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Nowadays the adoption rate of Facebook like social networking website is increasing with

significant ratio. These sites are widely used for posting posts, sharing images and group

communications. Possibilities of illegal activities are unavoidable as the post and activities are

public to everyone directly or indirectly. These illegal activities include creating fake accounts,

posting malicious posts, adult images etc. Anything posted on OSN gets viral within a short

span of time. If the post is malicious in nature it may cause a riot which would disturb the

normal working of society. Our proposed system is addressing this issue by automatically

removing malicious posts in zero hour by creating a portal which would classify the user's

post into different categories and to further analyze and recognize the malicious post using

NLP (Natural Language Processing). Sentiment analysis is done on user posts and comments

to detect user sentiments. Adult images are blocked using adult detection based on image

processing.

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Facebook Watchdog : Automated Removal of Malicious Post from Facebook

Chapter 1

INTRODUCTION

Web applications, especially social networks (such as Facebook, Twitter etc.) are enjoying ever

growing popularity. One of the most famous and popular social networks is Facebook with 1.17

billion monthly active users in 2016and has recently surpassed Google as the most visited site

on the Internet.[1] A multitude of examples exist which demonstrate how Facebook influences

our daily life. Even in areas of life which have always considered being private and/or intimate

is shared publically now, the usage of Facebook has become more popular .So eventually

the rise in Facebook activities is rapidly increasing day by day. For example Facebook saw

350 million users generating over 3 billion posts, comments and likes during the 32 days of

the FIFA world cup 2014. Every 60 seconds on Facebook 510 comments are posted,293000

statuses are updated and 136000 photos are uploaded. This enormous magnitude of activities

makes Facebook a lucrative venue for malicious entities to seek monetary gains and compromise

system reputation. Today Facebook, being the most preferred OSN for users to interact with

each other, group communications, to post their opinions and get news, is potentially the

most attractive platform for malicious entities to launch cyber-attacks. These cyber-attacks

include misinformation on Facebook, luring victims into scams, phishing attacks, malware

infections, malicious post etc. It has been claimed that Facebook spammers make 200 dollar

million just by posting links. Such activity not only degrades user experience but also violates

Facebook's terms of service. Lately one post can create a havoc or cause riots if group of

people find it offending. Thus the environment of the society is disturbed as well as properties

are damaged because of a single malicious post. There have been numerous real time examples

of this over the world where facebook posts caused riots. For eg In Mumbai on 21 June 2014,

riots took place in Dhule district because objectionable content about minority community

had been posted on Facebook. Another famous riot took place in Pune in June 2014 where

a controversial facebook post that contained defamatory pictures with allegedly derogatory

references to warrior King Shivaji Maharaj were posted on Facebook which caused a violent

protest and affected the place for two days.

1.1 INTRODUCTION OF THE SYSTEM

In this project, we address the problem of automatic real-time detection of malicious content

posted by user. We intend to develop a portal that classifies user posts with the help of NLP

into different categories such as Politics, Education, Entertainment and Sports etc. Classifying

the post gives probable effect of the original post, so it's easier to understand social effect of

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any post on Society or any such social media. So our system focuses on user statuses, which

can be viewed as opinions of users or their reaction on concern we want to analyze. Texts

are extracted from posts, images to know how people feel about different posts thus sentiment

analysis is done. Sentiment analysis is applied on classified post to identify good and bad words;

the post containing maximum bad words are further automatically removed by implementing

NLP. Therefore, any user posting any malicious post which would cause disturbance in society

is automatically removed from this Social Networking Portal. Adult images are blocked using

adult image detection based on Image Processing.

1.2 BRIEF DESCRIPTION

1.2.1 Aim

Our aim is to provide a portal which would automatically remove malicious post at zero hour

from the portal and to classify them into different categories like history, sports , education ,

entertainment and politics with the help of NLP. And also to identity and remove adult images

using AID algorithm .

1.2.2 Objectives

1. To block Adult images using AID Algorithm based on Image Processing.

2. To do Sentiment analysis on user posts and comments using NLP algorithm .

3. To classify post into different categories like history, sports , education , entertainment

and politics

4. To track bad count of user and ban users who exceed bad count.

1.2.3 Motivation

In 20 minutes on an average day in Facebook:

1. 1.3 millions photos are tagged

2. 1.9 million statuses are updated

3. 2.7 million photos are uploaded

4. 2.7 million messages are sent

5. 10 million comments will be made.

