Currently, employed to detect the posts containing terrorist propaganda. SNP filter, reads the social media posts and filters the results based on three specifications:
- Terrorism Tags
- Motivation Tags
- Out-of-context (OOC) Tags
Sample content of each Tag set is given in the table below:
| Tag Set | Content |
|---|---|
| Terrorist Tags (S1) | Specifically terrorism agents or terrorism actions, such as {isis, kill, threaten,taliban} |
| Motivation Tags (S2) | {Encourage, Inspire, etc.} |
| OOC Tags (S3) | {"Name of People from Unrelated Context", etc.} |
Input: twitter
Output: preprocessedTwitter
1) Split the post into independent sentences
2) Remove the stop words from text
3) Decompose compound data, such as #supportISIS =>support ISIS
3) Stem the words to root words, such as support, supported, supporting => support
Input : a new twitter
Out put : Accept or not
1)if the twitter contains words in filter set 1 then
2) if the twitter contains words in filter set 2 then
3) if the twitter contains words in filter set 3 then
4) return not accepted
5) else
6) return accepted
The code in Mainapp.java is doing basic filtering. Run this file, in the result, should see a set of filtered twitter set in the standard output. This set should contain the twitters that have motivational terrorism information in them.
Example of multiple nouns : Suiside Belt, cut their heads off, call upon
Since our matching algorithm workds via key word matching in a 1-gram model, such that it can't work well on Multiple Nouns. For example, it will be hard to filter out a twitter that contains the object Suicide Belt. The problem happens because when we break down the sencenten, the smallest unit after dividing is a token made up of one word, such as embasy, isis, division etc. In the meantime, the tags sets are also made up of single word, such as terrorism, isis, threaten.