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Deep Semantic Frame-Based Deceptive Opinion Spam Analysis
2015
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15
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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
2017
Proceedings of the 26th International Conference on World Wide Web - WWW '17
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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
Composite Sequential Modeling for Identifying Fake Reviews
2018
Journal of Intelligent Systems
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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
2013
Proceedings of the 51st ACM Southeast Conference on - ACMSE '13
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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
2020
Sustainability
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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
2020
Social Network Analysis and Mining
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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
2021
Applied Bionics and Biomechanics
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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
Research on False Review Detection Methods: A state-of-the-art review
2021
Journal of King Saud University: Computer and Information Sciences
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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]
2020
arXiv
pre-print
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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
2018
Computational Linguistics
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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
2021
Security and Communication Networks
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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
2017
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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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
2020
IEEE Access
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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
Opinion Mining Analysis: A Framework
2019
Zenodo
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Opinion mining is a sort of natural language processing for tracing the emotional frame of mind of the public regarding a distinct or certain product. ...
Opinion mining is otherwise called sentiment analysis as they are utilized reciprocally. ...
Online opinions have very deep impact on the business of various e-commerce sites. ...
doi:10.5281/zenodo.3461312
fatcat:kxxlkxqhyrgltpmypynjepyzpq
Detecting Concealed Information in Text and Speech
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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deception. ...
We reveal subtle signs of concealed information in speech and text, compare, and contrast them with those in deception detection literature, thus uncovering the link between concealing information and ...
Ott et al. (2011) investigated online deceptive opinion spams by crowdsourcing a dataset of fake hotel reviews using Amazon Mechanical Turk, and found deceptive spams exhibit more positive emotions, first-person ...
doi:10.18653/v1/p19-1039
dblp:conf/acl/Hu19
fatcat:wfuhoeej6rehjmo2xgvt67orie
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