This shows a huge amount of activity taking place in short span of time. This enormous

magnitude of activities makes Facebook the most attractive platform for malicious entities to

launch cyber-attacks. These cyber-attacks include misinformation, scams, phishing attacks,

cyber stalking, cyber bullying, malicious posts and etc. Often people express their views on

Facebook with statuses or comments, which can be malicious in nature; word malicious means

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to 'cause harm'. At times it happens malicious posts/comment can hurt the sentiments of individuals,

religious groups or organization.In return it can trigger uproar in society which can

disturb the normal functionality of society.Some examples which caused riots because of malicious

posts are UK riots 2012, Iranian election protests of 2009-2010, Egyptian protests 2011

etc[14].Therefore we intend to develop a portal which would automatically remove malicious

content on zero-hour using NLP.

1.2.4 Summary of the System Functionality

The portal/system will be able to detect and remove malicious post from the portal at zero

hour posted by the user. The post can be in text or an image format. For removing malicious

post in text format NLP is used. If the post is not malicious in nature it is further classified into

various categories like politics, sports, entertainment and education using sentiment analysis.

If the post is found malicious, it will be stopped from posting. In addition, adult image

detection algorithm is used for detecting adult images; if the image crosses the set threshold

value then it is declared as adult image and will not be used for further processing.

1.3 PROJECT SCOPE

1.3.1 Overview of the Target for the Final System:

The system is mainly targeted to the users using OSN like Facebook for securing users from

malicious post and adult images .

1.3.2 Overview of the Technical Area

1. JDK:

The Java Development Kit (JDK) is an implementation of either one of the Java SE,

Java EE or Java ME platforms released by Oracle Corporation in the form of a binary

product aimed at Java developers on Solaris, Linux, Mac OS X or Windows.

The JDK includes a private JVM and a few other resources to finish the recipe to a Java

Application. Since the introduction of the Java platform, it has been by far the most

widely used Software Development Kit (SDK).

2. MySQL:

MySQL is an open-source relational database management system (RDBMS) .The SQL

acronym stands for Structured Query Language. The MySQL development project has

made its source code available under the terms of the GNU General Public License, as

well as under a variety of proprietary agreements.

1.4 APPLYING SOFTWARE ENGINEERING

APPROACH

The development model that we will be following for implementation of the software is the

waterfall model.

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1. Requirement Gathering and Planning:

This is the first stage of our system where we gathered information related to our project

by visiting social sites and go through different papers.

2. Implementation:

In implementation stage we programmed different modules .In addition, different modules

are combined together and executed as single program.

3. Verification:

In verification stage we did various testing like unit testing and system testing.In unit

testing we tested modules individually. And in system testing , we tested our system.

4. Deployment and maintenance:

This is the final stage of our system where our system is ready for use.

1.5 ORGANIZATION OF THE PROJECT REPORT

Chapter 1: Introduction

This section consists of basic information about proposed system. This chapter includes

various goals and objectives which are to be achieved by the proposed system. It helps

to focus on desired aim of the system.

Chapter 2: Background and Literature Survey

This section consists of literature survey of proposed system.

Chapter 3: Requirement and Analysis

This section consists of the problem statement which need to achieved .System requirements

specifications are defined in this section. A system requirement specification is

a structured collection of information that embodies the requirements of a system i.e

software and hardware requirement for running the proposed system. In addition, it also

contains use case diagram for the system. This section also contains the various method

used for achieving the desired aim.

Chapter 4: Design

This section includes the E-R Diagram: An entity relationship diagram shows the relationships

of entity sets stored in a database.ER diagrams illustrate the logical structure of

databases. It also includes the UML diagrams such as State diagram, Activity diagram,

Component diagram, Deployment diagram etc.

Chapter 5: Implementation

This section includes actual implementation of the system. System Architecture for

the proposed system is defined in this section. This part mainly focuses on coding for

different modules. It includes some important screen-shots of the project.

Chapter 6: Result Analysis and Evaluation

This section includes the final result and related discussion. It gives tabular representation

of test cases.

