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Deep Semantic Frame-Based Deceptive Opinion Spam Analysis

Seongsoon Kim, Hyeokyoon Chang, Seongwoon Lee, Minhwan Yu, Jaewoo Kang
2015 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15  
In this paper, we propose a frame-based deep semantic analysis method for understanding rich characteristics of deceptive and truthful opinions written by various types of individuals including crowdsourcing  ...  Existing work on opinion spam detection focuses mainly on linguistic features such as n-grams, syntactic patterns, or LIWC. However, deep semantic analysis remains largely unstudied.  ...  Frame-based Semantic Analysis Frame-by-frame Analysis The goal of our analysis is to understand how frames are distributed in different datasets.  ... 
doi:10.1145/2806416.2806551 dblp:conf/cikm/KimCLYK15 fatcat:mzsq6siduzchno5fgirtfdmxj4

Constructing and Evaluating a Novel Crowdsourcing-based Paraphrased Opinion Spam Dataset

Seongsoon Kim, Seongwoon Lee, Donghyeon Park, Jaewoo Kang
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
We believe that our new deceptive opinion spam dataset 1 will help advance opinion spam research.  ...  The classification experiments and semantic analysis results show that our POPS dataset most linguistically and semantically resembles truthful reviews.  ...  FRAME-BASED SEMANTIC ANALYSIS In this section, using the semantic frame-based analysis method, we will investigate how the internal composition of our POPS dataset may differ from that of other datasets  ... 
doi:10.1145/3038912.3052607 dblp:conf/www/KimLPK17 fatcat:zqh6awgxezasri7epaqh6aojle

Deceptive Opinion Spam Detection Using Neural Network

Yafeng Ren, Yue Zhang
2016 International Conference on Computational Linguistics  
Deceptive opinion spam detection has attracted significant attention from both business and research communities.  ...  Finally, the document representation is used directly as features to identify deceptive opinion spam.  ...  Kim et al. (2015) introduced a frame-based semantic feature based on FrameNet. Experimental results show that semantic frame features can improve the classification accuracy.  ... 
dblp:conf/coling/RenZ16 fatcat:lvaodn7l6zbrbhvgvredeexf5i

Composite Sequential Modeling for Identifying Fake Reviews

Rupal Bhargava, Anushka Baoni, Yashvardhan Sharma
2018 Journal of Intelligent Systems  
This paper presents a comprehensive analysis and comparison of various proposed sequential models based on different deep networks such as the convolutional neural network, long short-term memory, and  ...  The different sequential models are analyzed based on the number of layers, the number of output dimensions, order, and the combination of different deep network architectures.  ...  This article presents an experimental study and its analysis on the variants of sequential models based on the deep network architecture.  ... 
doi:10.1515/jisys-2017-0501 fatcat:bot26uvkqnazvczd4vjoe5tmxi

Review spam detector with rating consistency check

Kuldeep Sharma, King-Ip Lin
2013 Proceedings of the 51st ACM Southeast Conference on - ACMSE '13  
Consequently, websites containing customer reviews are becoming targets of opinion spam.  ...  This paper aims to detect spam reviews by users. Characteristics of the review will be identified based on previous research, plus a new featurerating consistency check.  ...  To obtain a deeper understanding of nature of deceptive opinion spam, researchers have three potentially complementary framings of the problem.  ... 
doi:10.1145/2498328.2500083 dblp:conf/ACMse/SharmaL13 fatcat:nil5pmafx5e5ppnpxgicpav5qe

Content Noise Detection Model Using Deep Learning in Web Forums

Jiyoung Woo, Jaeseok Yun
2020 Sustainability  
In this regard, as the importance of a web post is evaluated in terms of the number of involved authors, noise distorts the analysis results by adding unnecessary data to the opinion analysis.  ...  Spam posts in web forum discussions cause user inconvenience and lower the value of the web forum as an open source of user opinion.  ...  Ren and Ji also proposed a combined model of CNN and RNN for deceptive opinion spam detection [26] .  ... 
doi:10.3390/su12125074 fatcat:tnzi6qarlncjpcevtwuup54ivm

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Md Rafiqul Islam, Shaowu Liu, Xianzhi Wang, Guandong Xu
2020 Social Network Analysis and Mining  
However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation.  ...  We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve  ...  Generative model for detecting misinformation Over the last few decades, online social media platforms have become the main target space of deceptive opinions where deceptive opinions (such as rumor, spam  ... 
doi:10.1007/s13278-020-00696-x pmid:33014173 pmcid:PMC7524036 fatcat:473ziygl7jffbhwvpav3hlmppu

Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets

Saleh Nagi Alsubari, Sachin N. Deshmukh, Mosleh Hmoud Al-Adhaileh, Fawaz Waselalla Alsaade, Theyazn H. H. Aldhyani, Fahd Abd Algalil
2021 Applied Bionics and Biomechanics  
Furthermore, comparative analysis of the results of in-domain experiments with existing approaches has been done based on accuracy metric and, it is observed that the proposed model outperformed the compared  ...  For an in-domain experiment, the model is applied on each dataset individually, while in the case of a cross-domain experiment, all datasets are gathered and put into a single data frame and evaluated  ...  Ren and Ji [22] have proposed a hyper deep learning model that is consisted of a gated recurrent neural network and convolutional neural network (GRNN-CNN) for detecting deceptive opinion spam on indomain  ... 
doi:10.1155/2021/5522574 pmid:33953796 pmcid:PMC8062208 fatcat:7yonl2sbfbgkbgu2azzpd4onqu

