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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (9): 1-15    DOI: 10.11925/infotech.2096-3467.2018.0959
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Detecting Product Review Spam: A Survey
Jiafen Wu,Feicheng Ma()
Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This paper reviews current studies on fighting product review spam. [Coverage] We searched “review spam” with eight major scholarly databases (e.g., WoS, CNKI and EI, etc.), and retrieved a total of 90 relevant papers. [Methods] First, we adopted systematic review procedure to identify and categorize the methods detecting product review spam. Then, we compared the impacts of spam features on detection performance. [Results] The spam features and detection methods were the key issues in fighting product review spam. The acquisition of large-scale annotation data was a challenging task for current research. [Limitations] We did not examine the detection and classification methods for spammers. [Conclusions] This paper analyzes spam detection methods from the perspectives of data acquisition, spamming features and detection methods. It offers suggestions and directions for future research.

Key wordsReview Spam Detection      Review Spam      Spamming Features      Detection Methods     
Received: 31 August 2018      Published: 23 October 2019
ZTFLH:  TP391  

Cite this article:

Jiafen Wu,Feicheng Ma. Detecting Product Review Spam: A Survey. Data Analysis and Knowledge Discovery, 2019, 3(9): 1-15.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0959     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I9/1

序号 质量标准 判断方法 排除文献数
1 不是以产品虚假评论识别方法为研究主题的文献(例如: 社交媒体虚假评论、评论质量、评论效用、治理研究等) 阅读文献标题、摘要及关键字 54
2 没有提出完整识别方法、没有指明实验数据集、没有进行实证研究的文献(缺少以上内容无法进行特征、方法性能的对比) 精读文献 6
3 冗余文献(相同作者的研究发表在不同会议或期刊上, 选择其中最新最全的版本) 精读文献 10
总计 70
特征类型 特征 特征功能 提取方法
语言特征 词袋/n-grams 以向量形式表示文本, 计算评论文本的相似度 词法分析, 建立词袋模型或n-grams模型, 常用的有Unigram、Bigrams及Trigrams
词性(Part-of-Speech, POS) 捕捉真实和虚假评论的词性分布差异 词法分析, 进行词性标注及词频统计
LIWC 从语言学和心理学角度理解虚假评论文本 词法分析, 使用LIWC(Linguistic Inquiry and Word Count)工具的输出结果
句法结构 获取评论文本深层的句法结构特点 句法分析, 使用概率上下文无关文法(Probabilistic Context-Free Grammar, PCFG)或依存关系分析树
情感 捕捉虚假评论的极端情感倾向 语义分析, 情感分析
文本特征 文本表示 获取评论深层语义表示 语义分析, 通过建立词嵌入模型、深度学习模型进行文本表示
文体风格 获取评论者写作风格特点 词法和句法分析, 基于词汇和句法特征分析提取
元数据 从评论语言以外的角度获取评论、评论者
及评论对象的属性
基于评论正文以外的其他与评论相关的数据提取, 例如评论长度、评论时间、评分、评论ID、产品ID等
虚假行为 行为说明 虚假行为特征 量化方法 特征说明
复制评论
行为
为降低写虚假评论的时间代价, 虚假评论者倾向于复制自己或他人的评论, 致使评论内容的相似程度较高[14,15] 最大内容相似度[16]
(Maximum Content
Similarity, MCS)
${{f}_{MCS}}={{\max }_{{{r}_{i}},{{r}_{j}}\in {{R}_{a}},i<j}}\cos ({{r}_{i}},{{r}_{j}})$ 利用余弦距离计算评论者a发表的评论文本${{r}_{i}}$和${{r}_{j}}$间的相似度, 取最大值
异常评分
行为
虚假评论者通过给出与正常评论者不一致的、甚至极端的评分, 达到提高或拉低评论对象综合评分的目的[9] 极端评分[16]
(Extreme Rating, EXT)
${{f}_{EXT}}=\left\{ \begin{align} & 1,\text{*}({{r}_{a}},p({{r}_{a}}))\in \{1,5\} \\ & 0,\text{*}({{r}_{a}},p({{r}_{a}}))\in \{2,3,4\} \\ \end{align} \right.$ 判断评论者a是否总是对特定产品p给出极端评分。5星评价体中, 1星或5星为极端评分
积极评论比例[8]
(Percentage of Positive Reviews, PPR)
${{f}_{PPR}}=\frac{\left| \left\{ r\in {{R}_{a}}:\text{*}r\in \{4,5\} \right\} \right|}{\left| {{R}_{a}} \right|}$ 计算评论者a倾向于发布积极情感评论的程度。5星评价中, 评分≥4星的评论为积极评论
