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Statistical Features-Based Real-Time Detection of Drifted Twitter Spam
2017
IEEE Transactions on Information Forensics and Security
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Twitter Spam has become a critical problem nowadays. Recent works focus on applying machine learning techniques for Twitter spam detection, which make use of the statistical features of tweets. ...
In our labelled tweets dataset, however, we observe that the statistical properties of spam tweets vary over time, and thus the performance of existing machine learning based classifiers decreases. ...
ACKNOWLEDGMENT The authors would like to thank Trend Micro for providing us the service to label spam tweets. This work is supported by ARC Linkage Project LP120200266. ...
doi:10.1109/tifs.2016.2621888
fatcat:agvfrp6b7jepzpvctu6pwjz7cy
Statistical Detection of Online Drifting Twitter Spam
2016
Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security - ASIA CCS '16
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We observe existing machine learning based detection methods suffer from the problem of Twitter spam drift, i.e., the statistical properties of spam tweets vary over time. ...
Recent research works focus on applying machine learning techniques for Twitter spam detection, which make use of the statistical features of tweets. ...
The problem is that Twitter spam is drifting over time in the statistical feature space. ...
doi:10.1145/2897845.2897928
dblp:conf/ccs/Liu0X16
fatcat:rroswjq5drgs7imnoedrujr3ri
Advanced Characteristic Analysis of Real Time Junk Occurrences in Twitter
2018
International Journal of Computer Applications Technology and Research
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Spam on twitter is a major threat in recent days. To overcome these problems we take many steps to work on this. This work uses twitter as the input data source to address the problem of real-time. ...
In order to solve these problem, we firstly carry out a deep analysis on the statistical features of taking training sets of data to differentiate spam tweet and non-spam tweet. ...
The spam detection mechanism currently uses the email body only.
CONCLUSION In this paper, we firstly sympathize the "Spam Drift" problem in statistical features based Twitter spam detection. ...
doi:10.7753/ijcatr0712.1001
fatcat:3aknsycxardtbdvp2umy6wdja4
Drifted Twitter Spam Classification using Multiscale Detection Test on K-L Divergence
2019
IEEE Access
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INDEX TERMS Concept drift, drift detection test, twitter spam classification, K-L divergence. 108384 This work is licensed under a Creative Commons Attribution 4.0 License. ...
Twitter spam with illegal links may evolve over time in order to deceive filtering models, causing disastrous loss to both users and the whole network. ...
ACKNOWLEDGMENT The authors would like to thank the help of Mr. Zibo Yu, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China, in typesetting this paper. ...
doi:10.1109/access.2019.2932018
fatcat:wvxxqa563rcuvbrtlqn22trdze
A Deep Neural Network Technique for Detecting Real-Time Drifted Twitter Spam
2022
Applied Sciences
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Most of the recent research focuses on detecting spammers using statistical features. ...
However, such statistical features are changed over time, and spammers can defeat all detection systems by changing their behavior and using text paraphrasing. ...
Most of the described methods focus on detecting spam tweets based on some statistical features. ...
doi:10.3390/app12136407
fatcat:mmcb3a6655afpasqh2kvdiv3zi
Twitter Spam Detection: A Systematic Review
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2011.14754v1 fatcat:llkr4hjxofgxxfkkbl24lefena
Therefore, it raises a motivation to conduct a systematic review about different approaches of spam detection on Twitter. ...
Nowadays, with the rise of Internet access and mobile devices around the globe, more people are using social networks for collaboration and receiving real-time information. ...
Since spam tweets are drifting over time in the statistical feature scope, the performance of classifiers reduced. Thus, in order to address the Twitter spam drift problem, Liu, et al. ...
arXiv:2011.14754v2
fatcat:byldhhoffbhhldsxdj7siybsua
Trends Manipulation and Spam Detection in Twitter
2018
International Journal for Research in Applied Science and Engineering Technology
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It tries to detect, is a tweet is spam or not based on properties of the tweet like the presence of some keywords, types of hash tags present. ...
Data is collected using the live public tweets in real time, twitter trends are manipulated by using Naive Bayes classifier. ...
In [2] the author has discussed about the statistical features based on detection techniques. ...
doi:10.22214/ijraset.2018.4462
fatcat:kx7feup3onhqzgrmkjsjdo5plu
Detecting spam campaign in twitter with semantic similarity
2020
Journal of Physics, Conference Series
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Twitter is a widespread supply for real-time news distribution between individuals. ...