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Chapter 7: Conclusion and Future scope

This section concludes the project from evaluation of the result and defines scope of the

project in future.

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Chapter 2

LITERATURE SURVEY

OSN like Facebook are popular collaboration and interpersonal communication tool for millions

of users and their friends, with 500 active million users. Facebook has gained popularity

among all age group of people; it encourages users to create profiles that contain information

about themselves, like their photo, name, occupation, interests, address etc. Because of this

huge amount of information, Facebook is prone to various malicious attacks like frauds, phishing

attacks, malware infections, malicious post etc. Researchers have used various supervised

learning models to detect spam and other types of malicious content on OSNs like Facebook

and achieved positive results [10]. One of the studies on detecting malicious content is experimented

on 4.4 million public posts generated during 17 news-making events on Facebook[1]

which found substantial presence of malicious content . This study observed characteristic

differences between malicious and legitimate posts and used them to train machine learning

models for automatic detection of malicious posts. The extensive feature set was completely

derived from public information available at post creation time, and was able to detect more

number of malicious posts as compared to existing clustering based spam campaign detection

techniques. This study also deployed a real world solution in the form of a REST API and a

browser plug-in to identify malicious Facebook posts in real time. Some of the study done to

detect malicious content on Facebook and other OSN's are stated in following paragraphs.

2.1 Detection of malicious content on Facebook:

Gao et al.[2] used facebook accounts of different users to do an initial study to quantify and

characterize spam campaigns with the help of a set of automated techniques to detect and characterize

the coordinated spam campaigns .The authors observed a huge anonymized dataset

of 187 million asynchronous wall messages between various Facebook users. In return, authors

detected approximately 200,000 malicious wall posts with embedded URLs, which were originating

from more than 57,000 user accounts. Following this, Gao et al. [3] then proposed an

online spam filtering system to inspect messages generated by users in real time as a component

of the OSN platform. Rather than analyzing each post individually, this approach mainly

focused on redeveloping spam messages into campaigns for classification. Resultant was that,

using 187 million facebook wall posts as their dataset they got true positive rate of roughly

over 80 percent. In addition, authors achieved 1,580 messages/sec as average throughput and

21.5m as an average processing latency rate. However, this approach was not successful in

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detecting any new malicious post if the system has not noted it previously as the clustering

approach used always marked a new cluster as legitimate. To protect Facebook users from

real time malicious posts, Rahman et al.[4] took advantage of the social context of posts to

deploy a social malware detection method. Using a SVM based classifier trained on 6 features;

a maximum true positive rate of 97 percent was achieved by the authors. The classifier took

46ms to classify a post. MyPageKeeper, a facebook app was developed using this model to

protect its users from malicious posts. This model also targeted at detecting spam campaigns,

and depended on message similarity features. Such techniques are efficient in detecting content,

which they have seen in the past, for example, campaigns. However, if the system is

not familiar with the post in past, then these techniques are incapable of detecting malicious

posts in real time. Nevertheless, in our propose system we overcome this flaw by using NLP to

detect malicious post at zeroth hour. Below is a summary table i.e table 2.1 of the give study

above.

Table 2.1: Summary of Literature Survey

Sr.

No.

Paper Name Objective Data/Method

Used

Result and Limitation

1 Detecting and

characterizing

social spam

campaigns

To quantify

and characterize

spam campaign

Observed a huge

anonymized dataset

of 187 million

asynchronous wall

messages between

various Facebook

users

Detected approximately

200,00

malicious wall post

with embedded

URL

2 Towards online

spam filtering in

social networks

Proposed an

online spam

filtering software

to observe messages

send by

user

Used 187 million

facebook wall posts

as their data-set

Achieved true positive

rate of 80

percentage. However

this approach

was not successful

in detecting any

new malicious post

which the system

has not recorded

previously.

3 Efficient and

scalable socware

detection

in online social

networks

To protect users

from real-time

malicious post

Used a SVM based

classifier trained on

6 features

Achieved a true

positive rate of

97 percent. Took

46ms to classify

a post. But this

approach also

had the similar

problem as stated

in the above

approach.

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2.2 Detection of malicious content on other OSNs:

Many machine-learning models have been studied in the past to detect malicious content on

other OSN such as Twitter and YouTube, [5], [6]. The parameters that affect the efficiency of

such models are age of the account, number of social connections, past messages of the user, etc.