Leveraging Transfer learning techniques- BERT, RoBERTa, ALBERT and DistilBERT for Fake Review Detection

Priyanka Gupta, Shriya Gandhi, Bharathi Raja Chakravarthi
2021 Forum for Information Retrieval Evaluation  
On similar lines, Guo et al. (2021) proposed a Deep Graph neural network-based Spammer detection (DeG-Spam) model.  ...  To overcome this problem, researchers prefer using machine learning or deep learning-based models for spam detection.  ... 
doi:10.1145/3503162.3503169 fatcat:3wz5iyx4qbd4pmq3vc4hbb4lee

Research on False Review Detection Methods: A state-of-the-art review

Arvind Mewada, Rupesh Kumar Dewang
2021 Journal of King Saud University: Computer and Information Sciences  
We used "Review Spam Detection", "Fake Opinion Analysis", "Deceptive Reviews Detection", "Opinion Spam Detection", "Fake Reviews Detection", "Spam Review Detection", "Review Spammer", and "Social Media  ...  Sentence-level Semantic Analysis: -Sentence-level semantic analysis is mainly divided into two parts: shallow semantic analysis and deep semantic analysis.  ... 
doi:10.1016/j.jksuci.2021.07.021 fatcat:7em4xwgwejavjg4nrqo4mzfd4y

Online Social Deception and Its Countermeasures for Trustworthy Cyberspace: A Survey [article]

Zhen Guo, Jin-Hee Cho, Ing-Ray Chen, Srijan Sengupta, Michin Hong, Tanushree Mitra
2020 arXiv   pre-print
Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons from existing literature.  ...  As a consequence, online social deception (OSD) in SNSs has emerged as a serious threat in cyberspace, particularly for users vulnerable to such cyberattacks.  ...  The semantic analysis method may ignore the hidden messages and background knowledge. In addition, the model requires tuning many input parameters. 5.2.3 Sentiment-based Deception Detection.  ... 
arXiv:2004.07678v1 fatcat:k4a6siywefb6lhkmyn67lmoqwe

Combining deep learning and argumentative reasoning for the analysis of social media textual content using small datasets

Oana Cocarascu, Francesca Toni
2018 Computational Linguistics  
Concretely, we define a deep learning method for Relation-based Argument Mining to extract argumentative relations of attack and support.  ...  We define a deep learning architecture based on a Long-Short Term Memory (LSTM) model (Hochreiter and Schmidhuber 1997) to determine relations of attack, support, and neither attack nor support between  ...  of the reviews (i.e. positive deceptive opinions and negative deceptive opinions).  ... 
doi:10.1162/coli_a_00338 fatcat:f3ob3onxsbat5krgdz5mkv52uq

Survey on Astroturfing Detection and Analysis from an Information Technology Perspective

Tong Chen, Jiqiang Liu, Yalun Wu, Yunzhe Tian, Endong Tong, Wenjia Niu, Yike Li, Yingxiao Xiang, Wei Wang, Zhe-Li Liu
2021 Security and Communication Networks  
we restudy it mainly from the perspective of information technology, summarize the latest research findings of astroturfing detection, analyze the astroturfing feature, classify the machine learning-based  ...  [4] , fake review [6] , spam (social) [8, 9] , and link framing [13] .  ...  [39] proposed a multi-iterative graph-based opinion spam detection (MGSD), which can be regarded as a graph-based model.  ... 
doi:10.1155/2021/3294610 fatcat:j3x3c5c6zfh6rcphaebenq6u3e

Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors

Xuepeng Wang, Kang Liu, Jun Zhao
2017 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
Solving the cold-start problem in review spam detection is an urgent and significant task.  ...  This paper proposes a novel neural network model to detect review spam for the cold-start problem, by learning to represent the new reviewers' review with jointly embedded textual and behavioral information  ...  Li et al. (2014a) proposed a positive-unlabeled learning method based on unigrams and bigrams; Kim et al. (2015) carried out a frame-based deep semantic analysis.  ... 
doi:10.18653/v1/p17-1034 dblp:conf/acl/WangLZ17 fatcat:n7x5tp5afnhohbjzagvoig3yu4

Online Social Deception and Its Countermeasures: A Survey

Zhen Guo, Jin-Hee Cho, Ing-Ray Chen, Srijan Sengupta, Michin Hong, Tanushree Mitra
2020 IEEE Access  
Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons learned from the existing literature.  ...  As a consequence, online social deception (OSD) in SNSs has emerged as a serious threat in cyberspace, particularly for users vulnerable to such cyberattacks.  ...  The semantic analysis methods may ignore hidden messages and background knowledge and require tuning many input parameters, which leads to high complexity and labor-intensive. 3) SENTIMENT-BASED DECEPTION  ... 
doi:10.1109/access.2020.3047337 fatcat:xw2rr2sjnrdf3nk4vfuowrkizy
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