The number of solutions concentrate on detecting spam campaign based on URL only and ignoring text in a tweet. ...
Feature
Analysis
Blacklist
Twitter Spam Detection
Syntax
Analysis
Key
Segment
Tweet
content
Tweet
statistic
Info
Account
statistic
Info
campaign
statistic
Info
Figure 1. ...
doi:10.1088/1742-6596/1447/1/012044
fatcat:a73fmtla4ng7bpjotiqyicgjxe
A MULTI-CLASSIFIER APPROACH FOR TWITTER SPAM DETECTION USING INNOVATIVE ANN-FDT ALGORITHM
2020
Indian Journal of Computer Science and Engineering
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Twitter is using Google Safe-browsing to detect the spam URL and block spam links. ...
Due to the presence of advanced API which enables to read and write the data in Twitter, different kinds of spammers are attracted in the Twitter. ...
learning techniques. [9] For solving the issue of Twitter Spam Drift, a deep analysis was performed initially in the statistical features that were obtained of about one million spam and same amount of ...
doi:10.21817/indjcse/2020/v11i5/201105182
fatcat:doefk6463fgh3cid5x5qj3rx5m
Spam, a Digital Pollution and Ways to Eradicate It
2019
International Journal of Engineering and Advanced Technology
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We then compared all the methods present in the papers to see which method or combination of methods could give the best result in detecting spam. ...
This survey is thus mainly used to discuss and analyze the recent research that had been put forth regarding the spam detection in social media sites such as Twitter. ...
Most of the spam detecting methods use behavioral and statistical methods to detect spam, but they all have their limitations. ...
doi:10.35940/ijeat.b4107.129219
fatcat:uze7gfg3wrgjdmetvpuhzhl7p4
Cost-based heterogeneous learning framework for real-time spam detection in social networks with expert decisions
2021
IEEE Access
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The characteristics of Twitter spam change over time-a phenomenon called "spam drift" by Chen et al. [14, 42] . ...
Moreover, if the statistical information of spam tweets is similar to that of normal tweets, attacks cannot be detected only by statistical features. ...
Author Name: Preparation of Papers for IEEE Access (2021) VOLUME XX, 2017 ...
doi:10.1109/access.2021.3098799
fatcat:x7bv5dhmc5geplrh4z6rpb76uq
A Machine Learning Method for Spam Detection in Twitter using Naive Bayes and ERF Algorithms
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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To combat with the issue of spams, there has been a lot of methods available, Yet, there is not a perfect effective solution for detect the Twitter spams with the exactness. ...
Now a day the people are spending their most of the time in the online social media. ...
SYSTEM DESIGN The better framework for the detection of twitter spam namely, A Novel Twitter Spam Detection System is proposed. ...
doi:10.35940/ijitee.f4729.049620
fatcat:eghpqql7mzbqxmtym5yqeivjie
Blacklist Creation for Detecting Fake Accounts on Twitter
2018
International Journal of Networked and Distributed Computing (IJNDC)
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In this paper, fake accounts are detected using blacklist instead of traditional spam words list. Blacklist is created using topic modeling approach and keyword extraction approach. ...
We evaluate our blacklist based approach on 1KS-10KN dataset and Social Honeypot dataset and compared the accuracy with the traditional spam words list based approach. ...
FAKE ACCOUNTS DETECTION ON TWITTER In this section, fake accounts are detected using content-based features. Content-based features are extracted from text of the tweets posted by the user. ...
doi:10.2991/ijndc.1970.1.7.6
fatcat:jefrptidyrc4nm5u2rk4fywwkm
Detection of Malicious Data in Twitter Using Machine Learning Approaches
2021
Turkish Journal of Computer and Mathematics Education
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Due to data restrictions and communication categories, the current systems do not deserve an exact statistical classification for a piece of news. ...
We will study different research papers using various techniques for master training in the prediction and detection of malicious data on social networks online. ...
This model can detect spam over time. spam can be detected. Drift detection (MDD) and divergence of the KL have been used to identify the drifts. ...
doi:10.17762/turcomat.v12i3.2008
fatcat:ty3riinbx5ertaemi2niqfkywy
Spammer Detection and Fake User Identification on Social Networks
2019
IEEE Access
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Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending ...
Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. ...
The results of the study show that random forest classifier achieves high spam detection accuracy in real-time. Shen et al. [29] investigated issues of detecting spammers on Twitter. ...
doi:10.1109/access.2019.2918196
fatcat:jht2723d4zbwzj6exkc4yyjdke
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