[10], which are not available on Facebook publicly. Other techniques make use of OSN specific

features like user mentions, user replies, retweets (Twitter), views and ratings (YouTube)[9],

which are not available in Facebook. Blacklists have been shown to be ineffective, capturing

less than 20 percent URLs at zero-hour [7].

2.3 Facebook's Current Techniques:

1. For detecting malicious URLs in real time and preventing them from entering the social

graph, Facebook's immune system uses multiple URL blacklists [8]. The limitation of

the blacklist is that it is incapable in detecting URLs at zero-hour which limits the

effectiveness of this technique [7].After taking an analysis using Graph API to check if

Facebook removed any of the 11,217 malicious posts identified by blacklists after being

posted. The result was disappointing as only 3,921 out of the 11,217 (34.95 percent)

malicious posts had been deleted the remaining got past Facebook's real time filters i.e.

almost two thirds of all malicious posts (65.05 percent) and it remained undetected even

after 4 months (July - November, 2014) from the date of post.

Figure 2.1: An example of a malicious post from Facebook, this URL in the post ask users to

like a post on Facebook to earn money as indirectly its pointing to a scam website.

2. To protect its users from malicious URLs, in 2011 Facebook collaborated with Web

of Trust. This partnership states that whenever a user clicks on a link which has been

reported on WOT as malware, phishing, spam or any other kind of abuse, then Facebook

shows a warning page to the user (Figure 2). To verify this claim and to cross check the

existence and effectiveness of the warning pages , we visited some random 1000 posts

on Facebook containing a URL marked as malicious by WOT, and clicked on the URL.

Surprisingly, the warning page did not appear even once.

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Figure 2.2: Example of WOT warning page which Facebook claims to show whenever a user

clicks on a link which is noted as abusive on Web of Trust.

As stated above one of the problem with Facebook is malicious post.Although a single post is

considered as user's view, it can also be malicious in nature. If a post is found offensive it can

cause a riot. Table 2.2 consist of such incidents which are caused by Facebook post.

Table 2.2: Riots caused by Facebook status across world

Sr.

No.

Description Place Consequences

1 Communal violence erupted

over'objectionable video' posted on

Facebook over Hindu God and Goddess

Chhapra, Bihar

Aug 6, 2016

1.Mosques were

damaged by petrol

and shops of Muslim

were looted

and shops were set

to fire.

2 Communal violence erupted from an

alleged objectionable Facebook post

against Prophet Muhammad

Birbhum, West

Bengal March 3,

2016

1 killed in police firing

and1police station

was ransacked

3 Defamatory post morphing photos of

Chhatrapati Shivaji, Bal Thackeray and

others on Facebook sparks violence

across the city

Pune , Maharashtra

Jun 2,

2014

24 out of 33 police

stations were

affected stones were

pelted at vehicles

and damaged 130

PMPML buses and

21 private vehicles,

as also set fire to

one bus, tempo

4 Woman and two children killed by mob

in riots over 'blasphemous' Facebook

post

Pakistan July

2014

Houses of religious

minority group

Ahmadiyyas, were

torched by a mob

5 A boy posted a morphed image of a

Hindu goddess on Facebook

Gujarat's Vadodara

The place was disturbed

for a week

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Chapter 3

REQUIREMENTS AND ANALYSIS

Requirements analysis encompasses those tasks that go into determining the needs or conditions

to meet for a new product. We determined whether the stated requirements are clear,

complete, consistent and unambiguous, and resolving any apparent conflicts.

3.1 PROBLEM STATEMENT

Automatic removal of malicious posts from Facebook using NLP.

3.2 SPECIFICATIONS OF THE SYSTEM

3.2.1 Software requirements specification

' Hardware Requirements:

1. Personal Computers/Laptops

2. RAM : 8GB

3. System : I3 processor 2.4 GHz

4. Monitor : 15 VGA Colour

' Software requirements:

1. Operating System 7/8

2. Apache Tomcat Server 7

3. JAVA(1.8)

4. MySQL

5. Eclipse Mars

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3.2.2 System Interfaces

' Admin -

1. Admin can access the Portal posts from the developers account.

2. Admin can keep track of banned users.

3. Admin can manage all the functionalities of user account

4. Admin can unbanned users

' User -

1. User will be able to login into the system.

2. User can send friend request and also accept friend request.

3. User can start a chat with their friends

4. User can upload images and post status

5. User can like or comment post/image.

6. User can upload profile picture

' Hardware Interface:

The server is directly connected to the client systems. Also the client has the access

to the database for accessing the account details and storing the login time. The client

access to the database in the server is only read only.

