This PPT presents the HISBmodel a rumor propagation model based on human individual and social behavior in online social networks. Un like models in the literature this model is more interested on how individuals spread a rumor in online social networks rather then how a rumor will spread in these networks.

1. HISBmodel: A Rumor Diffusion Model Based on
Human Individual and Social Behaviors
in Online Social Networks
Presented by : HOSNIAdel Imad Eddine
Authors: HOSNIAdel Imad Eddine, Li kan,Ahmed Sadique
Beijing Institute ofTechnology
北京理工大学
07/12/2019
25th International Conference on Neural Information Processing

2. Introduction
Related work
Proposed model
07/12/2019 2
5 Conclusion and future work
Outline
Performance evaluation4
3
2
1

3. Introduction
Related work
Proposed model
07/12/2019 3
5 Conclusion and future work
Outline
Performance evaluation4
3
2
1

4. 07/12/2019 4
Introduction
December the 22nd of 2017
A strange shape appeared in the Californian sky

5. 07/12/2019 5
Introduction

6. 07/12/2019 6
Introduction
Aliens ?

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Introduction
It was a space rocket

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Introduction
Observation Hypothesis Explanation Attractiveness
• Long
• complicated
• Easy
• Short
X Boring
 Very attractive
Aliens

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Introduction
Existence of aliens Earth is flat Fake moon landing
AntivaxxWe can cure cancer naturallyTomatoes cure cancer

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• The propagation of rumors in Online social networks can result
in wide and fast spread causing:
• Shape public opinion,
• Create a political issues,
• Ruin people's reputation,
• Create economic failure. Accused of terrorisem act for a fake picture
Tweet causes a quick increase in the price of oilA fake pricture could caused a political issues
Introduction

11. Introduction
Related work
Proposed model
07/12/2019 11
5 Conclusion and future work
Outline
Performance evaluation4
3
2
1

12. 07/12/2019 12
Related work
• Rumor propagation problem is old.
• Socrate investigates the rumors propagation problem.
Socrate
can we verify the
veracity of the
information?
Is it a position
information?
Is it a useful
information?
Word of mouth propagation Online social networks (OSNs) propagation
| Introduction | Macroscopic Approaches | Microscopic Approaches |

13. 07/12/2019 13
Rumor propagation in online
social networks problem
Rumor detection problem Rumor diffusion problem
Macroscopic approaches
Microscopic approaches • Rumor influence minimization,
• Detection,
• Tracking,
• Stance classification,
• Veracity classification,
• Study and analyze the propagation
process of rumors,
Related work
| Introduction | Macroscopic Approaches | Microscopic Approaches |

14. 07/12/2019 14
• Mainly based on Epidemic models,
• Spread of rumors and epidemics are similar,
• Example of an Epidemic model:
 The SIR model (Susceptible, Infected, Recovered)
Related work
| Introduction | Macroscopic Approaches | Microscopic Approaches |
S I R
𝛼 𝐼 𝑡 𝜆 𝑅 𝑡
𝑆(𝑡) = −𝛼 𝑆(𝑡)𝐼 𝑡
𝐼(𝑡) = 𝛼 𝑆 𝑡 𝐼 𝑡 − 𝜆 𝐼(𝑡)𝑅 𝑡
𝑅(𝑡) = 𝜆 𝐼(𝑡)𝑅 𝑡
𝑆 𝑡 + 𝐼 𝑡 + 𝑅 𝑡 = 1
Ignorant Spreader Stifler
𝛼 rate of
infection 𝜆 rate of
recovery

15. 07/12/2019 15
Aims of this approaches
• Predict properties of how this phenomenon spread,
• Investigate the influential of different factors on rumors spreading .
• Investigate critical rumor spreading threshold.
Forgetting and remembering factor Hesitating factor Education factor
Related work
| Introduction | Macroscopic Approaches | Microscopic Approaches |

16. 07/12/2019 16
Rumor propagation models
Macroscopic approaches
X Individuals do not have identical characteristics ,
X Spread of rumors and epidemics are different,
X Few works investigate the rumor influence minimization,
Related work
| Introduction | Macroscopic Approaches | Microscopic Approaches |

