## 1 Introduction
### Related Literature
This small group were aware of issues in online privacy but they had been selected asbeing the most \"digitally engaged students\", which is not a representative sample of the whole cohort of students. They advocate teaching online privacy in schools in their conclusion [39]. [PERSON] _et al._ (2014) have undertaken a long term study of Twitter users' concerns using surveys and interviews with adult subjects. The results outlined the concerns that the users of Twitter and other social network sites had and how these had changed over the duration of this study. They also included the users perception of the impact of targeted advertising related to what the user was posting at that time. The subjects of this study were aware that their data and posts were being mined and used for directed advertising. The term \"creepy\" in the title of the paper [40] relates to the perceptions of the people interviewed to the targeted advertising using Twitter and not the application used in our work. [PERSON] _et al._ based their work on the analysis of leaks in Twitter on a very large sample obtained over a nine month period [41]. Their research was to identify privacy leaks related to three distinct categories of tweets, namely vacation plans, tweeting while being drunk and divulging medical conditions. They identified that people were tweeting on the assumption that this information was private as it was shared only with friends and family, which they classified as primary leaks. However the information leakage occurred during retweeting, which they classified as secondary leaks. They also identified that 76% of alerts related to vacations were easily available to burglars [41]. [PERSON] _et al._ (2014) developed a framework to retrieve tweets which enabled researchers to investigate trends and topics using time stamps, geo-location data and real-time streaming. This is looking at information flows and patterns between Twitter users rather than at individual users [42]. Another paper presented a framework for determining a user's location to within 10 Km by using the textual content of their tweets [43]. [PERSON] _et al._ (2013) undertook a survey into user behavior on social networks such as Facebook, Twitter, Google\(+\), LinkedIn, and Foursquare [44]. They were using social graphs to identify relationships within the Twitter data based on friendships, followings, interaction between users and latent interaction, which is described as someone browsing a profile. They also included an investigation into traffic activity on these sites and the use of mobile devices for social networking sites. Their coverage of malicious behavior relates to spam and to attacks at the network level rather than individual misbehaving users, as in our work. Retweeting is an important aspect for Twitter users, which is used to spread information amongst users. There is a body of research which has investigated the extent to which users retweet. [PERSON] _et al._[45] use a dataset harvested from Twitter which, on analysis, showed that retweeting displays a \"chain effect\" and that retweeting is extremely popular because it is easy to use within Twitter applications. Analysis of retweeting behaviour and the factors that influence how retweets propagate, such as the user themselves, the content of the tweet and the timing factor determine whether a tweet is retweeted is presented [46]. They concluded that each user in their dataset retweeted in a seven day period, and that the majority do so at a low frequency, with the average number of retweets being 197. The 3% of users who retweeted more than 1,000 times in the same period were classified as retweet-aholics [46]. Tweet and retweet behaviors were gathered in a micro blogging web site located in China, which has similar functionality to Twitter. They collected a data sample of 1.7 million users with 4 billion flowing relationships. 300,000 micro blogs were extracted which consist of the original \"tweet\" and all of the associated \"retweets\".