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COMP20008 Elements of Data Processing
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标签： COMP20008
Elements of Data Processing

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COMP20008 Elements of Data Processing

Assignment 
Marks and due date
The assignment is worth 20 marks (20% of the subject grade) and is due on at
6pm, Wednesday 13 April 2022 (Melbourne time). The estimated time commit-
ment 20 – 30 hours.
Submission instructions
Please see Ed.
Background
It should be no surprise to us that while news sources provide most of the in-
formation we learn every day, it is questionable whether the news we read is
credible and trustworthy. Differentiating legitimate and correct news articles
from ‘fake’ or misleading ones is an important task, raising the interest of jour-
nalists, policy makers and researchers in many fields such as social science and
data science. Specifically in the context of health, the consequences of delivering
illegitimate news can be paramount and irreversible.
In this assignment, you will be working on a dataset comprising more than
1,000 health-related news articles, the reviews of these articles, and tweets about
the new articles.
Using this dataset, you will have an opportunity to learn how to identify
‘good’ and ‘bad’ news by characterising some linguistic patterns of news, as
well as comparing different news outlets based on their correctness of reporting.
Learning outcomes
• Be able to read and write data in different formats, and combine different
data sources.
• Practice text processing techniques using Python.
• Practice data processing, exploration and visualisation techniques with
Python.
• Practice visual analysis and written communication skills.
1
Dataset
The dataset consists of more than 1,000 health-related news articles, the reviews
of these articles, and tweets about the new articles. They are located in the
folder /course/data/a1/.
News articles
Each of the news articles is stored in a file in the /course/data/a1/content
folder. The name of each file corresponds to the ID of the article and is in
the format story_reviews_xxxxx.json, where each x is a digit; for example,
story_reviews_00001.json contains the news article of ID story_reviews_00001.
News reviews
Each article was independently reviewed by at least one expert, based on a list of
criteria. Against each criterion, the article receives a rating of satisfactory, not
satisfactory or not applicable, and an explanation for the rating. Each article is
also assigned an overall rating between 0 (least accurate) to 5 (most accurate).
All reviews are stored in a single file: /course/data/a1/reviews/HealthStory.json.
If you read it using Python’s json, you should see a list of reviews.
Tweets
News articles are shared on Twitter. The IDs of Tweets about the news articles
have been recorded in /course/data/a1/engagements/HealthStory.json. You
should get a dictionary with keys being article IDs, and values containing tweets.
Each tweet is represented by a unique integer. Note that a tweet may appear
multiple times, so if you count tweets, you need to avoid counting duplicates.
Your tasks (Total 20 marks)
Task 1. Loading data (1 mark)
Implement the function task1() in task1.py that outputs a json file called
task1.json in the following format:
{
"Total number of articles": X,
"Total number of reviews": Y,
"Total number of tweets": Z
}
where X, Y and Z are the number of news articles, number of reviews, and number
of tweets respectively.
NOTE: You are free to write any helper functions (even in another file) if
you like, but do not change the function definition of task1(). For example, do
not add any argument to task1(). This applies to all tasks.
To run your solution, open the terminal and run python main.py task1
2
Task 2. Data aggregation (2 marks)
In each review, you will see a news_id field containing the ID of the article.
This information allows you to match the review with the news article.
Implement the function task2() in task2.py which combines the articles
with their reviews to work out how many “satisfactory” ratings that article
receives, out of 10 criteria in total. Your function should save its output to
a csv file called task2.csv, which contains the following headings: news_id,
news_title, review_title, rating, num_satisfactory. Each row in the file
should contain the details of one article, with
• news_id being the ID of the news article in the format story_reviews_xxxxx;
• news_title being the title of the news article;
• review_title being the title of the review article;
• rating being the overall rating of the article (between 0 and 5); and
• num_satisfactory being the total number of criteria (between 0 and 10)
that are satisfactory.
The rows in task2.csv should be in ascending order of news_id.
To run your solution, open the terminal and run python main.py task2
Task 3. Extracting meta-data (2 marks)
Each news article comes with a publish_date field, which specifies when the
article was published. The field is in the millisecond precision format, which
is a floating point number. Convert it into a more readable format using the
datetime.datetime.fromtimestamp() function. It should return you an ob-
ject of type datetime, and you will need to consult the documentation to extract
the year, month and date of the article.
Implement the function task3() in task3.py that performs the following
two sub tasks:
• Extract the year, month, and day components from the publish_date of
each article, and output a csv file called task3a.csv, which contains the
following headings: news_id, year, month, day. Each row should contain
the ID of an article in the format story_reviews_xxxxx, and the year,
month, day on which it was published. The formats for year, month,
and day are 4-digit year, 2-digit month, and 2-digit day of the month. For
example, for the date 17 April 2022, the year, month, and day components
are 2022, 04, and 17 respectively.
The rows in task3a.csv should be in ascending order of news_id.
• Count the number of articles in each calendar year in the dataset, and
output a file called task3b.png, describing the yearly article counts using
an appropriately chosen graph.
There might be articles for which the publish date is unspecified; exclude
those articles in your output. Also note that you will likely write helper functions
for this task; however, as always, do not change the definition of task3().
