A2 - Writing Guide for
Social Media Analysis Report
CCB302, Digital Media Analytics, Assessment One
Due Date Refer to Canvas
Length 2000 words (maximum)
Weight 50%
Individual/Group Individual
Submission Items A report (Word or PDF)
A Tableau Workbook (Packaged Workbook in .twbx format)
The aim of the A2 project is to build on your skills working with larger social media metadata
datasets (from YouTube), and perform mixed methods analyses using industry-standard tools.
Students are asked to create visualisations in Tableau and Leximancer and prepare an analytical
report.
I mentioned this conference paper & video during the last lecture. It features a presentation by
QUT Digital Media Research Centre Professor Laureate Axel Bruns titled From Cable Niche to
Social Media Success: International Engagement with Sky News Australia's Brand of 'News' (AoIR 2021),
see https://snurb.info/node/2626. You may wish to view it if you discuss the finding that Sky
News Australia is the dominant content publisher on the Voice Referendum both before & after
Garma.
IMPORTANT NOTE
It is a requirement of the ethical clearance you have received in this unit that you do
not publish any identifiable information about your datasets in this assignment. What
you find through your analysis of the datasets should only be shared with the teaching
team and must not be disclosed elsewhere.
Making any aspect of your datasets public (such as posting on social media about it) is
in violation of your ethical clearance and will result in disciplinary action.
There are three steps to doing this assignment.
Step One: Read the A2 Project Brief (a separate document)
Read the A2 Project Brief carefully. It contains a list of questions related to metrics, tool used to
gather the social media metadata, the data dictionary, and other governance considerations.
The datasets you have been provided with contain YouTube video metadata. We are providing
you with more prompts than can be answered in a single report. It is up to you to decide which
2
questions to answer. You do not get more points for answering a greater number of questions.
Rather, you will be assessed on your in-depth analysis of the data and inclusion of relevant
citations.
There are prompts that can be answered easily, without creating visualisations. Others can be
answered using clear, simple visualisations (i.e., right choice of graph, legible axes, clear and legible
annotations).
Step Two: Analysis and Visualisations in Tableau
Youte
As in A1, this data was collected using Youte. a tool developed by the QUT Digital Observatory
that collects and tidies YouTube video metadata and comments using the YouTube API v.3.
Please see the Youte documentation to familiarise yourself with the tool, see
https://youte.readthedocs.io/en/latest/ You are not expected to install or run the tool for
this project. You should understand who produced the tools used for data extraction and cite
them accordingly. **
You should refer to the YouTube API Data Dictionary (see below). When writing, use the
field names and descriptions when talking about the metadata fields. For example, if
discussing the keywords associated with a video, you might say, ‘the tags used for this video
were X, Y and Z’ and list them in a table. **
Steps for Analysis
a) Join the datasets as you did for A1. This time, you have two sets of data, ‘before Garma’
and ‘after Garma’ to allow you to do some temporal analysis.
b) Review some of the comments in Tableau or a spreadsheet (that doesn’t truncate the
content) to get a sense of the data & insights you may wish to drill into.
c) You are to perform an analysis demonstrating your understanding of why digital analytics
matters, and how it is performed, this time using multi-method content analysis.
d) Then, respond to a selection of the questions below (‘Prompts for your analysis’).
e) Be creative and explore the data with an open mind, incorporating concepts discussed in
all the lectures, L1 – Why does digital analytics matter? thru L6 – Bringing it all together).
f) You may focus on YouTube and why its participatory culture, (YouTube’s core business
discussed in Lecture #4).
g) Alternatively, you may choose to drill down to the dynamics of how various publishers used
YouTube during a key inflection point in modern Australian history to hold the Voice
Referendum. Consider the stance/positionality of the publishers (conservative through
progressive), funding model (privately held or publicly funded). Did they increase or
decrease the publications on YouTube before or after the Garma Festival. **
3
h) Discuss insights the data reveals. You are intended to show an understanding of the
concepts & readings discussed in CCB302, and background knowledge you have as a
Communication major.
NOTE: This is not intended to be a social or political critique, but rather an analysis
based on the data.
Visualisations in Tableau & Leximancer
To support your findings, you are to produce:
4 (minimum) visualisations using Tableau, and
2 (minimum) using Leximancer. There is no set maximum.
In each visualisation, the goal is to obtain insights about the dataset, Some of the prompts can be
answered without visualisations (e.g. simple counts). In such cases, you can include this
information in the report and supplement it with visualisations that answer other relevant
questions.
Make sure to make your visualisations as communicative and expressive as possible. This involves
the use of legible axes and labels, colours, labels, and annotations. Try to use a variety of
visualisation types (e.g., line, bar chart(s), tables) to highlight your insights.
The data set has no geospatial information, so maps are not required for this assignment. However,
you will note that not all the publishers are based in Australia. A simple map plotting the
readership of the publishers would make for a relevant visualisation. You can create this dataset
by cross-referencing publications & countries.
Step Three: Report Writing
Whatever approach you adopt, introduce your topic, describe your approach, and detail
insights from the data. Include references to scholarship in the course’s reading list. Your
References section (as the final page of your report) should include four to six sources and include
readings for this unit.
Write a report of your findings for the client and discuss the insights you have obtained from the
analysis of the dataset. Answer the prompts in a professionally written report, including
visualisations. Go beyond mere description. You should synthesize the findings and engage in
qualitative analysis where possible.
You can structure your report in any way that you deem fit. Here is one possible structure:
Executive Summary [~250 words]
Briefly, tell the reader what the report is about, including an overview of its main findings. The
introduction/executive summary often includes information that will help the reader quickly
familiarise themselves with the topic and understand the main points and information before
reading the whole document.
4
Main Body (including any relevant sub-headings, e.g., Introduction, Analysis & Findings)
[~1200 words]
Write the main body of the report, and if appropriate, use sub-headings. In the body of the report,
the task is to break down, interpret, and critically evaluate the insights for the client, in order to
help them understand the data, and to make decisions.
Avoid being too descriptive, and instead, provide critical insights. Just as an example:
Descriptive: “Sky News Australia was the greatest publisher of Voice Referendum
content before and after the Garma Festival.”
This sentence alone will not give the reader much insight. They can see the peaks and troughs in
the visualisation. Instead, find out why there was a peak, what happened next, and what it tells the
readers. Interpret it for them.
Interpretive/Critical: “Sky News Australia is operated by a controversial conservative
media magnate Rupert Murdoch and News Corporation Limited, a multinational mass
media conglomerate. According to Copeland, Bruns & Graham (2021), Sky News
Australia has promoted a bifurcated strategy to flood media channels with ordinary
news during the daytime hours and transition to conservative commentators by night.
YouTube’s characteristics of being an open, anyone can publish platform, supported
Sky News Australia’s strategy and tends to support viewpoints favouring conservative
parties, including the Liberal and National parties (Stapleton, 2019). The A2 datasets
suggest that Sky News Australia sees YouTube useful platform to make content
appealing to their target audience 7x24.
Be logical, coherent and concise. Find a narrative in the data and write your report around that
narrative.
When presenting the report, include screenshots of your Tableau worksheets in the body of the
text, to make it easier for the client to follow the logical structure of the report. Professional
reports often include visualisations to draw the reader’s attention to key points and insights.
Implications/Limitations/Considerations [~250 words] **
This section briefly explains any implications, limitations, or ethical considerations involved in the
analysis. How much data did you analyse? Was it one or multiple videos? Is this a representative
sample for the object of study? Would more or less data have been useful? Is so, why? What
additional data would have made your analysis more robust or thorough? Are there ethical
considerations that your client needs to address? Will their treatment of the dataset respect the
privacy of users? Are there any social justice issues to consider (e.g. bias and discrimination)? How
could they minimise harm and maximise public good? Are there any legal considerations involved?
Does their approach respect YouTube’s Terms of Service?
5
Conclusions/Future Directions/Recommendations [~200 words] **
Briefly summarise the key insights and point to the future directions the client can take. Are there
any other approaches, data sources, platforms, or analytical methods that could be used to
strengthen this report? Do you have any recommendations for them?
SUBMISSION INFORMATION
What You Need to Submit
You need to submit two items:
- Your Written Report (Word or PDF)
- Your Tableau workbook
o VERY IMPORTANT: Submit your Tableau workbook in the .twbx format
(Packaged Workbook)
- Please use the following naming convention for your submissions:
o SURNAME firstname A2 essay.[docx or .pdf]
o SURNAME firstname A2 graphs.twbx
- For example, my submission would be named:
o HYLAND-WOOD bernadette A2 essay.pdf
o HYLAND-WOOD bernadette A2 graphs.twbx
NOTE: On submission, you are declaring that, unless otherwise acknowledged, this submission
is wholly your work and it has not been used and already submitted. If you use an LLM to help
collect or synthesise information, such as ChatGPT, you must comply with QUT’s Guidelines on
the use of AI for your submitted work.1 You understand that this work may be submitted for
plagiarism check and consent to this taking place.
Moderation
All staff who are assessing your work meet to discuss and compare their judgements before marks
or grades are finalised. Refer to MOPP C/5.1.7.
Academic Integrity
As a student of the QUT academic community, you are asked to uphold the principles of academic
integrity during your course of study. QUT sets expectations and responsibilities of students
specifically stating that students “adopt an ethical approach to academic work and assessment in
accordance with this policy and the Student Code of Conduct (E/2.1)". Students need to be aware
that academic integrity refers to text and non-text sources, i.e. "copying or adapting non-text based
material created by others, such as diagrams, designs, musical score, audio-visual materials, art
work, plans, code or photographs without appropriate acknowledgement" (MOPP C/5.3.6
Academic Integrity). It also includes self-plagiarism, this “involves the re-use by a student of their
own work without appropriate acknowledgement of the source. Students should seek express
1 QUT Library Guide on Using and Referencing AI, see
https://libguides.library.qut.edu.au/c.php?g=958007&p=6958701
6
consent from the unit coordinator prior to re-using their own work in an assessment submission"
(MOPP C/5.3.6 Academic Integrity).
Students are expected to demonstrate their own understanding and thinking using ideas provided
by ‘others’ to support and inform their work, always acknowledging the source. While we
encourage peer learning, it is not appropriate to share assignments with other students unless your
assessment piece has been stated as being a group assignment. If you do share your assignment
with another student, and they copy all or part of your assignment for their submission, this is
considered collusion and you may be reported for academic misconduct. If you are unsure and
need more information, please refer to:
http://www.mopp.qut.edu.au/C/C_05_03.jsp#C_05_03.03.mdoc
7
CCB302 ASSESSMENT ONE MARKING CRITERIA
Criteria High Distinction Distinction Credit Pass Marginal Fail Fail
TABLEAU (40%)
Analytical
approach
(20%)
Highly appropriate, creative,
and innovative use of various
chart types, visualisations, and
analytical tools in Tableau
Error-free treatment of data
fields, regarding their use in
Tableau and their interpretive
implications
Metrics selected and analysed
appropriately and sufficiently
answer a high range of client’s
questions
Appropriate and considerate
use of metrics, such as user,
temporal, comparative, and
combination metrics
Appropriate, creative, and
innovative use of various chart
types, visualisations, and
analytical tools in Tableau
Appropriate treatment of data
fields regarding their use in
Tableau and their interpretive
implications, with minor
inaccuracies that do not impact
interpretations
Metrics selected and analysed
sufficiently answer many of
client’s questions
Appropriate use of metrics,
such as user, temporal,
comparative, and combination
metrics
Some creative and innovative
use of various chart types,
visualization, and analytical
tools in Tableau
Appropriate treatment of data
fields, but with some
inaccuracies which impact their
interpretive implications
Metrics selected and analysed
answer some of client’s
questions, but some
opportunities missed
A good range of metrics used,
but some opportunities for
comparative and/or
combination metrics missed
Visualisations use basic chart
types and analytical tools in
Tableau, and/or don’t use an
appropriate variety of chart
types or approaches
Data fields properly selected,
but with major inaccuracies
which negatively impact the
interpretive implications of
visualisations
Metrics selected and analysed
answer some of client’s
questions, but remain at
descriptive, non-critical levels
Simple metrics used to create
visualisations, but many
opportunities for comparative
and/or combination metrics
missed
Visualisations use basic chart
types and analytical tools in
Tableau, with frequent use of
similar chart types
Data fields not properly
selected, and interpretive
implications of visualisations is
not clear
Metrics selected and analysed
answer few of client’s
questions, and remain at
descriptive, non-critical levels
Simple metrics used to create
visualisations, and minimum
