Abstract
Crimes occur all over the world and with regularly changing criminal strategies, law enforcement agencies need to manage them adequately and productively. If these agencies have prior data on the crime or an early indication of the eventual felonious activity, it would encourage them to have some strategic preferences so that they can deploy their restricted and elite assets at the spot of a suspected crime or even better explore it to the point of anticipation. So, integration of social media content can act as a catalyst in bridging the gap between these challenges as we are aware of the fact that almost all our population uses social media and their life, thoughts, and, mindset are available digitally through their social media profiles. In this paper, an attempt has been made to predict crime pattern using geo-tagged tweets from five regions of India. We hypothesized that publicly available data from Twitter may include features that can portray a correlation between Tweets and the Crime pattern using Data Mining. We have further applied Semantic Sentiment Analysis using Bi-directional Long Short memory (BiLSTM) and feed forward neural network to the tweets to determine the crime intensity across a region. The performance of our prosed approach is 84.74 for each class of sentiment. The results showed a correlation between crime pattern predicted from Tweets and actual crime incidents reported.
Highlights
With the upsurge of online media, the web has become an energetic and enthusiastic domain wherein billions of people all around the globe associate, offer, post and share their daily activities
We hypothesized that publicly available data from Twitter may include features that can portray a correlation between Tweets and the Crime pattern using Data Mining
The results showed correlation between crime pattern predicted from Tweets and actual crime incidents reported
Summary
With the upsurge of online media, the web has become an energetic and enthusiastic domain wherein billions of people all around the globe associate, offer, post and share their daily activities. Social media allows its users to share their apprehensions, ideas and daily activities on the web This shared content by the individuals when joined together provides a rich resource of naturally occurring data. The influence can be judged from the fact that the fake news travels or gets viral faster than the real and valuable information This effect has only increased and sometimes does get morphed into something unpleasant and hostile, where these interactions have gravitated towards the unconstructive side of things which includes bullying, trolling, stalking, social media trials etc. This paper is organized as follows: After brief introduction, Section II provides a summary of related works in area of crime Prediction using data from social networking sites.
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