17. 07/12/2019 17
Rumor influence minimization strategies
Blocking nodes/links strategiesTruth campaign strategies
https://www.snopes.com/whats-new/
http://www.emergent.info/
Related work
| Introduction | Macroscopic Approaches | Microscopic Approaches |

18. 5
8
3
1
7
2
4
6
07/12/2019 18
𝑃21
𝑃52 𝑃53
𝑃84𝑃58
𝑃57
𝑃67
𝑃16
5
8
3
1
7
2
4
6
𝜃2
𝜃1
𝜃3
𝜃4
𝜃5
𝜃6
𝜃7
𝜃8
• The information propagation through a cascade
of information.
• A probability of the information transmission
assigned for each edge
Independent cascade model Linear threshold models
• They model the behavior of groups.
• A node v has random threshold 𝜃𝑣.
• A node v becomes active when at least fraction of its
neighbors are active
1
ofneighbor
,  vw
vwb
𝑏21
𝑏52 𝑏53
𝑏84𝑏58
𝑏57
𝑏67
𝑏16
Related work
| Introduction | Macroscopic Approaches | Microscopic Approaches |

19. 07/12/2019 19
• The problem is formulated as an optimization problems,
• Considering the rumor infected a set A of nodes,
• Given a constraint K,
• The influence function 𝜎 𝐴 ,
• The objective is:
1. M𝑖𝑛
𝑘
𝜎 𝐴 , for blocking nodes strategy
2. Ma𝑥
𝐴
𝜎 𝐾 or M𝑖𝑛
𝐾
𝜎 𝐴 , for truth campaign strategy
5
8
3
1
7
2
4
6
Related work
| Introduction | Macroscopic Approaches | Microscopic Approaches |
Rumor propagation model

20. 07/12/2019 20
Rumor propagation models
Microscopic approachesMacroscopic approaches
X Individuals do not have identical characteristics ,
X Spread of rumors and epidemics are different,
X Few works investigate the rumor influence minimization,
X Little attention is paid to the propagation model,
X They are too simple to reproduce a complex phenomenon,
X The spread of opinion is ignored,
X Human individual behaviors is ignored,
Related work
| Introduction | Macroscopic Approaches | Microscopic Approaches |

21. Introduction
Related work
Proposed model
07/12/2019 21
5 Conclusion and future work
Outline
Performance evaluation4
3
2
1

22. 07/12/2019 22
• Modeling a rumor propagation is an increasingly important step to overcome its adverse effect.
• A propagation model allows:
1. Highlight the major factors involved in this process,
2. Device strategies to limit their spread,
3. Test these methods so as to ensure that they can be applied in the real-world situations.
• The objectives are:
• Propose a model that reproduce a realistic rumor propagation.
• Device strategy to minimize the influence of the rumors.
Proposed model
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |

23. 07/12/2019 23
Human
Individual
behaviors
Human social
behaviors
HISBmodel
A problem that’s deals with individuals’ activities should consider
Rumor propagation model considering the Human Individual & Social Behaviors
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
Proposed model

24. 07/12/2019 24
• What is the individual behavior toward a rumor?
• We consider various human factors:
• Individual Background.
• Remembering-Forgetting factor
• Hesitating factor,
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10
TIME STEP
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10
TIME STEP
Proposed model

25. 07/12/2019 25
• What is the individual behavior toward a rumor?
• We consider various human factors:
• Individual Background.
• Remembering-Forgetting factor
• Hesitating factor,
• We use the analogy that rumors bringing effects to individuals are similar to oscillator system being
displaced from the equilibrium position.
𝐴 𝑡 = 𝐴𝑖𝑛𝑡 𝑒−𝛽𝑡|cos 𝜔𝑡 − 𝛿 |
0
5
10
15
20
0 5 10
TIME STEP
ω=π
ω=π/2
ω=π/6
0
5
10
15
20
0 5 10
TIME STEP
β=0,2
β=0,4
β=0,6
0
5
10
15
20
25
0 5 10
TIME STEP
δ=0
δ=π/4
δ=π/2
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
Proposed model

