## 1 Introduction
### Related Literature
They determined that each \"tweet\" had been \"retweeted\" up to 80 times. They then modeled the \"retweet\" behavior, which demonstrated the same behavior is found on similar micro blogging sites as on more famous sites such as Twitter [47]. The speed and degree by which information is disseminated through the \"Twittersphere\" is modeled in a number of papers. [PERSON] _et al._[48] used a dataset which comprised 1.7 million Twitter users. They identified 23.7 million retweets that originated from these users and categorized them according to their popularity. They defined four levels of popularity which ranged from \"unpopular\" to \"highly popular\" based on the number and the speed of diffusion of retweets within the data sample. [PERSON] _et al._[49] have developed two models to identify the content of peoples' tweets to identify how likely they would be to retweet and also the likelihood as to whether they would propagate information via retweets when asked to do so by a stranger. [PERSON] _et al._[50] have modeled \"retweetability\" using their \"Information Diffusion Impact\" model which examines the roles of the Twitter users involved in retweeting. These are the \"information starter\", \"amplifier\", and \"transmitter\". They have validated the results from their model with a dataset from the retweeting that occurred directly after the Boston bombings and the Japanese earthquake. [PERSON] _et al._[51] have developed a framework to measure the way that dynamic information propagates using retweets. They compare a large scale dataset from Twitter with their experimental results to evaluate their framework. Modeling retweeting behaviors is presented in this paper [52] to predict how many times a tweet is retweeted. [PERSON] _et al._ use a two-phase model to predict how many times a tweet will be retweeted. They have also used a dataset from Twitter to test the accuracy of their framework when compared to real data. The results of several retweet behavior models which have been used to evaluate Twitter data are presented by [PERSON] _et al._[53]. Their dataset consisted of over 768,000 tweets which originated from 30,000 Twitter users and they concluded that there are a lot of factors that influence retweeting behavior and this complexity cannot be duplicated with a single model. Current research into social networks is mainly focused on investigating the mining of information from Twitter to identify trends and themes, often using social graphs. Retweeting is part of normal behavior amongst Twitter users and follows a number of models, as the studies discussed above have shown. Some tweets are massively retweeted while others are only retweeted a few times. Our work differs from the approach taken by the majority of researchers in that we demonstrate the danger to an individual user of leaking even the smallest piece of information from social networks which could expose them to being stalked or even having their home burgled. The test subjects in this work were completely unaware of their exposure and they are all adults over 21 years of age. We believe our approach to this research area is novel.