, 官方, 人性, 理智, 良心, 明显, 所谓, 民众, 不用, 无辜, 底线, 言论, 该死, 肯定, 水军, 真的假, 遭报应, 有意思, 侮辱, 生命, 央视, 闭嘴, 活该, 愤怒, 确定, 喷子, [怒], 煽动, 真实, 常识, 骂人, 缺德, 鄙视, 无知, 不删
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表 3 $CE$、$CK$、$CN$的取值范围
Table 3 The range of values of $CE$, $CK$ and $CN$
证据 取值范围 $ CE$ [$-0.530$, 0.782] $ CK$ [0, 0.178] $ CN$ [$-7.245$, 8.231]
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表 4 不同特征的准确率比较
Table 4 Comparison of accuracies of different features
序号 特征 准确率 1 $Length$ 0.513 2 $Has\_Multimedia$ 0.627 3 $Emotion\_Tendency$ 0.601 4 $Number\_of\_@$ 0.543 5 $Number\_of\_topics$ 0.515 6 $Time\_Span$ 0.633 7 $Client\_Type$ 0.645 8 $Participation$ 0.563 9 $Has\_Verify$ 0.671 10 $Has\_Description$ 0.532 11 $Influence$ 0.513 12 $Register\_Time$ 0.639 13 $Number\_of\_blogs$ 0.703 14 $Comment\_Abnormality$ 0.831
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表 5 不同特征集合的组合情况
Table 5 Combination of different feature sets
对照实验 特征组合描述 基本特征组合 内容特征+传播特征+用户特征 特征组合1 基本特征组合+评论情感异常度 特征组合2 基本特征组合+评论情感异常度+评论用词异常度+评论数目异常度 特征组合3 基本特征组合+微博评论异常度
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表 6 不同特征集合的实验结果对比
Table 6 Comparison of experimental results of different feature sets
特征组合 准确率 召回率 F1值 基本特征组合 0.868 0.913 0.890 特征组合1 0.902 0.930 0.916 特征组合2 0.928 0.937 0.933 特征组合3 0.954 0.971 0.962
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