' Software Interface:

Integrated system for monitoring and management is a multi-user, multi-tasking environment.

It enables the user to interact with the server and leaves a record in the

database.

' Communication Interface:

This system uses java servlets and hence requires HTTP for transmission of data.

3.3 METHODS USED

3.3.1 Natural Language Processing (NLP)

NLP is used for detecting and removing malicious post on zero hour. Following are the steps:

1. Sentiment Analysis

Sentiment analysis is a technique that determines the attitude of text. Sentiment analysis

is a type of classification. It is a concerned with determining what text is trying to convey

to a reader, usually in the form of a positive and negative attitude. Over here, we use

'sentiment analysis' to refer to the task of automatically determining feelings whether

text, is malicious in nature or not.

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2. Tokenisation

Tokenization is the process of breaking a stream of text up into words, phrases, symbols,

or other meaningful elements called tokens. The list of tokens becomes input for further

processing such as parsing or text mining. We use tokenization for breaking the user's

post or comment for further processing.Then each token is inserted into stack.

3. RSW

Sometimes, some extremely common words which would appear to be of little value in

helping select documents matching a user need are excluded from the vocabulary entirely.

These words are called stop words. Stop words are filtered out before or after processing

of data. Stop words are usually referred to the most common words in a language, there is

no single universal list of stop words used by all natural language processing tools. After

tokenization, we remove stop words from the stack which are not useful in detecting the

malicious word eg. When , the , a etc.

4. Dataset Matching

The words which are left after the removal of stop words are compared with the dataset

which contains a list of abusive/ malicious words. If the word founds a match in the

dataset then it is malicious in nature and is blocked from further processing and the

words which are not malicious in nature are categorised into various category in the

dataset

3.3.2 Adult Image Detection

1. Skin tones

The goal of skin tone detection is to build a decision rule that will differentiate between

skin and non-skin pixels.

2. Threshold

The next step for skin detection in an image is by assigning a threshold value(skin tone)

i.e. if the probability of skin tone in image is more or equal to the assigned threshold

value then that image is considered as adult and will be blocked from uploading .

3. Skin tone(Hex value)

To do the comparing of the skin tone with the assigned threshold value, it is converted

into hex value.

4. Color Model (HSV)

HSV is named for three values - Hue Saturation Value. Hue-saturation based color

spaces describes hue or tint , saturation or amount of gray in term of colors and shade

and brightness value .Hue defines color (such as red, green, yellow and purple) of an

area, saturation measures the colorfulness of an area in proportion to its brightness .The

'intensity', 'lightness' or 'value' is related to the color luminance. To do all these

processing e we are using HSV color model

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3.4 EXPECTED OUTCOMES

If post is malicious in nature or matches with the dataset , it will be blocked at zero hour. If

image uploaded is adult image i.e. containing more skintone than threshold value then it will

be blocked.

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Chapter 4

DESIGN

During the design phase we designed the system architecture of our system and also other

diagrams like ER Diagram and UML diagram.

4.1 SYSTEM ARCHITECTURE DIAGRAM

Figure 4.1: System Architecture

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Figure 4.1 gives an overview of the system. Firstly, a user has to be registered with the

portal, after that only user is able to access the system functionalities. After user is logged

on to the system successfully , he/she has various options like upload profile picture, upload

image, like post/image, add friend, accept friend request, send message etc. On the uploaded

image, Adult Image Detection Algorithm is applied. In addition, on the upload post NLP and

sentiment analysis is applied for detecting and removing malicious post at zero hour. All the

data get stored in the database. For the communication between client and server, HTTP

protocol is used.

