Public media such as TV or newspapers, paired with crime statistics from the authority, raise awareness of crimes within society. there exists a spatial dependency regarding the activity space of a user (and the crime-related tweets of this user) and the actual location of the crime incident. Furthermore, the demographic analysis indicates that the type of a crime as well as the gender of the victim has great influence on whether the crime incident is usually spread via Twitter or not. squared value from the linear to the quadratic model which indicates that this latter model fits the data somewhat better than the former (linear: R2?=?0.644, Nelarabine (Arranon) quadratic: R2?=?0.662). That is due to the curve effect of the quadratic function that describes better the initial increasing slope of the points. Nevertheless, the data do not show a strong spatial dependency that would have been observed if a lognormal, exponential or Nelarabine (Arranon) a power model could have been fitted to our data. This could be the result of the small volume of the dataset that was available after the home estimation method since only 61 messages were used for this analysis. Fig.?5 Distance from users’ estimated home locations to the crime incidents’ locations. Sub determine (a) at the top shows the cumulative percentage of the crime tweets and the respective distance from the crime incidents whereas sub determine (b) at the bottom shows … Topic dependence In the last part of the analysis the crimes’ characteristics were grouped as impartial variables, which aimed to explain the depended variable of the count of the messages. Here we used the initial 3116 worldwide messages since the restriction of the location attribute was not substantial for this analysis. The characteristics were identified from the crime articles. We selected only those characteristics from which we found information in the majority of the articles. These are: i) the offender’s gender, ii) the offender’s age, iii) the victim’s gender, iv) the victim’s age, and v) the crime type (acquisitive or violent). The technique that was employed was the ordinal logistic regression analysis. The explanatory variables were measured at ordinal (age) and nominal (type and gender) scales and the dependent variable was transformed into an ordinal scale of five classes with increasing order. First, we employed the Pearson productCmoment correlation coefficient for all those variables and we found a positive correlation between the two explanatory variables and the messages’ Nelarabine (Arranon) frequency. These are the victim’s gender (r?=?0.663, n?=?30, p?r?=?0.445, n?=?30, p?Ldb2 Examples of weekday night activity spaces of users and locations of crime incidents. The activity spaces are shown as the density of activity locations over the street network. Then, we utilised these two variables in the ordinal regression model. The results of the model revealed that if the victim is a woman or the crime type is usually acquisitive, there are less chances for the crime incident to be posted on Twitter than if the victim is a man or the type of the crime incident is usually violent. In particular, for an additional victim being women, controlling for crime type, the odds of that incident being in a higher messages’ class are lower by 98.17% (Wald2 (1)?=?8.831, p?=?.003). Similarly, for an additional acquisitive crime, controlling for victim gender, the odds of that incident being in a higher messages’ class are lower by 97.98% (Wald2 (1)?=?7.596, p?=?.006). Future recommendations The details in this section are describing the initial methodology we developed for this paper. As this approach turned out not to be successful, the approach was reworked to its current state. However, we still see some valuable information in our experiences that we would like to share with the community. At the very beginning of this research work, we focussed around the available georeferenced Twitter data from London, UK in the year 2012. Unfortunately, the percentage of the georeferenced tweets is usually.