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CCB302 – Digital Media Analytics
Creation date：2024-06-04 16:32:00
Belonging category：计算机computer science
Tag CCB302
Digital Media Analytics

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CCB302 – Digital Media Analytics
Assessment 2: Social Media Analysis Brief on the Voice
Referendum
Document history
CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 2
Introduction CCB302 students are being asked to perform an analysis of real-world data collected over a period of time. The topic is the 2023 Voice Referendum.
As part of this assignment, you are encouraged to review the upcoming Referendum
in which all eligible Australian voters must vote on 14-Oct 2023.1 Note: For over a decade, the Voice Referendum has been supported by governments led by both Australian Coalition and Labor governments. More recently, the issue has become politicised by the Opposition and several prominent Aboriginal politicians. Many Australians consider the Referendum a human rights issue. You may find that contemporaneous issues such as an investigation of Australia’s national airline, Qantas, has been woven into the Voice Referendum discussion. This may inform part of your analysis.
NOTE: This is not a hypothetical case. We are analysing real world data on the
upcoming Voice Referendum.
For this project, we are analysing real-word data that is deeply personal for many people, first
& foremost, Australia’s First Peoples. The datasets contain emotional, and sometimes
hyperbolic content. As digital communication students and soon communication
professionals, this reflects the type of analysis one does in the ‘real world’ and it can be
disturbing. If it raises any serious concerns, please contact QUT Student Counselling
immediately.2
Background and methods In this exercise, you are provided four data sets with video and comments data, similar to Assignment 1; however, instead of a single video (on AI and healthcare), students will have four files to facilitate temporal analysis (before & after Garma). We will discuss how to do this in tutorials, or you are encouraged to experiment on your own using what you learned about JOINING datasets earlier this semester (for A1).
1 Aboriginal and Torres Strait Island Voice. The Voice Referendum will be held on 14-Oct 2023, and
Australians will have a say about whether to change the Australian Constitution to recognise First

CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 3
To help you get started, we have provided preliminary metrics on the YouTube data. As you begin your own analysis, you may ‘sanity check’ your initial findings in terms of number of unique videos, dates and channels posting content against our initial analysis. If there are discrepancies, seek to understand why and explain them. This is raw real-world data, so there will be inconsistencies and missing data (i.e., videos, comments, and users who are missing or have been deleted). That is usual and expected; you are welcome to identify that in your analysis. There are no intentional ‘red herrings’. We want you to explore the data with an open mind and surface insights. And remember, social media
content is ephemeral! The data were retrieved from YouTube Data API using the QUT Digital Observatory’s tool call ‘Youte’. You have previously received information about this tool and encouraged to review the lectures and URLs provided to refresh your memory on how it works. You are being asked to perform mixed methods analyses using YouTube metadata. Textual analysis is done using Leximancer. Quantitative and qualitative data can be plotted using Tableau. You are welcome to incorporate other tools you may be familiar with, however, you must use Leximancer and Tableau at a minimum. The matching criteria was as follows: • “voice referendum australia” • published before 19 June 2023 • This date is before the 2023 Garma Festival of Traditional Cultures, Australia’s largest Indigenous cultural gathering taking place in early August in northeast Arnhem Land.3 These datasets are called “before Garma”. • We have collected data after the Garma Festival, between 1 and 15 August 2023, called “after Garma.”
3 The Garma Festival of Traditional Cultures (Garma) is Australia's largest Indigenous cultural
gathering, taking place over four days each August in northeast Arnhem Land, in the Northern Territory,
Australia. Hosted by the Yothu Yindi Foundation, Garma is a celebration of the cultural traditions of
the Yolngu people, and a major community gathering for the clans and families of the Arnhem Land
region. The event showcases traditional miny'tji (art), ancient story-telling, manikay (song),
and bunggul (dance). It is held at Gulkula, a significant Gumatj ceremonial site about 40 kilometres
(25 mi) from the township of Nhulunbuy, attracts more than 2500 guests each year and is often sold out
months in advance.
In recent years, Garma has become an important fixture on the political calendar, attracting business,
political, academic, and philanthropic leaders to help shape Indigenous affairs policy through the Key
Forum conference. 