4.2 DIAGRAMS

4.2.1 ER-Diagram

Figure 4.2: ER Diagram

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4.2.2 UML diagrams

1. Use Case Diagram -

Figure 4.3: Use Case Diagram

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2. Class Diagram -

Figure 4.4: Class Diagram

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3. Collaboration Diagram -

Figure 4.5: Collaboration Diagram

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4. Sequence Diagram -

Figure 4.6: Sequence Diagram

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5. State Diagram -

Figure 4.7: State Diagram

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6. Component Diagram -

Figure 4.8: Component Diagram

7. Deployment Diagram -

Figure 4.9: Deployment Diagram

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Chapter 5

IMPLEMENTATION

This chapter gives an overview of the proposed system as well as the working of given system.

This chapter also contains the screen-shots of the working models.

5.1 Overview of proposed system

The purpose of proposed system is accessing the Facebook posts from the registered user

account.User can upload Images, Messages, Comments to posts as well as make friendship and

upload profile pictures etc. User can perform two main tasks i.e uploading textual post and

image.Sentiment analysis will be done on textual post. This analysis consists of tokenization

and removal of stop words.If it encounters any illegal post it wont upload the post.Further

verified user Posts will be classified into different categories such as Politics, History, Education,

Entertainment and Sports by using Text Mining from Data-set and Online API's.Recognition

of adult image is done by applying AID (Adult Image Detection). This technique uses skintone

pixels of an image to detect if it is explicit in nature or not. If skin-tone pixel is more

than threshold value then it will be considered as adult image and wont be posted. Figure 5.1

gives an overview of the above stated working of system.

Figure 5.1: Overview of proposed system

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Below figure 5.2 show the logic of detecting malicious post i.e text posted by the user.

Figure 5.2: Logic of proposed system

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5.2 SCREENSHOTS

1. Screen-shot for Login Page

Figure 5.3: Login Page

Figure 5.3 shows the log in page of the portal where user can login using email id or

phone number and password.

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2. Screen-shot for User Registration Page

Figure 5.4: Registration Page

Figure 5.4 shows User registration. It is the first step of the system. User registers on

the server by filling up a form which includes personal information of the user.

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3. Screen-shot for chat window

Figure 5.5: View users

Figure 5.5 shows Chat window that shows the messages exchanged between two users

and also user can send message to their respective friends.

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4. Screen-shot for Post blocking

Figure 5.6: Post blocking

Figure 5.6 shows Post blocking of the system. If the post is malicious in nature or

matches with the data set it will be blocked.

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5. Screen-shot for Adult Image Detection

Figure 5.7: Adult Image Detection

Figure 5.7 shows the Adult Image Detection. If the image posted by the user exceeds

the set threshold value that means it is explicit in nature i.e. adult image, which will be

blocked from uploading.

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6. Screen-shot for Profile Page

Figure 5.8: Profile Page

Figure 5.8 shows Profile page of system. This image shows the profile page of user where

user can update his/her information as well as view their respective time-line .

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Chapter 6

RESULT AND EVALUATION

For checking the performance of our system we used WAPT tool(Web Application Performance

Testing).WAPT is a load and stress testing solution applicable for mobile applications,

web services and all types of web sites from online stores to corporate ERP and CRM

systems.Descriptive graphs and reports will let you analyze the performance of your system

components under various load conditions, isolate and fix any bottlenecks and optimize your

software and hardware configuration.

6.1 RESULT ANALYSIS

Following is a table which shows how the system will work with different number of user.

Different parameter like average response time, sending speed etc is taken into consideration.

Table 6.1: Result Analysis table

Sr

no

Active

no. of

user

Avg response

time, sec

(with

page

elements)

Sending

speed(kbit/s)

Receiving

speed,

kbit/s

CPU utilization

Memory

utilization

(Mb)

1 1 4.23(4.38) 1.70 2742 60

444(21)

2 5 12.8(12.9) 2.22 3430 91

539(25)

3 15 17.3(17.84) 8.20 13190 92

572(27)

Avg Response time, sec (with page elements): Shows values of average response time.

The first value is the response time without page elements, and the second value (in brackets)

is the response time with page elements

Sending speed, kbit/s :Shows how many kbits per second were sent to the server.

Receiving per user speed, kbit/s :shows the receiving speed per user.

AISSMS IOIT-INFORMATION TECHNOLOGY 2016-17 30

Facebook Watchdog : Automated Removal of Malicious Post from Facebook

Memory utilization: Memory utilization is represented by 2 values. The first value is the

amount of used memory in megabytes (Mb). The second value (in brackets) is the percentage

of memory utilization.