number of visualisations not
created
No evidence
of learning
in this
criterion
Technical
approach
(20%)
Highly expressive, rich, and
communicative visualisations
Highly appropriate use of
visual elements in Tableau to
make visualisations aesthetically
pleasing and easy to digest
Visualisations are labelled,
marked, and/or annotated
appropriately
Considerate and thoughtful
number of visualisations have
been created to properly
answer a good range of client’s
questions
Expressive, rich, and
communicative visualisations
Appropriate use of visual
elements in Tableau to make
visualisations aesthetically
pleasing and easy to digest,
with minor inaccuracies that
cause difficulty in
understanding the visualisations
Visualisations are labelled,
marked, and/or annotated
appropriately, with only minor
inaccuracies
An appropriate number of
visualisations have been created
to properly answer a good
range of client’s questions
Generally expressive
visualisations, but some
analytical potentials not fully
operationalized
Visualisations are aesthetically
pleasing, but some are more
difficult to understand due to
inaccuracies or information
overload
Visualisations are properly
labelled, but some information
is missing or inaccurately
marked, labelled, or annotated
A good range of visualisations
have been created to answer
client’s questions, but some
could be condensed or
combined
Visualisations are
communicative, but show
minimal use of Tableau’s
analytical and visual potentials
Visualisations are aesthetically
pleasing, but do not explore
potentials beyond basic
functions in Tableau
Visualisations are minimally
labelled, marked, or annotated
A range of visualisations have
been created to answer client’s
questions, but majority remain
at basic visualization elements
Visualisations are not
communicative and show
minimal use of Tableau’s
analytical and visual potentials
Visualisations remain at default
options in Tableau
Visualisations are not labelled,
marked, or annotated
Visualisations cannot answer
client’s questions, and remain
at basic visual elements
No evidence
of learning
in this
criterion
8
REPORT (60%)
Analytical
aspects
(50%)
Provides a logically structured,
highly critical, and in-depth
report of findings
The report goes beyond mere
visual analysis, and engages in
appropriate, context-aware,
qualitative examination of
datasets.
The findings are interpreted
and digested accurately and
professionally
Report shows a high awareness
of ethical implications,
interpretive limitations and
considerations, and/or data-
driven recommendations for
the client
Provides a logically structured,
critical, and in-depth report of
findings
The report goes beyond mere
visual analysis, and engages in
appropriate qualitative
examination, with minor
inaccuracies in interpreting the
role of context
Interpretation of findings is
accurate, with minor omissions
of some key issues/topics
High awareness of ethical,
practical, or interpretive
considerations, with minor
omission of key factors
Generally logical and well
structured, with some good
depth. Some parts are more
descriptive, and lack critique
The report is mainly based on
the visual analysis, with some
inaccuracies in interpretation of
findings
The findings are interpreted to
some extent, but with omission
of some key issues/topics.
Some awareness of ethical,
practical, or interpretive
considerations, with some
omission of key factors
Report has an acceptable
structure, but remains at a
descriptive level
The report is predominantly
based on the visual analysis,
with major inaccuracies in
interpretation of findings.
The findings are not
interpreted at great depth, and
remain mainly at descriptive
levels, with some omission of
key issues/topics
The report shows little
awareness of ethical, practical,
or interpretive considerations.
Report does not have a clear
connection with the Tableau
analysis, and remains at a
general, descriptive level
The report is solely based on
the visual analysis, and does not
interpret the findings
The report does not show
awareness of ethical, practical,
or interpretive considerations
No evidence
of learning
in this
criterion
Technical
aspects
(10%)
The report shows industry-
standard level of professional
writing
Report shows proper proof
reading and accurate,
professional use of language
The report adheres by the word
limits.
The report shows industry-
standard level of professional
writing
Report shows proper proof
reading and accurate,
professional use of language,
with only minor inaccuracies
The report adheres by the word
limits.
The report shows a good level
of professional writing
Report shows proper proof
reading and accurate,
professional use of language,
with some inaccuracies
The report is slightly above the
word limits
The report is well written, but
is not at industry-standard level
Report shows lack of proper
proof reading, with several
inaccuracies in language use
The report exceeds word limits
by a large margin
The report does not show an
acceptable standard of writing
Report does not show proper
proof reading
The report is substantially
over/under word limits
No evidence
of learning
in this
criterion