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This assignment consists of a report worth 20 marks. Delays caused by student`s own computer downtime cannot be accepted as a valid reason for late submission without penalty. Students must plan their work to allow for both scheduled and unscheduled downtime.

Submission instructions: You must submit an electronic copy of all your assignment files via Cloud- Deakin. You must include both your report, source codes, necessary data files and optionally presentation file. Assignments will not be accepted through any other manner of submission. Students should note that email and paper based submissions will ordinarily be rejected.

Special requirements to prove the originality of your work: On-campus students (B and G) are required to demonstrate the execution of your classification programs in R to your tutor in Week 10; Cloud students are required to attach a 3-5 minutes Video presentation to demonstrate how your R codes are executed to derive the claimed results. The video should be uploaded to a cloud storage (You can find out how to upload a video  Failure to do so will result a delayed assessment of your submission.

Late submissions: Submissions received after the due date are penalized at a rate of 5% (out of the full mark) per day, no exceptions. Late submission after 5 days would be penalized at a rate of 100% out of the full mark. Close of submissions on the due date and each day thereafter for penalties will occur at 05:00 pm Australian Eastern Time (UTC +10 hours). Students outside of Victoria should note that the normal time zone in Victoria is UTC+10 hours. No extension will be granted.

It is the student`s responsibility to ensure that they understand the submission instructions. If you have ANY difficulties ask the Lecturer/Tutor for assistance (prior to the submission date).

Copying, Plagiarism Notice

This is an individual assignment. You are not permitted to work as a part of a group when writing this assignment. The University`s policy on plagiarism can be viewed online at



The popularity of social media networks, such as Twitter, leads to an increasing number of spamming activities. Researchers employed various machine learning methods to detect Twitter spams. In this assignment, you are required to classify spam tweets by using provided datasets. The features have been extracted and clearly structured in JSON format. The extracted features can be categorized into two groups: user profile-based features and tweet content-based features as summarized in Table 1.

The provided training dataset and testing dataset are separately listed in Table 2 and Table 3. In testing dataset, we can find that the ratio of spam to non-spam is 1:1 in Dataset1, while the ratio is 1:19 in Dataset 2. In most of previous work, the testing datasets are nearly evenly distributed. However, in real world, there are only around 5% spam tweets in Twitter, which indicates that testing Dataset 2 simulates the real-world scenario. You are required to classify spam tweets, evaluate the classifiers’ performance and compare the Dataset 1 and Dataset 2 outcomes by conducting experiments.


Twitter Spam Detection Work Flow

Problem Statement

This is an individual assessment task. Each student is required to submit a report of approximately 2,000-2,500 words along with exhibits to support findings with respect to the provided spam and non-spam messages. This report should consist of:

Overview of classifiers and evaluation metrics

Construction of data sets, identification of features and the process of conducting classification

Technical findings of experiment results

Justified discussion of the performance evaluation outcomes for different classifiers

To demonstrate your achievement of these goals, you must write a report of at least 2,000 words (2,500 words maximum). Your report should consist of the following chapters:

1.A proper title which matches the contents of your report.

2.Your name and Deakin student number in the author line.

3.An executive summary which summarizes your findings. (You may find hints on writing good executive summaries from http://unilearning.uow.edu.au/report/4bi1.html.)

4.An introduction chapter which lists the classification algorithms of your choice (at least 5 algorithms), the features used for classification, the performance evaluation metrics (at least 5 evaluation metrics), the brief summary of your findings, and the organization of the rest of your report. (You may find hints on features used for classification from Twitter Developer Documentation

5.A literature review chapter which surveys the latest academic papers regarding the classifiers and performance evaluation metrics of your choice. With respect to each classifier and performance evaluation metrics, you are advised to identify and cite at least one paper published by ACM and IEEE journals or conference proceedings. In addition, Your aim of this part of the report is to demonstrate deep and thorough understanding of the existing body of knowledge encompassing multiple classification techniques for security data analytics,

specifically, your argument should explain why machine learning algorithms should be used rather than human readers. (Please read through the hints on this web page before writing this chapter 

6.Technical demonstration chapter which consists of fully explained screenshots when your experiments were conducted in R. That is, you should explain each step of the procedure of classification, and the performance results for your classifiers. Note, what classifiers you presented in literature review should be what you conduct experiments.

