Intelligent Disaster Response via Social Media Analysis A Survey <s> Spam <s> With the increase in GPS-enabled devices, socialmedia sites, such as Twitter, are quickly becoming a primeoutlet for timely geo-spatial data. Such data can be leveragedto aid in emergency response planning and recovery operations. Unfortunately, the information overload poses significantdifficulty to the quick discovery and identification ofemergency situation areas. The system tackles this challengeby providing real-time mapping of influence areas based onautomatic analysis of the flow of discussion using languagedistributions. The workflow is then further enhanced throughthe addition of keyword surprise mapping which projects the general divergence map onto specific task-level keywords for precise and focused response. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Spam <s> Malicious users are a threat to many sites and defending against them demands innovative countermeasures. When malicious users join sites, they provide limited information about themselves. With this limited information, sites can find it difficult to distinguish between a malicious user and a normal user. In this study, we develop a methodology that identifies malicious users with limited information. As information provided by malicious users can vary, the proposed methodology utilizes minimum information to identify malicious users. It is shown that as little as 10 bits of information can help greatly in this challenging task. The experiments results verify that this methodology is effective in identifying malicious users in the realistic scenario of limited information availability. <s> BIB002 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Spam <s> The popularity of microblogging platforms, such as Twitter, makes them important for information dissemination and sharing. However, they are also recognized as ideal places by spammers to conduct social spamming. Massive social spammers and spam messages heavily hurt the user experience and hinder the healthy development of microblogging systems. Thus, effectively detecting the social spammers and spam messages in microblogging is of great value. Existing studies mainly regard social spammer detection and spam message detection as two separate tasks. However, social spammers and spam messages have strong connections, since social spammers tend to post more spam messages and spam messages have high probabilities to be posted by social spammers. Combining social spammer detection with spam message detection has the potential to boost the performance of each task. In this paper, we propose a unified framework for social spammer and spam message co-detection in microblogging. Our framework utilizes the posting relations between users and messages to combine social spammer detection with spam message detection. In addition, we extract the social relations between users as well as the connections between messages, and incorporate them into our framework as regularization terms over the prediction results. Besides, we introduce an efficient optimization method to solve our framework. Extensive experiments on a real-world microblog dataset demonstrate that our framework can significantly and consistently improve the performance of both social spammer detection and spam message detection. <s> BIB003
Spam is the "content designed to mislead or content that the sites legitimate users do not wish to receive  ." Spammers degrade the credibility of social media and are capable of deviating the discussions and affect the popularity of topics. An example of such manipulations is promoting the material for Scholastic Assessment Test (SAT) that was happening close to the occurrence of Hurricane Sandy. The wordcloud ( Figure 2 ) that has been generated using the surprise level of words BIB001 from Twitter data after this disaster shows #HaveToAceMySATExam as one of the keywords that stands out among thousands of others. Social media sites such as Twitter and Facebook actively remove who violates their regulations and specifically spammers. The dominance of spammers becomes clearer when Twitter suspension included 50% of the accounts that were created in 2014  . Spammers, although elaborate, show specific characteristics that discriminate them from legitimate users. Posting numerous posts on a handful (and even just one) topics throughout the lifetime of an account is a sign of spamming as marketing spammers advertise a specific product and publish many posts to gain visibility. Profile of spammers in most cases does not show their age and gender BIB002 . Their connections are different from normal users as the majority of their connections are not reciprocal BIB003 .