26. 07/12/2019 26
• Propose new rules of rumor spreading:
• Sending probability.
• Acceptance probability.
U V
𝑃𝑠𝑒𝑛𝑑(𝑡) = 𝑒−𝛽𝑡 )𝑠𝑖𝑛(𝜔𝑡 − 𝛿 𝑃𝑎𝑐𝑐
𝑣 =
1
1 + 𝑑 𝑣 𝑑 𝑢
𝑃
Individual behaviors of u Social interaction between v and u
𝑃 𝑡 = 𝑃𝑠𝑒𝑛𝑑(𝑡)𝑃𝑎𝑐𝑐
𝑣
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
𝑑 𝑢, 𝑑 𝑣 are the connection
degree of the node v and u.
Proposed model

27. 07/12/2019 27
• We proposed to introduce the opinion of individuals in the propagation process,
• Example: rumor about the president Obama is Muslim,
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
Supporting
2010-09-28 18:36:47 , Obama
Admits He Is A Muslim,I thought
he did that before he was elected.
Commenting
2010-09-28 22:22:40, The more Obama
says he’s a Christian, the more right
wingers will say he’s a Muslim.”
Querying
?
2010-10-09 06:54:18 Obama,
Muslim Or Christian? (Part 3).
Denying
2010-10-01 05:00:28, barack Obama was
raised a christian he attended a church
with jeremiah wright yet people still
beleive hes a muslim.
Proposed model

28. 07/12/2019 28
• We proposed to introduce the opinion of individuals in the propagation process,
• Example: rumor about the president Obama is Muslim,
100-10-∞ +∞
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
• Individuals spread their opinion about the rumor,
• Individuals’ opinion is computed based on:
• The herd mentality,
• The negative and positive reinforcement,
SupportingCommentingQueryingDenying
?
𝐵𝑣 𝑡 =
𝑢∈𝑁 𝑣 𝑗=1
𝑛
𝐵𝑢 𝑡 − 1
𝑗
Proposed model

29. 07/12/2019 29
• Given an online social network represented by a graph G(V,E),
• Initially set of individuals are spreader with different opinions, the rest are
ignorant,
• Each step time:
1. The transmission probability is estimated by 𝑃 𝑡 = 𝑃𝑠𝑒𝑛𝑑(𝑡)𝑃𝑎𝑐𝑐
𝑣
2. If an ignorant accept the rumor he/she becomes a spreader,
3. Each time an individual accept a rumor her/his opinion is revaluated,
4. The propagation proceeds ends when the rumor popularity fade,
𝑅(𝑡) = 𝑘=0
|𝑉|
𝐴 𝑘 𝑡 ≈ 0
SupportingCommentingQueryingDenying
100-10-∞ +∞
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
Proposed model

30. 07/12/2019 30
• Our goal is to minimize the influence of the rumor in an online social network.
Truth campaign to fight the rumor
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
Proposed model

31. 07/12/2019 31
• We consider that individual with negative opinion about the rumor do not contribute in the spread of
the rumor but contrarily they minimize the influence of the rumor.
SupportingCommentingQueryingDenying
100-10-∞ +∞
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
Proposed model

32. 07/12/2019 32
0
death
survive
t
death
• In context of rumor propagation
• A nodes « survives » means not being infected by the rumor.
• Goal is maximizing the likelihood of nodes « surviving » during the observation time,
S t( )=Pr T >t( )
• Survival theory.
• Probability of an event occur within a time period t
• If the event occurs during t time means « Death», otherwise « survival»,
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
Proposed model

33. 07/12/2019 33
• We propose a greedy algorithm,
• Maximize the likelihood of the nodes to getting infected by the truth campaign,
-
| Introduction | Model formulation | Diffusion process | Rumor influence minimization strategy |
Proposed model

34. Introduction
Related work
Proposed model
07/12/2019 34
5 Conclusion and future work
Outline
Performance evaluation4
3
2
1

35. 07/12/2019 35
0
0.5
1
0 20 40 60
NUMBEROFSPREADERS
Proposed model
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50
NUMBEROFSPREADERS
Charliehebdo
• The model depicts the evolution of a rumor in three stagers as proved in literature and real rumor propagation:
• Rapid growth,
• Fluctuant,
• Slow decline;
Performance evaluation

