Twitter Content-Based Spam Filtering

International Journal of Scientific Research in Science, Engineering and Technology, 2019

The spreading and learning of new discoveries and information is made available using current online social networks. In Recent days, the solutions may be irrelevant to the actual content; also termed as attacks in the layman’s term such attacks are been performed on Twitter as well and called as Twitter spammers. The quality of data is being compromised by addition of malicious and harmful information using URL, bio, emoticons, audio, images/videos & hash-tags through different accounts by exchanging tweets, personal messages (Direct Message’s) & re-tweets. Misleading sites may be linked with the malicious links which may affect adverse effects on the user and also interfere in their decision making processes. To improve user-experience from the spammers attacks, the training twitter dataset are applied and then by extracting and using the 12 lightweight features like user’s age, number of followers, count of tweets and re-tweets, etc. are used to distinguish the spam from non-spam. For enhancing the performance, the discretization of the function is important for transmission of spam detection between tweets. Our system creates classification model for Spam detection which includes binary classification and automatic learning algorithms viz. Naïve Bayes classifier or Support Vector Machine classifier which understands the behaviour of the model. The system will categorize the tweets from datasets into Spam and Non-spam classes and provide the user’s feed with only the relevant information. The system will report the impact of data-related factors such as relationship between spam and non-spam tweets, size of training dataset, data sampling and detection performance. The proposed system’s function is detection and analysis of the simple and variable twitter spam over time. The spam detection is a major challenge for the system and shortens the gap between performance appraisals and focuses primarily on data, features and patterns to identify real user and informing it about the spam tweets along with the performance statistics. The work is to detect spammed tweets in real time, since the new tweets may show patterns and this will help for training and updating dataset and in knowledge base.

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

In today's world humans have the ability to connect and communicate through social media independent of time or location. Since past two decades social media and internet has made a tremendous change in digital platform. Those were the old days where letters were written and posted. As technology is evolving digital media has changed the game over years, now there is Gmail, twitter, linked-in, what's-app, Instagram, telegram and many such applications which connects the world from one end to other where there is exchange of knowledge human to human connection and communication where association and exchange of ideas has become convenient and uncomplicated. Like everything created has its own pros and cons the digital media has its own. The improper use of social media has been misleading the bona fide users, because there is lot of personal and confidential information being shared on digital media such as twitter, Instagram and so on. The information is being hacked or leaked by the spammers. These spammers hack the genuine users account and post illegal information and abusive comments. To avoid this a methodology has been put forth which can be done by algorithms using machine learning. The performances of this model are measured using recall and F measure techniques. To detect spam we use Naïve bayes classification algorithm and support vector machine algorithm.

2020

Machine learning is a branch of artificial intelligence concerned with the creation and study of systems that can learn from data. A machine learning system could be trained to distinguish between spam and non-spam (ham) tweets on the Twitter platform. I aim to employ a text categorization technique to classify content on tweets.

2015

Social spam produces a great amount of noise on social media services such as Twitter, which reduces the signal-tonoise ratio that both end users and data mining applications observe. Existing techniques on social spam detection have focused primarily on the identification of spam accounts by using extensive historical and network-based data. In this paper we focus on the detection of spam tweets, which optimises the amount of data that needs to be gathered by relying only on tweet-inherent features. This enables the application of the spam detection system to a large set of tweets in a timely fashion, potentially applicable in a realtime or near real-time setting. Using two large hand-labelled datasets of tweets containing spam, we study the suitability of five classification algorithms and four di↵erent feature sets to the social spam detection task. Our results show that, by using the limited set of features readily available in a tweet, we can achieve encouraging results which are competitive when compared against existing spammer detection systems that make use of additional, costly user features. Our study is the first that attempts at generalising conclusions on the optimal classifiers and sets of features for social spam detection over di↵erent datasets.