To run your solution, open the terminal and run python main.py task3
3
Task 4. Assessing the credibility of news agencies (2 marks)
Are news article published by The New York Times more credible than those
by Fox News, according to the judgement of experts? Recall that each article is
given an overall rating, which is a score between 0 and 5 (inclusive) indicating
the credibility of the information presented in the article. In this task, you will
compare the average rating of articles published by each news source.
In each review, you will see a news_source field, which tells you the where
the article was published. Some reviews leave this field as blank (an empty
string), in which case you can exclude them.
Implement the function task4() in task4.py that performs two sub tasks:
• Output a csv file called task4a.csv, which contains the following head-
ings: news_source, num_articles, avg_rating. Each row should contain
details about one news source.
The rows in task4a.csv in ascending order of news_source.
• Output a file called task4b.png comparing the average ratings of all news
sources, that have at least 5 articles. Choose an appropriate plot for this
task. Also, sort the axes so that the most and least credible news sources
are easily detected.
To run your solution, open the terminal and run python main.py task4
Task 5. Assessing credibility against popularity (1 mark)
Before doing this task, ask yourself some of these questions. Do people tend
to share more accurate, credible news than less credible ones on social media?
Why does “fake news” propagate much faster online (that is, why do people
share much more fake news )? In Tasks 5, 6 and 7, you will perform analyses
that give you some insights to these questions.
Implement the function task5() in task5.py which outputs a file called
task5.png comparing the average number of tweets for each article-rating group.
For example, for an article with a rating of 0, how many tweets does it have on
average? Note that the tweets here include both original tweets and retweets.
To run your solution, open the terminal and run python main.py task5
Task 6. Text processing (2 marks)
News articles often have some characteristics that make them difficult to process;
for example, they might contain unicode characters and even emojis. To make
our analysis easier, in this task you will perform some preprocessing on the
articles.
Implement the function task6() in task6.py that performs the following
pre-processing steps on the content of the news articles: The content of an
article is in the text field.
1. Convert all non-alphabetic characters (for example, numbers, apostrophes
and punctuation), except for spacing characters (for example, whitespaces,
tabs and newlines) to single-space characters. For example, ‘&’ should be
converted to ‘ ’.
4
2. Convert all spacing characters such as tabs and newlines into single-space
characters, and ensure that only one whitespace character exists between
each token.
3. Change all uppercase characters to lowercase.
4. Tokenise the resulting string into a list of words, using the space delimiter.
5. Remove all stop words in nltk’s list of English stop words from the re-
sulting list. You may find the function filter in Python useful, but this
is not necessary.
6. Remove all remaining words that are only a single character long from the
resulting list.
7. Once steps 1 – 6 are done, build a vocabulary of distinct words as a JSON
object and output it to a file called task6.json. Each key is a word and
the value is an array of the news_id of articles containing that word. The
entries in task6.json should be in ascending order of word and the list of
articles for each word should also be in ascending order of news_id. The
format of each key-value pair of the JSON object should be
"word": ["story_reviews_xxxxx", "story_reviews_yyyyy"]
8. The creation of vocabulary should be implemented reasonably efficiently.
The run time of task 6 should be no more than 45 seconds. Excessively
long execution time for this task will result in a deduction of 1 mark. For
example, using the re package is likely to be more efficient than writing
for loops.
Note that you might want to use an output from a previous task for conve-
nience, but this is not required.
To run your solution, open the terminal and run python main.py task6
Task 7. Detecting most indicative words of fake news (4
marks)
What is fake news? What is real news?
In the context of this dataset, an article is considered real or legitimate if its
rating is at least 3. On the other hand, a fake news article has a rating below 3.
Based on this, you should be able to separate the articles into two collections:
• Fake news: articles with a rating below 3; and
• Real news: articles with a rating of 3 or higher.
Odds ratio
In this task we will use the vocabulary from Task 6 to discover which words are
the most indicative of fake news.
5
First, define the probability that a word w in our vocabulary appears in a
real news article as
pr(w) =
# real articles containing word w
# real articles
.
Further, the odds that a word w appears in a real article is defined as
or(w) =
pr(w)
1− pr(w) .
If you have done some probability, odds are an equivalent way of presenting a
probability. Odds essentially compare the number of events that produce an
outcome against the number that do not: the higher the probability, the higher
the odds. An odds value greater than 1 indicates that the word w is more likely
to appear in a real news article than it is absent.
Similarly, we define the pf (w) and of (w) for each w in fake news articles.
Note that in your calculation, to avoid odds of 0 or infinity, you can exclude
words w such that pr(w) ∈ {0.0, 1.0} or pf (w) ∈ {0.0, 1.0}.
Now, for each word w in the vocabulary, we have two values: or(w) and
of (w). Define the odds ratio for fake news to be
or(w) =
of (w)
or(w)
.
This ratio compares the odds that w appears in a fake article against the odds
that w appears in a real article. A fraction less than 1 indicates w is more likely
to appear in a real article; a value greater than 1 indicates w is more likely to
appear in a fake article; and a value of exactly 1 indicates w appears equally
frequently.
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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 MAS316/414 ACCT2522 MSIN0181 ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 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