36. 07/12/2019 36
0
0.5
1
0 20 40 60
NUMBEROFSPREADERS
Proposed model
IC model
Epidemic Model
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50
NUMBEROFSPREADERS
Charliehebdo
• The model depicts the evolution of a rumor in three stagers as proved in literature and real rumor propagation:
• Rapid growth,
• Fluctuant,
• Slow decline;
• The HISBmodel depicts the evolution of a rumor more realistically then the classical models
Performance evaluation

37. 07/12/2019 37
0 50 100
TIME STEP
ω=[π/12,π/3]
ω=[π/3,π/2]
ω=[π/3,2π/3]
ω=[3π/2,π]
0
500000
1000000
1500000
0 20 40 60 80 100
RUMORPOPULARITY
TIME STEP
β=[0.2,0.4]
β=[0.4,0.6]
β=[0.6,0.8]
β=[0.8,1.0]
β=[1.0,1.2]
0 50 100
TIME STEP
δ=[π/24,π/12]
δ=[π/12,π/6]
δ=[π/6,π/3]
δ=[π/3,π/2]
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100
RATEOFINDIVIDUALS
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 50 100
RATEOFINDIVIDUALS
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100
RATEOFINDIVIDUALS
δ
Hesitating factor
β
Individual Background
ω
Remembering-Forgetting
• The model highlight the impact of the
human factor as proven in literature,
• β has greater impact on the final size
and the rumor popularity,
• ω , δ has an impact on the
propagation speed,
Performance evaluation
𝑷𝒓𝒐𝒑𝒂𝒈𝒂𝒕𝒊𝒐𝒏 𝑺𝒑𝒆𝒆𝒅

38. 07/12/2019 38
0 50 100
TIME STEP
ω=[π/12,π/3]
ω=[π/3,π/2]
ω=[π/3,2π/3]
ω=[3π/2,π]
0
500000
1000000
1500000
0 20 40 60 80 100
RUMORPOPULARITY
TIME STEP
β=[0.2,0.4]
β=[0.4,0.6]
β=[0.6,0.8]
β=[0.8,1.0]
β=[1.0,1.2]
0 50 100
TIME STEP
δ=[π/24,π/12]
δ=[π/12,π/6]
δ=[π/6,π/3]
δ=[π/3,π/2]
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100
RATEOFINDIVIDUALS
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 50 100
RATEOFINDIVIDUALS
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100
RATEOFINDIVIDUALS
δ
Hesitating factor
β
Individual Background
ω
Remembering-Forgetting
• The model highlight the impact of the
human factor as proven in literature,
• β has greater impact on the final size
and the rumor popularity,
• ω , δ has an impact on the
propagation speed,
Performance evaluation
𝑷𝒓𝒐𝒑𝒂𝒈𝒂𝒕𝒊𝒐𝒏 𝑺𝒑𝒆𝒆𝒅

39. 07/12/2019 39
0 35 70
RUMORPOPULARITY
NP TCS CGA DRUMIX
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 35 70
RATEOFINDIVIDUALS
0
0.1
0.2
0.3
0.4
0.5
0.6
0 35 70
RATEOFINDIVIDUALS
[*] Wang, B., Chen, G., Fu, L., Song, L., Wang, X., & Liu, X. (2016). DRIMUX: Dynamic Rumor Influence Minimization with User
Experience in Social Networks. In Thirtieth AAAI Conference on Artificial Intelligence.
Popularity of the rumor Rate of infected individuals Rate of believers
Performance evaluation
NP:natura propagation, CGA: classical greedy algorithem, DRIMUX*, TCS:Truth campaign strategy

40. 07/12/2019 40
0 35 70
RUMORPOPULARITY
NP TCS CGA DRUMIX
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 35 70
RATEOFINDIVIDUALS
0
0.1
0.2
0.3
0.4
0.5
0.6
0 35 70
RATEOFINDIVIDUALS
[*] Wang, B., Chen, G., Fu, L., Song, L., Wang, X., & Liu, X. (2016). DRIMUX: Dynamic Rumor Influence Minimization with User
Experience in Social Networks. In Thirtieth AAAI Conference on Artificial Intelligence.
Popularity of the rumor Rate of infected individuals Rate of believers
Performance evaluation
NP:natura propagation, CGA: classical greedy algorithem, DRIMUX*, TCS:Truth campaign strategy