Social Networking sites have become popular in recent years, among these sites Twitter is one of the fastest growing site. It plays a dual role of Online Social Networking (OSN) and Micro Blogging. Spammers invade twitter trending topics (popular topics discussed by Twitter user) to pollute the useful content. Social spamming is more successful compared to email spamming by using social relationship between the users. Spam detection is important because Twitter is mainly used for commercial advertisement and spammers invade the privacy information of the user and also the reputation of the user is damaged. Spammers can be detected using content and user based attributes. Traditional classifiers are required for spam detection. This paper focuses on study of detecting spam in twitter.

Social network sites involve billions of users around the world wide. User interactions with these social sites, like twitter have tremendous and occasionally undesirable impact implications for daily life. The major social networking sites have become a target platform for spammers to disperse a large amount of irrelevant and fundamental in detecting and defending cyber-attacks. Twitter, it has become one of the most extravagant platforms of all time and, most popular microblogging services which is generally used to share unreasonable amount of spam. Fake users send unwanted tweets to users to promote services or websites that do not only affect legitimate users, but also interrupt resource consumption. Furthermore, the possibility of expanding invalid information to users through false identities has increased, resulting in malicious content. Recently, the detection of spammers and the identification of fake users and fake tweets on Twitter has become an important area of researc...

Lecture Notes in Computer Science, 2017

Despite several benefits to modern communities and businesses, Twitter has attracted many spammers overwhelming legitimate users with unwanted and disruptive advertising and fake information. Detecting spammers is always challenging because there is a huge volume of data that needs to be analyzed while at the mean time spammers continue learning and changing their ways to avoid being detected by anti-spammer systems. Several spam classification systems are proposed using various features extracted from the content and user's information from their Tweets. Nevertheless, no comprehensive study has been done to compare and evaluate the effectiveness and efficiency of these systems. It is not known what the best anti-spammer system is and why. This paper proposes an evaluation framework that allows researchers, developers, and practitioners to access existing user-based and content-based features, implement their own features, and evaluate the performance of their systems against other systems. Our framework helps identify the most effective and efficient spammer detection features, evaluate the impact of using different numbers of recent tweets, and therefore obtaining a faster and more accurate classifier model.

Mukt Shabd Journal, 2022

Twitter is one of the most popular microblogging services, which generally used to share messages restricted to 280characters. However, its open nature and large user base are frequently exploited by automated spammers, content polluters, and other illintended users to commit various cyber crimes.Accordingly, number of approaches have been proposed by researchers to address these problems.Here in this approach we use three classifiers namely random forest, decision tree and Bayesian network .Community based features are determined to be the most effective for spam detection.

The issue of spam in various Social Networking Sites has become very trivial these days. There are various users in the outside world who have turned into spammers in order to earn their benefits. So, to deal with the problem of spam, there arises techniques for Spam Classification through which a user can take preventive measures beforehand. This paper gives insight about various spam classification techniques like Naïve Bayes, Support Vector Machine, URL analysis etc. Also, this paper focuses on spam classification techniques used mainly for Twitter as Twitter is considered one of the vulnerable and sensitive Social Networking platform.

Neurocomputing, 2018

Online Social Media platforms, such as Facebook and Twitter, enable all users, independently of their characteristics, to freely generate and consume huge amounts of data. While this data is being exploited by individuals and organisations to gain competitive advantage, a substantial amount of data is being generated by spam or fake users. One in every 200 social media messages and one in every 21 tweets is estimated to be spam. The rapid growth in the volume of global spam is expected to compromise research works that use social media data, thereby questioning data credibility. Motivated by the need to identify and filter out spam contents in social media data, this study presents a novel approach for distinguishing spam vs. non-spam social media posts and offers more insight into the behaviour of spam users on Twitter. The approach proposes an optimised set of features independent of historical tweets, which are only available for a short time on Twitter. We take into account features related to the users of Twitter, their accounts and their pairwise engagement with each other. We experimentally demonstrate the efficacy and robustness of our approach and compare it to a typical feature set for spam detection in the literature, achieving a significant improvement on performance. In contrast to prior research findings, we observe that an average automated spam account posted at least 12 tweets per day at well defined periods. Our method is suitable for real-time deployment in a social media data collection pipeline as an initial preprocessing strategy to improve the validity of research data.