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.
Many social spam detection studies focus on the identification of spam accounts. Lee al @cite_13 analyzed and used features derived from user demographics, follower following social graph, tweet content and temporal aspect of user behavior to identify content polluters. Hu al exploited social graph and tweets of a user to detect spam detection on Twitter. They formulated spammer detection task as an optimization problem @cite_2 . Online learning has been utilized to tackle the fast evolving nature of spammer @cite_9 . They have utilized both content and network information and incrementally update their spam detection model for effective social spam detection. Tan al @cite_18 proposed an unsupervised spam detection system that exploits legitimate users in the social network. Their analysis shows the volatility of spamming patterns in social network. They have utilized non-spam patterns of legitimate users based on social graph and user-link graph to detect spam pattern. Gao al @cite_11 identified social spam by clustering posts based on text and URL similarities and detecting large clusters with bursty posting patterns.
@cite_18 @cite_9 @cite_2 @cite_13 @cite_11
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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. 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. To date, as one of the most popular online social networks (OSNs), Twitter is paying its dues as more and more spammers set their sights on this microblogging site. Twitter spammers can achieve their malicious goals such as sending spam, spreading malware, hosting botnet command and control (C&C) channels, and launching other underground illicit activities. Due to the significance and indispensability of detecting and suspending those spam accounts, many researchers along with the engineers at Twitter Inc. have devoted themselves to keeping Twitter as spam-free online communities. Most of the existing studies utilize machine learning techniques to detect Twitter spammers. “While the priest climbs a post, the devil climbs ten.” Twitter spammers are evolving to evade existing detection features. In this paper, we first make a comprehensive and empirical analysis of the evasion tactics utilized by Twitter spammers. We further design several new detection features to detect more Twitter spammers. In addition, to deeply understand the effectiveness and difficulties of using machine learning features to detect spammers, we analyze the robustness of 24 detection features that are commonly utilized in the literature as well as our proposed ones. Through our experiments, we show that our new designed features are much more effective to be used to detect (even evasive) Twitter spammers. According to our evaluation, while keeping an even lower false positive rate, the detection rate using our new feature set is also significantly higher than that of existing work. To the best of our knowledge, this work is the first empirical study and evaluation of the effect of evasion tactics utilized by Twitter spammers and is a valuable supplement to this line of research. The explosive use of social media also makes it a popular platform for malicious users, known as social spammers, to overwhelm normal users with unwanted content. One effective way for social spammer detection is to build a classifier based on content and social network information. However, social spammers are sophisticated and adaptable to game the system with fast evolving content and network patterns. First, social spammers continually change their spamming content patterns to avoid being detected. Second, reflexive reciprocity makes it easier for social spammers to establish social influence and pretend to be normal users by quickly accumulating a large number of "human" friends. It is challenging for existing anti-spamming systems based on batch-mode learning to quickly respond to newly emerging patterns for effective social spammer detection. In this paper, we present a general optimization framework to collectively use content and network information for social spammer detection, and provide the solution for efficient online processing. Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework.