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

学霸联盟