MKTG 6998 — Social Media Analysis

Group Project Proposal

University of Sydney Business School

Due: Week 7 (Presentation + Written Report)

1. Overview

The Group Project Proposal is the first of two major group assessments in this course. It requires your

team to present and submit a research proposal for the final project you will complete later in the

semester. The proposal should demonstrate that your team has identified a meaningful research

question in social media marketing, grounded it in relevant literature, and developed a feasible plan to

collect social media data to address it.

This is your opportunity to receive early feedback from your instructor and peers before committing to

the full project. A strong proposal sets the foundation for a strong final project.

2. What You Will Deliver

2.1

Video Presentation

• Duration: 10–15 minutes.

• Format: Record a polished team presentation (slides + voiceover or camera). All team members

must participate visibly or audibly.

• Upload: Upload the video to YouTube as an Unlisted video and share the link with your instructor.

All video links will then be shared with the class so that peers can watch and prepare questions.

• Submission: Submit the YouTube link via the course portal by the Week 7 deadline.

2.2

Written Report

• Length: 2,000–3,000 words (excluding references and appendices).

• Format: PDF, 12 pt font, 1.5 line spacing, APA 7th edition referencing.

• Submission: Submit via the course portal by the Week 7 deadline.

3. What to Present & Write

Your proposal is a plan for the final group project. It should convince the audience that your team has a

clear, important, and feasible research idea. Your research question and data must be related to social

media marketing. The proposal should cover the following components:

Research Question.

State your research question clearly. Explain what you aim to investigate and why it

matters to marketing theory and/or practice. The question should be specific enough to be answerable

within one semester, and it must be grounded in a social media marketing context.

Importance and Motivation.

Why does this question matter? Provide motivation from industry trends,

real-world cases, or gaps in existing knowledge. Connect the question to broader themes in social media

marketing.

Literature Review.

Summarise the most relevant academic literature (minimum 8–10 sources).

Citations must be drawn primarily from top-tier research journals in marketing, specifically journals on

the FT50 (Financial Times Top 50) and UTD (UT Dallas Top 100) lists — for example, Journal of

Marketing, Journal of Marketing Research, Marketing Science, Journal of Consumer Research,

Management Science, and Information Systems Research. Identify the gap your project will address and

explain how your work contributes to the field. All citations will be verified for authenticity and journal

quality. Papers from non-ranked journals, predatory journals, or fabricated references will result in point

deductions.

Data Collection Plan.

Describe your social media data sources (e.g., platform APIs, web scraping,

existing datasets). Specify the type of data you will collect, the platform(s), time period, sample size, and

any ethical considerations (e.g., privacy, platform terms of service). Your data should come from or

relate to social media platforms.

Analytical Methods (Preliminary Thinking).

At this stage, you are not expected to have finalised your

analytical approach — the causal inference methods (A/B testing, PSM, DID, RDD, IV, Synthetic Control)

will be covered in detail from Weeks 8 to 11. However, you should begin thinking about what type of

method you might need to answer your research question. Importantly, your question should require a

causal answer, not merely a correlational one (see Section 4 below). Note: the methods section will not

be evaluated in this proposal. You will have the opportunity to refine and finalise your methodology

after completing the causal inference modules.

Feasibility Evaluation.

Provide a realistic assessment of your project's feasibility. This section should

include: (1) Key difficulties and challenges you anticipate (e.g., data access limitations, sample size

constraints, identification issues, technical skills required). (2) An estimated time budget — how many

total hours do you expect the project to require, and how will the workload be distributed across team

members? (3) A week-by-week project timetable from Week 7 to Week 13, specifying what tasks will be

completed each week, who is responsible, and the expected deliverables. A well-thought-out timetable

demonstrates that your team understands the scope of the project and has a concrete plan to deliver on

time.

Example Timetable Format:

Week Tasks Responsible Est. Hours

7 Submit proposal;

incorporate feedback

All —

8 Begin data collection; set

up API access

Member A & B 10

9 Complete data collection;

clean & preprocess

Member A & B 12

10 Exploratory analysis;

finalise causal method

Member C & D 10

11 Run causal analysis;

robustness checks

All 15

12 Write report; prepare

presentation

All 15

13 Final presentation &

submission

All 8

4. Thinking Causally, Not Correlationally

A central goal of this course is to move beyond correlational analysis and develop the ability to make

causal claims about social media phenomena. Understanding the difference is critical for designing your

project.

