Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation † †
Abstract
:1. Introduction
2. Related Work
3. Model for Representing Relations on Social Networks
- Tags in this paper are text-based posts only.
- All users on the social network understand the meaning of a tag.
- TIME is the data type as the timestamp.
- #(A) is the number of elements in set A.
3.1. U–Set of Users on a Social Network
- Profile: includes the personal information of a user, such as ID, name, DOB, and phone number.
- ListTags = [t1,…,tn]: list of tags ti in T-set, which are related to the corresponding user (i = 1…n).
- ListFriends = [f1,…,fm]: list of other users fj in U-set, which are friends with the corresponding user (j = 1…m).
- ListFollowers = [l1,…,lp]: list of other users lk in U-set, which are followers of the corresponding user (k = 1…p).
3.2. T–Set of Tags on a Social Network
- Content: describes the content of the tag.
- Owner ∈ U: this is the user as the seeder of the corresponding tag.
- Mention: list of users mentioned in the tag.
- τ ∈ Time: a timestamp of the corresponding tag.
- Interaction: is a set of users who interacted with the corresponding tag.
- Interaction: = {(u, πu) ∈ U × Time | interact(u, *this), *this is the corresponding tag,
- πu ∈ Time is the timestamp of the interaction corresponding tag of user u}
- Sh is a set of users who shared the corresponding tag.
- Sh: = {(u, πu) ∈ U × Time | share(u, *this), *this is the corresponding tag,
- πu ∈ Time is the timestamp of the corresponding sharing tag of user u}
- Com is a set of users who have comments on the corresponding tag.
- Com: = {(u, πu) ∈ U × Time | comment(u, *this), *this is the corresponding tag,
- πu ∈ Time is the timestamp of the comment of user u}
3.3. R–Set of Relations on a Social Network
- RU: a set of relations between two users.
- RT: a set of relations between a user and a tag.
4. Measures of Influence for a User
4.1. Influential Vector of a User
4.2. Information Propagation
- (a)
- Let u, v∈ U be users on F. The user u is more influent than the user v in the time windowδ, denoted v << u, if:
- ii
- IU(v) ≤ IU(u) and
- iii
- or
- (b)
- Let G ⊆ U, a user w ∈ G is an influencer on F in the time window δ if:#({v ∈ G | v << w}) ≥μ × #(G)
5. Determine the Influencer on a Social Network
5.1. The Graph for Connections between Users
- If follower (vi, vj), then w(eij) = 1.
- If friends (vi, vj), then w(eij) = w(eij) = 2.
- If interacted (vi, t), then w(eik) += 1 with vk = t.Owner
- For each relation comment (vi, t), w(eik) += 2 with vk = t.Owner
- If shared (vi, t), then w(eik) += 1 with vk = t.Owner.
5.2. Creating Graphs for Specific Brands/Products/News
5.2.1. Passion Point
5.2.2. Graphs for Specific Brands/Products/News
Algorithm 1: Creating a sub-graph representing the connection between brand-loving users. |
Input: A social network F = (U, T, R) as the SNet model. |
Graph G is represents the connections between users. |
A specific brand/product/news X. |
Output: Extract a sub-graph of users engaging with brand X. |
The process of creating sub-graphs is as follows: |
Step 1: Traverse each node v in Graph G. |
Let ω > 0 be a constant, showing the minimum passion point of a user with brand X. |
Check v.ListTags to see whether the corresponding user mentioned brand X in the tags. |
If PPv(X) ≥ ω, with PPv(X) is computed by Formula (13). |
Insert the node v into the sub-graph and go to Step 2; |
Otherwise, go to Step 3. |
Step 2: Expand the search space to the node’s neighbors. |
Insert edges between the current node and its neighbors into the sub-graph if: |
(1) the neighbors also mentioned brand/product/news X, or |
(2) the neighbors interact or have comments on the tags of the current node related to X. |
