This document discusses detecting false rumors on social media by examining propagation structures. It proposes using a hybrid SVM classifier with a graph kernel and RBF kernel to classify rumors based on 23 features extracted from message propagation trees, including whether the message started from an opinion leader or normal user. An evaluation on a dataset of over 5000 messages from Sina Weibo found the classifier could accurately detect false rumors, especially early in their propagation.

1. False Rumors Detection
On Sina Weibo by propagation structures
ICDE 2015

2. Introduction
==
<microblogs>

3. Introduction
In this paper,
Rumor : unverified or unconfirmed statement or report circulating in a community
Not been spread around story is not a rumor!

4. Introduction

5. Introduction

6. Introduction
"Coke can dissolve a tooth overnight”

7. Introduction
"Coke can dissolve a tooth overnight”
False Rumor
Not false rumor

8. Let’s see…
Dirty Cat Eats Foul Fish: Global
warning! Please repost! You should
never crush a rove beetle if it lands
on your skin. Rove beetles contain
venom which kills people for sure if it
contacts skin! Tell your children and
friends that it's better to just gently
blow the rove beetle away if it lands
on your skin. Doctors specially warn
that if you come across a rove beetle,
never smash it with hands!
@AccessCity @CongXiongzhuang
@DLTVLivingChannel @hiliziWebsite
@NewYouthWeb
Wenzhou City News: {Don't crush this
beetle with hands when you see itl Rove
beetles have been discovered in a school
of Wenzhou. According to a doctor, rove
beetle doesn't bite human beings, but
once the venom in its body comes in
contact with human skin, it can quickly
cause blistering and festering, spreading to
other areas and getting worse if you
scratch it. If you ever touch the venom of
rove beetle, please clean the skin
repeatedly with soap water, 4% baking
soda solution or 10% ammonia, and seek
help at a local hospital immediately.

9. Let’s see…
Dirty Cat Eats Foul Fish: Global
warning! Please repost! You should
never crush a rove beetle if it lands
on your skin. Rove beetles contain
venom which kills people for sure if it
contacts skin! Tell your children and
friends that it's better to just gently
blow the rove beetle away if it lands
on your skin. Doctors specially warn
that if you come across a rove beetle,
never smash it with hands!
@AccessCity @CongXiongzhuang
@DLTVLivingChannel @hiliziWebsite
@NewYouthWeb
Wenzhou City News: {Don't crush this
beetle with hands when you see itl Rove
beetles have been discovered in a school
of Wenzhou. According to a doctor, rove
beetle doesn't bite human beings, but
once the venom in its body comes in
contact with human skin, it can quickly
cause blistering and festering, spreading to
other areas and getting worse if you
scratch it. If you ever touch the venom of
rove beetle, please clean the skin
repeatedly with soap water, 4% baking
soda solution or 10% ammonia, and seek
help at a local hospital immediately.
False rumor True rumor

10. SO
It’s Hard to detection ‘FALSE RUMOR’, with only looking text
Main IDEA of this paper
- False rumor can be identified not only by what the rumor says, but also look the
way people respond to it and who theses people are

11. Problem Def.
W is message propagation trees
𝑝𝑟𝑜𝑝𝑎𝑔𝑎𝑡𝑖𝑜𝑛 𝑡𝑟𝑒𝑒 𝑇 = < 𝑉, 𝐸 >
𝑉 𝑖𝑠 𝑠𝑒𝑡 𝑜𝑓 𝑚 (𝑝𝑜𝑠𝑡𝑒𝑑 𝑚𝑒𝑠𝑠𝑎𝑔𝑒)
𝑚 = < 𝑢[𝑐𝑟𝑒𝑎𝑡𝑒𝑟 𝑜𝑓 𝑚𝑒𝑠𝑠𝑎𝑔𝑒], 𝑡[𝑡𝑖𝑚𝑒 𝑠𝑡𝑎𝑚𝑝], 𝑐[𝑡𝑦𝑝𝑒 𝑜𝑓 𝑐𝑙𝑖𝑒𝑛𝑡], 𝑖[𝑜𝑝𝑡𝑖𝑜𝑛𝑎𝑙 𝑖𝑚𝑎𝑔𝑒𝑠] >

12. Problem Def.
𝑚
𝑇

13. Problem Def.
1. Divide user in to two types – opinion leaders || normal users
If [a > 1 && followers >= 100] : opinion leaders
else : normal users

14. Problem Def.
2. Divide tree to two types – starts from opinion leader and otherwise
Opinion
leader
Normal
user

15. Problem Def.
3. Label edge
mi to mj v𝑗 =
< 𝑎𝑝𝑝𝑟𝑜𝑣𝑎𝑙 𝑠𝑐𝑜𝑟𝑒 𝑜𝑓 𝑚𝑗, 𝑑𝑜𝑢𝑏𝑡 𝑠𝑐𝑜𝑟𝑒 𝑜𝑓 𝑚𝑗, 𝑜𝑣𝑒𝑟𝑎𝑙𝑙 𝑠𝑒𝑛𝑡𝑖𝑚𝑒𝑛𝑡 𝑠𝑐𝑜𝑟𝑒 𝑖𝑛 𝑚𝑗 >
Approval score == how much contains approval words
Doubt score == how much contains doubt words
Sentiment score == ratio of positive and negative words

16. Problem Def.
4. Extract 23 feature from each message propagation tree

17. Dataset
All from weibo
(related users – 4 million )
2601 false rumors
2536 normal message

18. Problem Method – hybrid svm
with graph kernel and rbf kernel
SVM classify with small data to 10 ( 1000 message – 500 not false / 500 false)
=> With parameter tuning, accuracy point : 0.913

19. Problem result – svm compare

20. Problem result – early detection

21. Conclusion
<Develop graph-kernel-based SVM classifier with combine message and responses>
False rumors have a tendency to start from `normal user` and reposted by opinion leader