You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: Malware is any kind of computer software potentially harmful to both computers and networks. The amount of malware is increasing every year and poses a serious global security threat. Signature-based detection is the most widely used commercial antivirus method, however, it consistently fails to detect new malware. Supervised machine-learning models have been used to solve this issue, but the usefulness of supervised learning is far to be perfect because it requires that a significant amount of malicious code and benign software to be identified and labelled beforehand. In this paper, we propose a new method of malware protection that adopts a semi-supervised learning approach to detect unknown malware. This method is designed to build a machine-learning classifier using a set of labelled (malware and legitimate software) and unlabelled instances.We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used, while maintaining high accuracy rates.

[2]: Twitter has become one of the most used social networks. And, as happens with every popular media, it is prone to misuse. In this context, spam in Twitter has emerged in the last years, becoming an important problem for the users. In the last years, several approaches have appeared that are able to determine whether an user is a spammer or not. However, these blacklisting systems cannot filter every spam message and a spammer may create another account and restart sending spam. In this paper, we propose a content-based approach to filter spam tweets. We have used the text in the tweet and machine learning and compression algorithms to filter those undesired tweets.

[3]: 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.

[4]: We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally. On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to "trending" topics, and classify them as credible or not credible, based on features extracted from them. We use features from users' posting and re-posting ("re-tweeting") behavior, from the text of the posts, and from citations to external sources. We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70 to 80 .

[5]: In this paper, we present an approach for detection of spam calls over IP telephony called SPIT in VoIP systems. SPIT detection is different from spam detection in email in that the process has to be soft real-time, fewer features are available for examination due to the difficulty of mining voice traffic at runtime, and similarity in signaling traffic between legitimate and malicious callers. Our approach differs from existing work in its adaptability to new environments without the need for laborious and error-prone manual parameter configuration. We use clustering based on the call parameters, using optional user feedback for some calls, which they mark as SPIT or non-SPIT. We improve on a popular algorithm for semi-supervised learning, called MPCK-Means, to make it scalable to a large number of calls and operate at runtime. Our evaluation on captured call traces shows a fifteen fold reduction in computation time, with improvement in detection accuracy.
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, [2] 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 [1]6 . Similarly, Castillo al [1]2 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 [1]7 . Similarly, a semi-supervised approach is reported to have better performance than supervised approach for malware detection task [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.