41. 07/12/2019 41
Twitter Facebook Youtube
0,05% 0,10% 0,15% 0,20% 0,05% 0,10% 0,15% 0,20% 0,05% 0,10% 0,15% 0,20%
Detectiontimeoftherumor
2
N 0,5752 0,5752 0,5752 0,5752 0,6374 0,6374 0,6374 0,6374 0,5274 0,5274 0,5274 0,5274
CGA 0,4798 0,4145 0,3127 0,2214 0,4675 0,2840 0,1448 0,0954 0,3604 0,3017 0,2624 0,2333
DRUMIX 0,4451 0,3651 0,2537 0,1714 0,4335 0,2125 0,1015 0,0860 0,2008 0,1757 0,1478 0,1265
TCS 0,1266 0,1223 0,0957 0,0952 0,1201 0,1002 0,0896 0,0832 0,1700 0,1487 0,1329 0,1237
4
N 0,5752 0,5752 0,5752 0,5752 0,6374 0,6374 0,6374 0,6374 0,5274 0,5274 0,5274 0,5274
CGA 0,4974 0,4326 0,3636 0,3208 0,4721 0,3413 0,2139 0,1533 0,4618 0,4215 0,3887 0,3587
DRUMIX 0,4871 0,4139 0,2572 0,2116 0,3905 0,2751 0,1556 0,1331 0,3718 0,3397 0,3030 0,2818
TCS 0,2454 0,2383 0,2169 0,1891 0,1728 0,1582 0,1327 0,1269 0,3096 0,2798 0,2712 0,2550
8
N 0,5752 0,5752 0,5752 0,5752 0,6374 0,6374 0,6374 0,6374 0,5274 0,5274 0,5274 0,5274
CGA 0,5342 0,4537 0,3999 0,3687 0,4896 0,3615 0,3036 0,2357 0,4934 0,4665 0,4411 0,4321
DRUMIX 0,4921 0,4315 0,3677 0,2614 0,4466 0,3565 0,2360 0,1924 0,4227 0,4017 0,3850 0,3727
TCS 0,3829 0,3660 0,3498 0,3261 0,2550 0,2184 0,2165 0,2077 0,3469 0,3312 0,3044 0,2940
12
N 0,5752 0,5752 0,5752 0,5752 0,6374 0,6374 0,6374 0,6374 0,5274 0,5274 0,5274 0,5274
CGA 0,5116 0,4806 0,4545 0,3935 0,4710 0,4163 0,3334 0,2961 0,5021 0,4903 0,4753 0,4669
DRUMIX 0,4878 0,4348 0,4192 0,3409 0,4068 0,3591 0,2055 0,1508 0,4402 0,4290 0,4196 0,4159
TCS 0,4197 0,4007 0,3796 0,3792 0,3355 0,3206 0,3076 0,2850 0,3590 0,3579 0,3462 0,3470
[*] Wang, B., Chen, G., Fu, L., Song, L., Wang, X., & Liu, X. (2016). DRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social
Networks. In Thirtieth AAAIConference on Artificial Intelligence.
Performance evaluation
NP:natura propagation, CGA: classical greedy algorithem, DRIMUX*, TCS:Truth campaign strategy

42. Introduction
Related work
Proposed model
07/12/2019 42
5 Conclusion and future work
Outline
Performance evaluation4
3
2
1

43. 07/12/2019 43
Conclusion and future work
Conclusions
• Propose a novel rumor propagation model based on human individual and social behaviors,
 Depicts a realistic tend of the evolution of rumor propagation,
 Highlights the impact of human factors accurately.
• Propose a truth campaign strategy to minimize the influence of the rumor,
 Performs a good results in minimizing the influence of the rumor.
Future work
• Introduce the multiplex structure of the networks in the influence propagation model,
• Introduce the topological structure of networks features the RIM strategy for an accurate results.

44. Merci
pour votre attention