Dynamic feature selection for spam detection (1).pptx
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1) The document proposes a dynamic feature selection technique for classifying spam users on Twitter that uses different feature sets for different user groups rather than a static feature set. 2) Data is collected using a custom crawler to gather user information and features like user attributes, content attributes, and social network features are extracted. 3) Machine learning algorithms like k-NN and SVM are applied to the feature sets to classify users as spam or not spam based on their group.
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[IJET V2I4P9] Authors: Praveen Jayasankar , Prashanth Jayaraman ,Rachel HannahIJET - International Journal of Engineering and Techniques
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As we know cyber bullying is increasing day by day and Cyber troll is one of the cyber-aggressive actions that is not much different from cyberbullying in online abuse so that the victims feel uncomfortable. One of the most used social media platforms in which cyber trolling frequently happens is Twitter. Basically, it is found that during an investigation of cyberbullying cases a lot of information gathered is false which aims to give discomfort, hatred and waste lots of time. So, it is necessary to classify between cyberbullying tweets and normal tweets on twitter. There has already been research on classification of cyberbullying tweets and normal tweets using the Support vector machine (SVM) algorithm. But the drawback of the system is that it only gives 63.83% of accuracy. Firstly, we can improve the accuracy of the system by using the Recurrent Neural Network (RNN) And Secondly, for balancing the dataset we will be using Synthetic Minority Over-sampling Technique (SMOTE). We believe that using these techniques we will be able to increase the accuracy of the previous proposed.
Classification of instagram fake users using supervised machine learning algo...
On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users.
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FRAMEWORK FOR ANALYZING TWITTER TO DETECT COMMUNITY SUSPICIOUS CRIME ACTIVITY
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IRJET- Big Data Driven Information Diffusion Analytics and Control on Social ...
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EXPLORATORY DATA ANALYSIS AND FEATURE SELECTION FOR SOCIAL MEDIA HACKERS PRED...
In machine learning, the intelligence of a developed model is greatly influenced by the dataset used for the
target domain on which the developed model will be deployed. Social media platform has experienced
more of hackers’ attacks on the platform in recent time. To identify a hacker on the platform, there are two
possible ways. The first is to use the activities of the user while the second is to use the supplied details the
user registered the account with. To adequately identify a social media user as hacker proactively, there
are relevant user details called features that can be used to determine whether a social media user is a
hacker or not. In this paper, an exploratory data analysis was carried out to determine the best features
that can be used by a predictive model to proactively identify hackers on the social media platform. A web
crawler was developed to mine the user dataset on which exploratory data analysis was carried out to
select the best features for the dataset which could be used to correctly identify a hacker on a social media
platform.
Exploratory Data Analysis and Feature Selection for Social Media Hackers Pred...
In machine learning, the intelligence of a developed model is greatly influenced by the dataset used for the
target domain on which the developed model will be deployed. Social media platform has experienced
more of hackers’ attacks on the platform in recent time. To identify a hacker on the platform, there are two
possible ways. The first is to use the activities of the user while the second is to use the supplied details the
user registered the account with. To adequately identify a social media user as hacker proactively, there
are relevant user details called features that can be used to determine whether a social media user is a
hacker or not. In this paper, an exploratory data analysis was carried out to determine the best features
that can be used by a predictive model to proactively identify hackers on the social media platform. A web
crawler was developed to mine the user dataset on which exploratory data analysis was carried out to
select the best features for the dataset which could be used to correctly identify a hacker on a social media
platform.
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[IJET V2I4P9] Authors: Praveen Jayasankar , Prashanth Jayaraman ,Rachel Hannah
[IJET V2I4P9] Authors: Praveen Jayasankar , Prashanth Jayaraman ,Rachel HannahIJET - International Journal of Engineering and Techniques
Sentiment Analysis is the process of finding the sentiments from different classes of words.
Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with
respect to some topic or the overall contextual polarity of a document. The attitude may be his or
her judgment or evaluation, affective state, or the intended emotional communication. In this case,
‘tweets’! Given a micro-blogging platform where official, verified tweets are available to us, we
need to identify the sentiments of those tweets. A model must be constructed where the sentiments
are scored, for each product individually and then they are compared with, diagrammatically,
portraying users’ feedback from the producers stand point.
There are many websites that offer a comparison between various products or services based on
certain features of the article such as its predominant traits, price, and its welcome in the market and
so on. However not many provide a juxtaposing of commodities with user review as the focal point.
Those few that do work with Naïve Bayes Machine Learning Algorithms, that poses a disadvantage
as it mandatorily assumes that the features, in our project, words, are independent of each other.
This is a comparatively inefficient method of performing Sentiment Analysis on bulk text, for
official purposes, since sentences will not give the meaning they are supposed to convey, if each
word is considered a separate entity. Maximum Entropy Classifier overcomes this draw back by
limiting the assumptions it makes of the input data feed, which is what we use in the proposed
system.IRJET - Detection of Drug Abuse using Social Media Mining
This document discusses a study that aims to detect drug abuse patterns by mining social media data, specifically from Instagram. The study scrapes Instagram posts containing drug-related hashtags and analyzes the text and images. Text analysis identifies frequent drug slang and collocations between hashtags. Posts are filtered based on hashtags to collect information. A list of drug-related hashtags is generated. The model aims to dynamically detect illegal drug posts through hashtags and learn drug user lingo to study behavioral patterns and identify other potentially involved user accounts and locations.
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Dynamic feature selection for spam detection (1).pptx
- 1. Dynamic feature selection for spam detection Rivika jain under the guidance of : Dr Faraz Ahmad 19cs26
- 2. Introduction Now-a-days people are generally using social networking sites for communicating with the other users and for sharing information across the world Twitter is one among several social networking sites which are expanding on daily basis. SPAM attacks are increasing in social media these days, and many social media users are exposed to these and similar SPAM attacks. Spammers have the intention of collecting personal information and attacking user profiles that they identify. These Attackers share SPAM content with malware links and expect users to install this software on their computers. There is a need to develop effective systems for detecting SPAM accounts and SPAM contents so that social networks can be cleaned and users can have a better experience
- 3. Proposal In this study, we group similar Twitter users and introduce a dynamic feature selection technique that uses different features for each user group instead of using a static feature set and apply machine learning algorithms to classify spam users on Twitter.
- 4. Methodology 1. Architecture ● Data collection ● Feature extraction ● Machine learning
- 5. Data extraction A CRAWLER has been developed using Twitter Rest and Streaming API to collect user information. This software enables to collect user data without being tied to the API provided by Twitter as the restrictions that the Twitter API imposes on the user are not suitable for use because it prevents data collection intensively. Before the data collection process starts, users were randomly selected from among users who shared about Twitter’s agenda items.
- 6. 2.Feature extraction Property Tool is to be created by making calculations on raw user data. While the features accommodated in the property pool have been determined,attention has been paid to the most commonly used features in the literature, which does not require very high costs. In this way, we are trying to establish a decision mechanism with high accuracy by keeping the necessary time and resources required for collecting and extracting features at a minimum level.
- 7. Feature Set User based features Content based features User age Link count for per tweet Total tweet count Average hashtag count Total followers count Average mention count Total followings count Average favorite count Tweet count for per age Average retweet count Follower count for per age Retweeted rate Followings count for per age Followers count for per tweet Total common followers count Average spam
- 8. Machine Learning The machine learning phase is the last phase in which the target user is made a decision as to whether it is a SPAM account. In this phase, users who are already grouped are tried to be classified with various classification algorithms together with dynamically determined properties for the group they are belonging to. In this study we are using k-NN, SVM classifying machine learning algorithms.
- 9. References 1. Fabricio B, Magno G, Rodrigues T, Almeida V (2010) Detecting spammers on Twitter, collaboration, electronic messaging, anti- abuse and spam conference (CEAS), vol 6. National Academy Press 2. Rashhid C, Nuriddin M, Mahmud GAN, Rashedur M (2013) A data mining based spam detection system for YouTube. In: Eighth international conference on digital information management, pp. 373–378 3. Sarita Y, Daniel R, Grant S, Danah B (2010) Detecting spam in a twitter network. Microsoft Res First Monday, 15(1) 4. Stafford G, Louis LY (2013) An evaluation of the effect of spam on twitter trending topics.IEEE, New York 5. Zhao Y, Zhaoxiang Z, Yungonh W, Liu J (2012) Robust mobile spamming detection via graph patterns. In: 21st international conference on pattern recognition.