Thus the conclusion is, as number of user increases response time also increase.

6.2 TESTING

Software testing is the process which allows us to test each and every module of the system by

executing various tests on the system. Software testing is very helpful in order to check that

the system is working as per the requirements.

6.2.1 Unit Testing

Table 6.2: Test Cases for unit testing

Test

ID

Test Objective

Pre-Condition Steps Test Data Expected

Result

Actual

Result

1.

Successful

User Registration

A Registration

form to be

available

Enter all

required

information

All details

of the user

User

should be

registered

successfully

and

if any field

is empty/

incorrect

it gives an

alert

The user

is registered

successfully

2. Successful

User Login

A valid User account

should be

available

Enter

the username

and

password

in login

field and

click login

button.

A valid

username

and password

User

should be

logged in

successfully

and

if any field

is empty /

incorrect it

should give

an alert.

User is

logged

in successfully

.

AISSMS IOIT-INFORMATION TECHNOLOGY 2016-17 31

Facebook Watchdog : Automated Removal of Malicious Post from Facebook

Test

ID

Test Objective

Pre-Condition Steps Test Data Expected

Result

Actual

Result

3.

Post

uploading:image

User should be

logged in.

Upload

an image

and click

on post

button.

A valid

image

format.

If image

is not

adult it

should be

uploaded

successfully.

and

if image is

adult then

it should

not be

uploaded

Post is

uploaded

successfully.

4.

Post

uploading:Text

User should be

logged in.

Enter

the text

and click

on post

button.

Input

should

contain

only a

text.

If text is

malicious

in nature

it should

be blocked

from

further

processing

else

it should

be successfully

posted

Post is

uploaded

successfully.

5. ClassificationUser should be

logged in.

Enter

the text

and click

on post

button.

Input

should

contain

only a

text.

If the post

is not

malicious

in nature

the post

should get

classified

in different

categories

Post is

classifying

in

different

categories

successfully.

AISSMS IOIT-INFORMATION TECHNOLOGY 2016-17 32

Facebook Watchdog : Automated Removal of Malicious Post from Facebook

6.2.2 System Testing

Table 6.3: Test Cases for unit testing

Test

ID

Functionality

to tested

Test Procedure

Expected Result

Actual

Result

Pass/Fail

1

To verify

whether JDK is

installed

Java environment

checked

Java should be

installed

JDK installed

pass

2

To verify the

existence of

Eclipse IDE

Eclipse IDE

environment

checked

Eclipse should

be installed

Eclipse is

installed

Pass

3

To verify existence

of Apache

Tomcat server

To check the

installation of

Tomcat

Tomcat should

be installed

Tomcat is

installed

Pass

4

To verify installation

of MySQL

Workbench

Check the

MySQL environment

MySQL should

be installed

MySQL is

installed

Pass

AISSMS IOIT-INFORMATION TECHNOLOGY 2016-17 33

Facebook Watchdog : Automated Removal of Malicious Post from Facebook

Chapter 7

CONCLUSION AND FUTURE

WORK

7.1 CONCLUSION

Over the years many shortcomings on OSN like Facebook has be identified; one of them is to

identify and remove malicious content from Facebook especially which is posted by the user.

Many approaches have been used to identify and remove malicious content posted by user on

zero hour. None of them is successful in real time detection. Facebook is a social networking

site used for giving voice to ones thoughts however; a certain post can cause a riot or can hurt

the sentiment of the others. Our system overcomes this disadvantage by using NLP where it

protects it's user from malicious content using NLP. Our system has achieved the objective of

real time detection of malicious content and to further divide the post into various categories

like politics, sports, education, entertainment etc. The proposed system will also detect adult

images using adult image detection algorithm and will maintain bad count for every user. If

the bad count exceeds the set threshold value, the user will be blocked from the portal.

7.2 FUTURE WORK

1. We can enhance the system by adding facial recognition to precisely detect an adult

image as it classifies face as an adult image because of skin-tone

2. We can apply adult image detection technique on user's profile picture to ensure that

even profile picture is not an adult image

3. We can enhance our algorithm to detect a sarcastic post as now a days sarcasm is too

common.

4. We can add regional language to our system to attract user who don't know English.

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