Retrieved 09-09-2023
CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 4
Preliminary Analysis - Before Garma Festival of Traditional Cultures
Video stats before Garma • Number of videos: 351 • Earliest matching video: 2017-05-23 05:52:44 (this is an outlier that will need to be excluded) • Latest matching video: 2023-06-18 22:47:32
Video count by date
CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 5
CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 6
Comments • Total number of comments collected: 28,435
Video stats after Garma
Videos • Number of videos: 132 • Earliest matching video: 2023-08-01 04:57:59 • Latest matching video: 2023-08-14 15:10:49
Video count by date
CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 7
Comments • Total number of comments collected: 10,160
CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 8
Suggested prompts for your analysis (qualitative and quantitative) 1 Who are the channels publishing videos before & after Garma on this topic? Were they the same or different? 2 You can hone in on the top 5-10 publishers and interrogate information on the leading publishers. 3 Alternatively, you may wish to examine the ‘long tail’ of publishers of content and discuss that. 4 What is their geographic location? (Australia, overseas?) What can you deduce from the level of interest delivered by platforms in languages other than English? 5 How many followers do they have? 6 What categories do they publish? 7 Are they considered an authoritative source? 8 Consider the [political] alignment (conservative to progressive). 9 What is their geographic reach? (What is YouTube’s geographic reach?) 10 What is the timeframe of the post (original post & comments)? 11 How many people commented (total vs. distinct)? 12 What can be said about the nature of comments based on the metrics? 13 Is there any spamming, trolling, bot activity, or any indication of automated posting? 14 What other events were co-incident with this time period? 15 What is the sentiment of the videos published by the top 5 – 10 publishers. What can be said about the nature of comments? 16 What themes or topics surfaced before and after the Garma Festival (using textual analysis approach)? How do you know?
17 If you ‘tune’ Lexicmancer, be sure to keep careful track of what changes you made and
discuss them in the analysis. We will explore this in more detail during the remaining
tutorials. 18 What tool(s) was used to retrieve YouTube metadata? Who produced it? (hint: see https://youte.readthedocs.io/en/latest/). 19 What are the rate limitations of the YouTube API quote system?
CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 9
21 Are there other tools you could have used for gathering video data and statistics? If so, what is another tool you identified? 22 What are some of the limitations of analysing metadata from digital media platforms generally, or YouTube specifically? 23 What additional data would have made your analysis more robust and thorough? 24 What are some of the data governance considerations  25 Are there any other things you can tell that we didn’t think of? The above are some of the prompts you may wish to investigate. There may be other insights you find and you are encouraged to discuss them!
CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 10
YouTube API Data Dictionary
Video metadata
Field Description
kind Identifies the API resource’s type. The value will be
youtube#video.
id Unique identifier of the video, as provided by YouTube.
published_at The date and time that the video was published (in UTC
time).
channel_id Unique identifier of the channel this video belongs to, as
provided by Youtube.
title Title of the video.
description Full description of the video.
thumbnail_url URL of the video’s thumbnail.
thumbnail_width Width of the thumbnail image in pixels
thumbnail_height Height of the thumbnail image in pixels.
channel_tittle Title of the channel this video belongs to.
tags A list of keyword tags associated with the video.
category_id The YouTube video category associated with the video.
localized_title The localised video title for a specified language. Returns the default
language if no language is specified.
localized_description The localised video description for a specified language. Returns the
default language if no language is specified.
default_language The language of the text in video’s title and description.
default_audio_language The language spoken in the video’s default audio track.
CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 11
duration Length of the video in ISO 8601 duration (PT#M#S). The
letters PT indicate that the value specifies a period of time,
and the letters M and S refer to length in minutes and
seconds, respectively. For example, a value of PT15M33S
indicates that the video is 15 minutes and 33 seconds long. If
the video is at least one hour long, the duration is in the
format PT#H#M#S.
dimension Indicates whether the video is available in 3D or in 2D.
definition Indicates whether the video is available in high
definition (HD) or only in standard definition.
caption Indicates whether captions are available for the video.
licensed_content Indicates whether the video represents licensed content,
which means that the content was uploaded to a channel
linked to a YouTube content partner and then claimed by that
partner.
projection Specifies the projection format of the video (either 360 or
rectangular)
upload_status The status of the uploaded video.
privacy_status The video’s privacy status.
license The video’s license (e.g. creativeCommon or YouTube).
embeddable This value indicates whether the video can be
embedded on another website.
public_stats_viewable This value indicates whether the extended video statistics on
the video’s watch page are publicly viewable. By default,
those statistics are viewable, and statistics like a video’s
viewcount and ratings will still be publicly visible even if this
property’s value is set to false.
view_count The number of times the video has been viewed.
like_count The number of users who have indicated that they liked the
video.
comment_count The number of comments for the video. Will be empty if the
video disables comments.
topic_categories A list of Wikipedia URLs that provide a high-level
CCB302 – Digital Media Analytics - semester 2 2023 – A2 Project Brief 12
description of the video’s content.
live_streaming_* These values will only be available if the video is an
upcoming, live, or completed live broadcast.
meta_* Metadata of the youte version to collect data, time the data
was collected.
Comments metadata
Field Description
id Unique identifier of the comment, as provided by
YouTube.
video_id The ID of the video that the comment refers to.
Case category
Tag cloud
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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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