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COMP90042 Rumour Detection and Analysis on Twitter
创建日期：2024-05-06 14:46:06
标签： COMP90042
Rumour Detection and Analysis on Twitter

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COMP90042 Project 

Rumour Detection and Analysis on Twitter
Project type: Group
Codalab submission due date: 1pm Fri, 13th May 2022 (no extensions possible for this component)
The concept of rumour has a long history, and it is typically defined as an unverified statement or news circulat-
ing from person to person. Rumours have the potential to spread quickly through social media, and bring about
significant economical and social impact. The figure below illustrates an example of a rumour propagating on
Twitter. The source message (green box) started a claim about the cause of Michael Brown’s shooting, and it was
published shortly after the shooting happened. It claimed that he was shot ten times by the police for stealing
candy. The message was retweeted by multiple users on Twitter, and within 24 hours there were about 900K
users involved, either by reposting, commenting, or questioning the original source message. From the replies
we can see some users (e.g. User 7; red box) questioned the truthfulness of the original message.
0 hour
4 hours
8 hours
12 hours
16 hours
20 hours
24 hours
This is unbelievable,
or should be.
User 2
Follower Count: 3144
No excuse.
User 1
Follower Count: 1222
It applies to
Black people.
User 3
Follower Count: 6
These days anything,
especially with Stand Your
Ground and even a sneeze
is punishable by death.
User 5
Follower Count: 6632
Anything is punishable by
death if the youth is black.
User 8
Follower Count: 11197
apparently it is
now.
User 4
Follower Count: 205
He was 18. Nothing to do with
stealing candy. He was walking
in the street. Horrible situation.
But stop spreading false facts.
User 7
Follower Count: 122
17 year old unarmed kid shot
ten times by police for stealing
candy. I didn't know that was
punishable by death.
User 0
Follower Count: 873021
there has not been any
proof that he stole
candy. I guess skittles
has become a reason to
kill black teens.
User 6
Follower Count: 1141
I was just going off what I
read in the #ferguson tag
early last night. Wasn't any
real news out at that point.
User 0
Follower Count: 873021
The challenge of the project is to develop a rumour detection system and analyse the nature of rumours that are
being spread on Twitter. We will frame this using two tasks: rumour detection and rumour analysis.
Task 1: Rumour Detection
In this task, you will be provided with a set of tweet IDs for the source tweets (i.e. the first tweet that started
the story) and their replies (i.e. the comments we saw in the figure above), and each source tweet is labelled
as either a rumour or non-rumour. The task here is to use the Twitter API to crawl the tweet IDs to get
the tweet objects, and then build a binary classifier to classify whether a source tweet is a rumour or not. A
tweet object provides a variety of information, including the text of the tweet, information of the user who
made the tweet, when the post was created, etc (see “Datasets” section below for more information).
You’re free to explore any methods or machine learning models for building the binary classifier. To give
some ideas, we could model the source tweet and replies as a sequence of tweets using recurrent networks.
1
Alternatively we could also model them based on their propagation structure (like the tree structure of
comments we saw earlier) using recursive networks or graph networks. We might want to consider incor-
porating some user information, as it could provide hints to the trustworthiness of a user. While you are
permitted to use pretrained models or embeddings, you should only use the provided labelled tweets for
training the model, i.e. you should not find more labelled data beyond what we have provided. Whatever
methods or features you use, you must at least incorporate the tweet text in your model (we are doing an
NLP project, after all).
Task 2: Rumour Analysis
In this task, you will use your trained rumour classifier from the first task and apply it to unlabelled COVID-
19 tweets to detect rumours. Given the predicted rumours and non-rumours, the aim here is to perform
some analyses to understand the nature COVID-19 rumours and how they differ to their non-rumour coun-
terparts. We have provided a preliminary set of tweet IDs (of source tweets and their replies) related to
COVID-19 that you can use, but you’re free to search for more COVID-19 tweets to support your analyses
in this task.
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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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