1,763 research outputs found
BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology
This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software
Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis
This work has been partially funded by projects PID2020-113462RB-I00 (ANIMALICOS), granted by Ministerio Espanol de Economia y Competitividad; projects P18-RT-4830 and A-TIC-608-UGR20 granted by Junta de Andalucia, and project B-TIC-402-UGR18 (FEDER and Junta de Andalucia).During the recent COVID-19 pandemic, people were forced to stay at home to protect
their own and othersâ lives. As a result, remote technology is being considered more in all aspects
of life. One important example of this is online reviews, where the number of reviews increased
promptly in the last two years according to Statista and Rize reports. People started to depend more
on these reviews as a result of the mandatory physical distance employed in all countries. With no
one speaking to about products and services feedback. Reading and posting online reviews becomes
an important part of discussion and decision-making, especially for individuals and organizations.
However, the growth of online reviews usage also provoked an increase in spam reviews. Spam
reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit
or publicity. A number of spam detection methods have been proposed to solve this problem. As
part of this study, we outline the concepts and detection methods of spam reviews, along with
their implications in the environment of online reviews. The study addresses all the spam reviews
detection studies for the years 2020 and 2021. In other words, we analyze and examine all works
presented during the COVID-19 situation. Then, highlight the differences between the works before
and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine
different detection approaches have been classified in order to investigate their specific advantages,
limitations, and ways to improve their performance. Additionally, a literature analysis, discussion,
and future directions were also presented.Spanish Government PID2020-113462RB-I00Junta de Andalucia P18-RT-4830
A-TIC-608-UGR20
B-TIC-402-UGR18European Commission B-TIC-402-UGR1
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Malicious crowdsourcing forums are gaining traction as sources of spreading
misinformation online, but are limited by the costs of hiring and managing
human workers. In this paper, we identify a new class of attacks that leverage
deep learning language models (Recurrent Neural Networks or RNNs) to automate
the generation of fake online reviews for products and services. Not only are
these attacks cheap and therefore more scalable, but they can control rate of
content output to eliminate the signature burstiness that makes crowdsourced
campaigns easy to detect.
Using Yelp reviews as an example platform, we show how a two phased review
generation and customization attack can produce reviews that are
indistinguishable by state-of-the-art statistical detectors. We conduct a
survey-based user study to show these reviews not only evade human detection,
but also score high on "usefulness" metrics by users. Finally, we develop novel
automated defenses against these attacks, by leveraging the lossy
transformation introduced by the RNN training and generation cycle. We consider
countermeasures against our mechanisms, show that they produce unattractive
cost-benefit tradeoffs for attackers, and that they can be further curtailed by
simple constraints imposed by online service providers
Promotional Campaigns in the Era of Social Platforms
The rise of social media has facilitated the diffusion of information to more easily reach millions of users. While some users connect with friends and organically share information and opinions on social media, others have exploited these platforms to gain influence and profit through promotional campaigns and advertising. The existence of promotional campaigns contributes to the spread of misleading information, spam, and fake news. Thus, these campaigns affect the trustworthiness and reliability of social media and render it as a crowd advertising platform. This dissertation studies the existence of promotional campaigns in social media and explores different ways users and bots (i.e. automated accounts) engage in such campaigns. In this dissertation, we design a suite of detection, ranking, and mining techniques. We study user-generated reviews in online e-commerce sites, such as Google Play, to extract campaigns. We identify cooperating sets of bots and classify their interactions in social networks such as Twitter, and rank the bots based on the degree of their malevolence. Our study shows that modern online social interactions are largely modulated by promotional campaigns such as political campaigns, advertisement campaigns, and incentive-driven campaigns. We measure how these campaigns can potentially impact information consumption of millions of social media users
An Expert System Technique for Sentiment Analysis of Opinions
To help the users and the product owners it is quite necessary to extract aspects from the online reviews, their sentiment polarities, and associations between them. There is a great deal of work done in the field of sentiment analysis. Lexical and learning-based systems can be combined to separate the assessments from online opinions and reviews. In learning-based techniques, the Gaussian mixture model can be used for getting probabilistic results for polarities against aspects and naĂŻve baize classifiers for the problem of spam comments which produced better and competitive results against previous techniques
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