Social media have increasingly become popular platforms for information dissemination. Recently, companies have attempted to take advantage of social advertising to deliver their advertisements to appropriate customers. The success of message propagation in social media depends greatly on the content relevance and the closeness of social relationships. In this paper, considering the factors of user preference, network influence, and propagation capability, we propose a diffusion mechanism to deliver advertising information over microblogging media. Our experimental results show that the proposed model could provide advertisers with suitable targets for diffusing advertisements continuously and thus efficiently enhance advertising effectiveness

1. A diffusion mechanism for social
advertising over microblogs
Mohammad Moein Hosseini Manesh
M.S student of Information Technology (e­commerce)
Amirkabir University of Technology
abc@moein.xyz
July ­ 2016

2. Online Adversiting
Targeted Advertising
Based on user's preference
● Content­based approach
● Collaborative­based approach
hard to recommend new items to
users when there are no related
comments or rating records
Social Advertising
Based on social relationship
● Uses social relations and
social influences between
people to sell products or
services
● Indirect Advertising: Word­of­
mouth / Viral marketing

3. Social Advertising
● Goal: Influence more users with a fixed budget
● How: Disseminate advertisments via social endorsers
● Endorsers: Most influential users of social media
● Find endorsers of your market segment
● Motivate them to share your product/service

4. Architecture of endorser
discovery engine

5. Preference analysis module
● User preference
– Pre­Classiying
– Targeted Marketing: Right product to right user
● Advertisement fitness
– Evaluating advertisement matching
– Using advertisment ontology tree
– Better result than tag­based approach

6. Fitness
Similarity p=
2Dfm
D1+ D2+2 Dfm

7. Influence analysis module
Influential Leader
● Freeman:
– Degree Centerality
– Betweenness Centerality
●
Kiss and Bichler:
– Out­degree Centerality
● Kwak:
– In­degree centerality equals to Page Rank
● Our Approach:
– Connection degree (Freinds)
– Content Popularity

8. Influence analysis module
Connection degree influenc:
● Degree centrality: the number
of direct connections/links
upon a node
● mutual relationship
(friendship)
Content popularity influenc
● Evaluate the popularity of
what a user posts
● By the number of total
responses and message
reposts from people
● | |: The total number of Φ
the elements in a set Φ
Md(i)=∑
j=1
n
Eij Cd(i)=∑
t=1
T
(Φresponse(i,t)+Φrepost (i,t))
(Φpost (i,t))

9. Propagation strength
analysis module
Social activeness
● Calculate the activity intensity of a user
● Higher activeness higher probability of message →
sharing
Sa(i)=
∑
t =1
T
Φmessages(i ,t )
T

10. Propagation strength
analysis module
Social similarity
● Similarity between two users by implicit social
structure and behavior
– Social structure Firends in common→
– Behavior Contents in common→
● Friends tend to have similar interests if people have →
more common friends, their interests should be more
similar

11. Propagation strength
analysis module
Social similarity
● Common Friends:
● Common Contents:
– IR approaches
● Language Model
● TF­IDF
● ...
Similaritycf (i , j)=
(F(i)∩F( j))
(Max(F(i), F( j)))

12. Propagation strength
analysis module
Common Contents:
Wmp=tfmp∗idf m
Similaritycc (i, j)=cos(iM , jM )
Similaritytotal(i, j)=Similaritycf (i, j)+Similaritycc(i, j)

13. Propagation strength
analysis module
Social interaction:
● Social interaction explicitly catches the factor of
dynamic actions/interactions between people
● The intimacy between two users
● More interaction activities, the higher the probability
of sharing information
Si(i, j)=∑
t=1
T
(
(Φresponse(i, j,t))
(Φresponse(i,t))
)+(
(Φrepost(i, j,t))
(Φrepost(i,t))
)

14. Propagation strength
analysis module
Social propagation:
● Evaluate the network diffusion strength of a user
● Friends' social propagation capability would also affect
a social endorser's propagation score recursive→
Sp(i)= ∑
j∈F(i)
Sp( j)∗(Ss(i, j)+Si(i, j))

15. How to weight?
● Black­box method
– Artificial neural network
● can be trained to
recognize and generalize
the relationship between
a set of inputs and
outputs by nonlinear
processes
● Required a training data
for learning ANN.
● White­box method
– Analytic hierarchy process
– Weights:
● Preference: 0.340075768
● Influence: 0.325100803
● Propagation: 0.33482343

16. Evaluation
● Plurk micro blogging
● 217 Start users
● 55% Male, 45% Female
● Aged between 20 and 50
● Total Users: 247,900
● Three months crawling
● 216 advertisments samples (12 sets)

17. Evaluation
● 4 different approaches
– In­degree
– Ratio­degree followers ­ followings→
– Topic­influence preference + influence→
– Our approach
● Indicators
– Click­through rate
– Repost rate
– Exposure rate

18. The Category Tree

19. Evaluation : CTR

20. Evaluation : RR

21. Evaluation : EX

22. Evaluation
● 4 different approaches
– In­degree
– Ratio­degree followers ­ followings→
– Topic­influence preference + influence→
– Our approach
● Indicators
– Click­through rate
– Repost rate
– Exposure rate

23. Future works
● User prefrence discovery improvment
● Shortest path
● Real semantic analysis
● Using sentiment analysis

24. Thanks For Your Attention