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COMP20008 Elements of Data Processing
创建日期：2024-04-30 15:27:25
标签： COMP20008
Elements of Data Processing

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COMP20008 Elements of Data Processing

Assignment 1
Marks and due date
The assignment is worth 20 marks (20% of the subject grade) . 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.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A 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