A correlational finding tells you that two things tend to move together. For example, you might observe

that brands with more Instagram followers also have higher sales. But does having more followers

actually cause higher sales? Or do successful brands simply attract more followers? Perhaps a third

factor — such as advertising spend — drives both. Correlation alone cannot distinguish between these

explanations.

A causal finding, by contrast, identifies whether one variable actually produces a change in another,

holding all else equal. Establishing causation requires a credible research design that addresses

confounding factors — for example, exploiting a natural experiment, matching treated and control units

on observable characteristics, or leveraging a sharp discontinuity in treatment assignment. The methods

you will learn in Weeks 8–11 (A/B testing, propensity score matching, difference-in-differences,

regression discontinuity, instrumental variables, and synthetic control) are all designed to help you

move from "X and Y are correlated" to "X causes Y."

When formulating your research question, ask yourself: "Am I trying to show that X causes Y, or merely

that X and Y are associated?" Your project should aim for the former. Even at the proposal stage, you

should be thinking about what makes a convincing causal argument — even if you have not yet decided

on the specific method.

5. Connecting to Course Content

Your project should integrate knowledge from across the semester. The table below maps course topics

to potential project elements:

Weeks Topics Covered Relevance to Your Project

1–2 Data Collection & Preprocessing Foundations for working with social

media data — text cleaning, data

structures, quality checks

3–5 SEO, Keyword Strategy, Platform

Algorithms, Google Ads API

Research contexts — search

behaviour, content optimisation,

ad performance; API access for

programmatic data collection

(introduced in Week 5)

6–7 GEO, AI Search, Content Strategy Emerging topics — AI-generated

content, zero-click search, brand

reputation in AI search

8–10 A/B Testing, PSM, DID, RDD, IV,

Synthetic Control

Your analytical toolkit — causal

inference methods to be covered

after the proposal deadline

11–12 KOL/Influencer Analysis, Social

Networks

Research contexts — influencer

effectiveness, network effects, fake

KOL detection

6. Example Research Directions

The following are illustrative examples to inspire your thinking. You are encouraged to develop your

own original question. Note how each example frames the question causally rather than correlationally.

• Does influencer-generated content causally increase brand search volume? (e.g., DID around

campaign launch dates)

• What is the causal effect of Google AI Overviews on organic click-through rates for e-commerce

brands? (e.g., RDD on rollout thresholds)

• Do TikTok viral moments lead to sustained SEO gains or only temporary traffic spikes? (e.g.,

Synthetic control)

• Does responding to negative reviews on social media improve subsequent ratings? (e.g., PSM on

response vs. no-response)

• What is the incremental effect of KOL endorsements on product sales, controlling for selection bias?

(e.g., IV or PSM)

7. Evaluation Criteria

Important: Analytical Methods carries 0% weight in the proposal assessment. Since causal inference

methods will be taught from Weeks 8 to 11, you are not expected to have a finalised methodology at

this stage. Focus your effort on the research question, motivation, literature review, data collection

plan, and feasibility evaluation.

Component What We're Looking For Weight (%)

Research Question Clarity, originality, and relevance to

social media marketing

theory/practice

25%

Importance and Motivation Justification of why the question

matters, supported by industry or

theoretical reasoning

15%

Literature Review Use of top-tier FT50/UTD journal

sources; clear gap identification;

contribution to knowledge. All

citations will be checked for

authenticity and journal quality —

non-ranked or fabricated sources

will be penalised.

15%

Data Collection Plan Clear description of social media

data sources, methods, feasibility,

and ethical considerations

20%

Analytical Methods Preliminary thinking about causal

(not correlational) approach — not

graded, for feedback only

0%

Feasibility Evaluation Identification of key difficulties,

realistic time estimates, and a

detailed week-by-week project

timetable with task assignments

25%

8. Submission Checklist

• ☐

YouTube video uploaded as Unlisted; link submitted via course portal

• ☐

Written report submitted as PDF via course portal

• ☐

All team members credited and contributed to the presentation

• ☐

References formatted in APA 7th edition

• ☐

Video is 10–15 minutes in length

• ☐

Research question and data are social media marketing related

• ☐

Feasibility section includes difficulties, time estimates, and a week-by-week timetable

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