Last updated: 05 April, 2019 (See changelog).
POCSO case-laws can be accessed from the District Courts portal - Ecourts. Mining these datasets, one can easily analyse the metadata related to a case such as hearing dates, judge, petitioner and police station details, etc. But important variables related to a victim and the accused are hidden in the judgement, which is a textual representation of a proceedings inside the court accompanied by the final order for the case. Reading multiple judgements, we found patterns where we were able to extract some of these variables and through this brief analysis, we aim to figure out what all is possible if we can extract hidden entities present in a judgement.
Current analysis is a WIP.
Region: For the purpose of this pilot, we are doing a textual analysis of some judgements from the Thane District of Maharashtra. Here are some numbers
Timeline: 2013 - 2019 (March)
Case Type: All cases (with and without judgement)
Total cases: 4,181
Total judgements: 1,326
Below graphs show a yearly, monthly and a daily trend of POCSO cases as per tee registration dates and the data of decision.
The below analysis is done for reported judgements only. The above plots show that the year 2018 had the maximum number of judgements, but the most reported judgements were from the year 2015
Several reports around POCSO cases have shown a trend where the victim is often acquitted at the end of the trial. There have been very few convictions in these cases. Let’s see if a similar trend is observed when we analysed the final orders passed in these judgements
Analyse age of the victims - This is an important variable given the context of this act and it is not available directly as a variable from the metadata through Ecourts.
We will also try to see if there are yearly patterns in convctions and if age of the victim is somehow correlated with the acquittal of the accussed
Analysing the final orders passed in the judgements. We obsereved some patterns:
Let’s look at the wordclouds:
A judgement is a treasure trove of information. In our analysis we found several important keywords which can be used to categorise judgement in several buckets and understand them better. This can help the policy makers to understand these cases efficiently and design policies accordingly to prevent the occurrance of this crime. To showcase the capabilities, we tried finding the occurrance of the word whatsapp in judgements to see where and how it was used. Please check the table below for the results.
The first coulmn displays the ID of the judgement
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