评分偏差[17]
(Rating Deviation, RD)
${{f}_{RD}}=\frac{\left| {{r}_{p,a}}-{{{\bar{r}}}_{p}} \right|}{4}$ 计算评论者a对某产品p的评分
rp,a与该产品平均评分${{\bar{r}}_{p}}$的不一
致程度。分母表示5星评价体系中的最大评分偏差为4
早期评论
行为
虚假评论者倾向于在产品刚上线时对其进行评论[14], 可以最大程度地影响后来消费者对产品的印象。 早期评论[16]
(Early Time Frame,
ETF)
${{f}_{ETF}}=\left\{ \begin{align} & 0,L(a,p)-A(p)>\delta \\ & 1-\frac{L(a,p)-A(p)}{\delta }, 其他 \end{align} \right.$
计算评论者a评论某产品p的早期程度。$L(a,p)$为最近评论时间, $A(p)$为产品上线时间, $\delta$为早期评论阈值, 估计值为7个月
头条评论比例[16]
(Ratio of First Review, RFR)
${{f}_{RFR}}=\frac{\left| \left\{ r\in {{R}_{a}}:r 是头条评论\right\} \right|}{\left| {{R}_{a}} \right|}$ 计算评论者a的所有评论中是头条评论的比例
突发评论
行为
虚假评论者在较短时间间隔内多次发表评论, 以达到快速影响评论对象综合评分、操纵评论情感倾向的目的[16, 18] 突发性发布[16]
(Reviewing Burstiness, RB)
${{f}_{RB}}=\left\{ \begin{align} & 0,L(a)-F(a)>\tau \\ & 1-\frac{L(a)-F(a)}{\tau }, 其他 \end{align} \right.$
计算评论者a首次评论时间$L(a)$和最近一次评论时间$F(a)$的接近程度, τ为突发期阈值, 估计值为20天
突发评论比例[17]
(Burst Review Ratio, BRR)
${{f}_{BRR}}=\frac{\left| {{B}_{\text{a}}} \right|}{\left| {{R}_{\text{a}}} \right|}$ 计算评论者a所有评论中是突发评论的比例。Ba为评论者a所有评论中处于评论突发期的评论集合
单例评论者比例[19]
(Singleton Ratio, SR)
${{f}_{SR}}=\frac{\left| \left\{ r\in {{R}_{p}}:r 是单例评论\right\} \right|}{\left| {{R}_{p}} \right|}$
单例评论者指只发表了一条评论的评论者, 其为虚假评论者的可能性高[18]Rp为产品p的评论集合
频繁评论
行为
正常评论者发布评论的时间和数量一般是随机的, 而虚假评论者由于执行特定的任务, 其评论时间和数量都有一定规律, 例如每天、每月都发表一定数量的评论[16] 最大日发布评论数[16]
(Maximum Number of Reviews, MNR)
${{f}_{MNR}}=\frac{MaxRev(a)}{{{\max }_{a\in A}}(MaxRev(a))}$ 计算评论者a单日内发表最大评论数MaxRev(a)与其他评论者单日内发表最大评论数的比值
评论发布频繁度[20]
(Posting Frequency, PF)
${{f}_{PF}}=\mathrm{Max}\{a({{u}_{i}})\left| a({{u}_{i}})=a{{c}_{j}}({{u}_{i}})-avg({{u}_{i}}) \right.\}$ 计算评论者每月实际评论数acj(ui)与平均评论数avg(ui)的最大差值
针对性评
论行为
正常评论者的评论对象通常是随机的, 而虚假评论者发表的评论则具有较强的针对性, 例如只对特定的产品、品牌或店铺进行评论[21] 品牌偏离分[10]
(Brand Deviation Score, BDS)
${{f}_{BDS}}=\text{H(X)}=\underset{i}{\overset{n}{\mathop \sum }}\,p({{x}_{i}})\mathrm{log}p({{x}_{i}})$ 计算评论者在不同品牌上评论数的分布差异。xi代表第i个品牌, p(xi)为第i个品牌被评论的概率, 采用熵值衡量
离群产品评论比例[22]
(Ratio of Outlier Products Reviewing, ROPR)
${{f}_{ROPR}}=\frac{\left| \left\{ r\in {{R}_{a}}:r 是离群产品评论\right\} \right|}{\left| {{R}_{a}} \right|}$ 离群产品指只被一个评论者评论的产品。该特征计算评论者a所有评论中是离群产品评论的比例
评论密度[21]包括产品类别密度(Category Density, CD)、店铺密度(Store Density, SD)和时间密度(Time Density, TD) ${{f}_{CD}}=\frac{numReview{{(c)}_{r}}}{\left| {{R}_{a}} \right|}$
${{f}_{SD}}=\frac{numReview{{(s)}_{r}}}{\left| {{R}_{a}} \right|}$
${{f}_{TD}}=\frac{numDays(a)}{\left| {{R}_{a}} \right|}$
计算评论者在特定产品类别、店铺或时间段上进行评论的密集程度
标注数据 方法 特征维度 代表性文献 识别效果 方法复杂度 说明
评论 评论者 关系
使用 LR [87, 93-94] 特征组合形式直观, 容易出现欠拟合
[9, 19, 56, 61]
SVM [7, 37, 39, 41-42, 50] 适用于处理高维特征空间问题, 分类性能高
[8, 49-52, 57]
RF [91] 适用于处理不平衡数据集问题, 减少过拟合
[26]
[89]
[25]
NB [63, 95-96] 模型简单、参数较少; 对数据稀疏性不敏感; 假设特征间是相互独立的, 但在实际问题中往往不成立
DT [21, 53] 模型计算简单, 可解释性强; 容易发生过拟合
MEM [38, 97] 约束条件设置灵活; 计算代价较大, 数据稀疏问题较严重
SAGE [12] 处理数据领域迁移能力较强
集成学习 [64, 88, 90] 提高分类稳定性和鲁棒性, 应对不平衡数据集问题
神经网络 [45-48] 自动学习文本特征, 无需人工设计特征; 需要大量参数, 学习时间较长
[28, 54, 56]
不使用 语义分析 [74-82] 虚假性判据简单, 误判率较高
聚类法 [67, 83-84] 聚类中簇的数目较难确定
MRF [33] 对网络结构有较强的拟合能力
[36]
时序分析 [18, 85] 可提高方法准确率和时间性能
部分使用 Co-training [69-70] 半监督方法, 适用于小数据集, 要求两个视图的特征具有独立性
[10, 32]
PU学习 [11, 71] 半监督方法, 适用于数据集包含少量正例及大量无标注数据的情况
[6]
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