Abstract:
Social media provides opportunities for users to share their thoughts freely. Every year
they generate a large volume of data. In the context of social media, they may include
hidden details, which may convey significant events. Crime prediction with the help of
Social media provides new dimensions in researches. This research aims to collect data
from Twitter posts and validate them using online news to avoid false data. First and
foremost, we selected the top crimes happening in the world after an extreme literature
review. We used Twitter API and News API to fetch data from Twitter and News
blogs. We used two filters to collect data. In the first filter, we fetch Twitter posts and
News posts for a specific time duration. These data are fetched by using keywords that
relate to crime. In the second filter, eliminate noisy Twitter posts from the collected
dataset. We have collected many noisy posts in both sources, i.e. Twitter and News.
With the help of collected datasets, we will compare each tweet and news datum and
give ratings for comparison data. We can build a crime prediction model with
integrating data. The result shows that 68% of collected Twitter posts are excluded
after using the second filter. Future development can divide into two main parts. To get
more accuracy, we can integrate other factors that affect crime prediction such as
weather, human behavior analysis data and we can improve the second filter using the
SVM algorithm. Secondly, we can integrate other Social media platforms to fetch data.
Keywords: Crime prediction, Social media, Twitter, News
