Most existing techniques for spam detection on Twitter aim to identify and block users who post spam tweets. In this paper, we propose a semi-supervised spam detection (S3D) framework for spam detection at tweet-level. The proposed framework consists of two main modules: spam detection module operating in real-time mode and model update module operating in batch mode. The spam detection module consists of four lightweight detectors: 1) blacklisted domain detector to label tweets containing blacklisted URLs; 2) near-duplicate detector to label tweets that are near-duplicates of confidently prelabeled tweets; 3) reliable ham detector to label tweets that are posted by trusted users and that do not contain spammy words; and 4) multiclassifier-based detector labels the remaining tweets. The information required by the detection module is updated in batch mode based on the tweets that are labeled in the previous time window. Experiments on a large-scale data set show that the framework adaptively learns patterns of new spam activities and maintain good accuracy for spam detection in a tweet stream.
Removing spam users cannot filter every spam message as spammer may create another account and restart spamming activity. This calls for tweet-level spam detection. Inspired from content-based techniques for emails, @cite_6 utilized standard classifiers to detect spam tweets. Language modeling approach has been used to compute the divergence of trending topic, suspicious message, and title of the page linked in the tweet @cite_16 . Similarly, Castillo al @cite_12 analyzed the credibility of tweets on trending topics based on users' tweeting and retweeting behaviors, tweet content and link present in the tweets. As spammers keep on evolving over time, hence semi-supervised approach is suitable for tracking such changing spamming activities. Semi-supervised spam detection approach has been utilized to identify spam on voice-over-IP call @cite_17 . Similarly, a semi-supervised approach is reported to have better performance than supervised approach for malware detection task @cite_1 . However, to the best of our knowledge semi-supervised approach has not be utilized to detect spam tweets. Our proposed method is capable of continuously updating itself by using semi-supervised approach.
@cite_1 @cite_6 @cite_16 @cite_12 @cite_17
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In this paper, we propose a novel semi-supervised approach for detecting profanity-related offensive content in Twitter. Our approach exploits linguistic regularities in profane language via statistical topic modeling on a huge Twitter corpus, and detects offensive tweets using automatically these generated features. Our approach performs competitively with a variety of machine learning (ML) algorithms. For instance, our approach achieves a true positive rate (TP) of 75.1 over 4029 testing tweets using Logistic Regression, significantly outperforming the popular keyword matching baseline, which has a TP of 69.7 , while keeping the false positive rate (FP) at the same level as the baseline at about 3.77 . Our approach provides an alternative to large scale hand annotation efforts required by fully supervised learning approaches. Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody's lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34K trending topics and 20million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-of-the-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts. With millions of users tweeting around the world, real time search systems and dierent types of mining tools are emerging to allow people tracking the repercussion of events and news on Twitter. However, although appealing as mechanisms to ease the spread of news and allow users to discuss events and post their status, these services open opportunities for new forms of spam. Trending topics, the most talked about items on Twitter at a given point in time, have been seen as an opportunity to generate trac and revenue. Spammers post tweets containing typical words of a trending topic and URLs, usually obfuscated by URL shorteners, that lead users to completely unrelated websites. This kind of spam can contribute to de-value real time search services unless mechanisms to fight and stop spammers can be found. In this paper we consider the problem of detecting spammers on Twitter. We first collected a large dataset of Twitter that includes more than 54 million users, 1.9 billion links, and almost 1.8 billion tweets. Using tweets related to three famous trending topics from 2009, we construct a large labeled collection of users, manually classified into spammers and non-spammers. We then identify a number of characteristics related to tweet content and user social behavior, which could potentially be used to detect spammers. We used these characteristics as attributes of machine learning process for classifying users as either spammers or nonspammers. Our strategy succeeds at detecting much of the spammers while only a small percentage of non-spammers are misclassified. Approximately 70 of spammers and 96 of non-spammers were correctly classified. Our results also highlight the most important attributes for spam detection on Twitter. The rapidly growing social network Twitter has been infiltrated by large amount of spam. In this paper, a spam detection prototype system is proposed to identify suspicious users on Twitter. A directed social graph model is proposed to explore the “follower” and “friend” relationships among Twitter. Based on Twitter's spam policy, novel content-based features and graph-based features are also proposed to facilitate spam detection. A Web crawler is developed relying on API methods provided by Twitter. Around 25K users, 500K tweets, and 49M follower friend relationships in total are collected from public available data on Twitter. Bayesian classification algorithm is applied to distinguish the suspicious behaviors from normal ones. I analyze the data set and evaluate the performance of the detection system. Classic evaluation metrics are used to compare the performance of various traditional classification methods. Experiment results show that the Bayesian classifier has the best overall performance in term of F-measure. The trained classifier is also applied to the entire data set. The result shows that the spam detection system can achieve 89 precision.