7.Performance evaluation chapter which evaluates the performance of classifiers. You should analyse each classifier’s performance with respect to the performance metrics of your choice. In addition, you should compare the performance results in terms of evaluation metrics, e.g., accuracy, false positive, recall, F-measure, speed and so on, for the selected classifiers and datasets.

8.A conclusions chapter which summarizes major findings of the study (You should use at least 5 evaluation metrics to evaluate the performance of classifiers and compare the performance of different classifiers. You can demonstrate your experiment results in the form of table and plots), discusses whether the results match your hypotheses prior to the experiments and recommends the best performing classification algorithm.

9.A bibliography list of all cited papers and other resources. You must use in-text citations in Harvard style and each citation must correspond to a bibliography entry. There must be no bibliography entries that are not cited in the report. (You should know the contents from this page http://www.deakin.edu.au/students/study-support/referencing/harvard.)



Proficient (above 80%)


Average (60-79%)

Satisfactory (50-59%)

Below Expectation (0-50%)









Use appropriate

Use discipline-specific

Use some discipline-specific

Fail to demonstrate

Out 0f 4

Writing in

language and genre to

language and genres to

language and prescribed genre

understanding for



extend the knowledge

address gaps of a self-selected

to demonstrate understanding

lecturer/teacher as audience.



of a range of audiences.

audience. Apply innovatively

from a stated perspective and

Fail to apply to a similar




the knowledge developed to a

for a specified audience. Apply

context the knowledge




di erent context.

to di erent contexts the






knowledge developed.









Collect and record self-

Collect and record self-

Collect and record required

Fail to collect required

Out of 4


determined information

determined information/ data

information/ data from self-

information or data from the



from self-selected

from self-selected sources,

selected sources using one of

prescribed source; Fail to



sources, choosing or

choosing an appropriate

several prescribed

organize information/data



devising an appropriate

methodology based on

methodologies; Organize




methodology with self-

structured guidelines;

information/data using

prescribed structure; Fail to



structured guidelines;

Organize information/data

recommended structures.

respond to questions/tasks



Organize information

using student-determined

Manage self-determined

arising explicitly from a



using student-

structures, and manage the

processes with multiple possible

closed inquiry



determined structures

processes, within the

pathways; Respond to




and management of

parameters set by

questions/tasks generated from




processes; Generate

the guidelines; Generate

a closed inquiry.










eses based on literature

framed within structured











Provide fully explained

Provide fully explained

Provide screenshots with R

No screenshots and

Out of 4


screenshots with R

screenshots with R script.

script. Explain each step of the

explanations provided.



script. Explain each step

Explain each step of the

procedure of classification, and




of the procedure of

procedure of classification,

the performance results. But




classification, and the

and the performance results.

many parts of demo are not




performance results in

The entire demo is clear, but

clear enough and/or contain




details. The entire demo

there are some mistakes.

major flows or mistakes.




is clear, correct and







covers all findings.









Evaluate information/data and

Evaluate information/data and

Fail to evaluate

Out of 4

ce Evaluation

information/data and

the inquiry process

reflect on the inquiry process

information/data and to



inquiry process

comprehensively developed

based on the given literature.

reflect on inquiry process.



rigorously based on the

within the scope of the given

Use only one testing data set.

Use one or no testing data



latest literature.


Less than 4 classifiers work

set. Less than 2 classifiers



Reflect insightfully to

Reflect insightfully to renew

correctly. Less than 4 evaluation

work correctly. Less than 2



renew others`

others` processes. Construct

metrics apply to analyse the

evaluation metrics apply to



processes. Construct

and use one testing data set

performance of classifiers.

analyse the performance of



and use one testing

and two training data sets. 4





data set and two

classifiers work correctly. 4





training data sets. 5

evaluation metrics apply to





classifiers work

analyse the performance of





correctly. 5 evaluation






metrics apply to analyse






the performance of












More than 10

More than 10 bibliographic

More than 10 bibliographic

Less than 10 bibliographic

Out of 4


bibliographic items (all

items (most of them are

items (most of them are

items are presented. Or



of them are academic

academic papers and at least 1

academic papers) are

there are more than 3 errors



papers and at least 1

items per classification) are

presented. Inline citations are

in the bibliographic list and



item per classifier/ at

presented, but there are a few

often used incorrectly.

inline citations.



least 1 item for per

errors. Inline citations are





evaluation metrics) are

used but with a few errors.





correctly presented and






inline citations are






correctly used.


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