In Case (1): if the current user posts the tag t related to the product/brand/news, which is shared from another user y = t.Owner, create an edge between the current user and the user y. |
Update the edge’s weight, as shown Definition 7. |
Step 3: If there are untraversed nodes in the network, go back to Step 1. |
5.3. Determine the Influencer on a Social Network
Algorithm 2: Determine the most influential user |
∙ Stage 1: Determine a group of users who are interested in brand X. |
Step 1: Create Graph G, as shown in Definition 7, representing connections between users on the social network. |
Step 2: Create a sub-graph of G by the algorithm in Section 5.3 to determine a group of users who are interested in brand X. |
This group is denoted GX. |
∙ Stage 2: Determine the most influential user to other people in the time window δ. |
Step 3: With each u ∈ GX, compute the influence measures of the user u. |
• Influence vector IU(u):= (Impress(u), Popularity(u)) as Formula (5) in Definition 2. |
• The average of the interaction of u’s tags AIu(δ) is computed by Formula (9). |
Step 4: Determine the set of influencers in GX as Definition 6. |
S:= { }; |
for w in GX do |
{ |
Sw(δ):= {v ∈ GX | v << u}, with the relation “<<” is defined in Definition 6. |
If #(Sw(δ)) ≥ μ × #(G) then |
S: = S ∪ {w}; |
} |
ReturnS is a set of influencers in GX. |
6. Testing and Experimental Results
6.1. Testing
- The values of (α1, α2, α3) in Formula (1), (β1, β2, β3) in Formula (2), and (γ1, γ2, γ3) in Formula (3) were chosen by the assumption that: the weight of a follower’s interaction was higher than a friend’s, and the weight of an unrelated user’s interaction was higher than other users. Despite the opinions from the experts and managers in online marketing, the values of parameters in formulas were chosen as follows:α1 = 0.25 α2 = 0.5 α3 = 0.75β1 = 0.25 β2 = 0.5 β3 = 0.75γ1 = 0.25 γ2 = 0.5 γ3 = 0.75
- The values of (α, β, γ) in Formula (6) are α = β = γ = 0.5.
- The value of μ in Formula (12) is 0.8, which means a user is an emerging influencer if he/she is more influential than 80% of users in the group GX.
6.2. Experimental Results
- Phase 1: Our customer used four users in our list for their influencer marketing strategy from 9–16 October.
- Phase 2: Our customer used other users—who were famous Key Opinion Leaders (KOLs) in Vietnam—for the marketing from 27–31 October 2018.
6.3. Discussions
7. Conclusions and Future Works
Author Contributions
Funding
Conflicts of Interest
References
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Kind | Relation | Meaning |
---|---|---|
Relations between two users (RU) | friend ⊆ U × U | friend(u, v): the user u is a friend of the user v. |
follower ⊆ U × U | follower(u, v): the user u is a follower of the user v. | |
Relations between a user and a tag (RT) | interact ⊆ U × T | interact(u, t): the user u interacts with a tag t, such as u likes/views/searches the tag t. |
comment ⊆ U × T | comment(u, t): the user u has a comment on a tag t. | |
share ⊆ U × T | share(u, t): the user u shares a tag t. |
Duration | Posts | Comments | Shares | Total | Rate |
---|---|---|---|---|---|
October, 2018 | 22,511 | 638,278 | 140,782 | 801,571 | |
September, 2018 | 15,193 | 24,2714 | 86,052 | 343,959 | 133% |
November, 2018 | 8883 | 218,341 | 83,673 | 310,897 | −61% |
Phase | Duration | Posts | Comments | Shares |
---|---|---|---|---|
Phase 1 | 9–16 October | 9108 | 229,158 | 54,934 |
17–24 October | 5350 | 171,701 | 28,256 | |
Total | 14,458 | 400,859 | 83,190 | |
Phase 2 | 26–31 October | 3241 | 108,519 | 25,504 |
1–8 November | 3691 | 58,427 | 25,993 | |
Total | 6932 | 166,946 | 51,497 | |
Rate 1 | 36% | 47% | 46% |
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Huynh, T.; Nguyen, H.; Zelinka, I.; Dinh, D.; Pham, X.H. Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †. Sustainability 2020, 12, 3064. https://doi.org/10.3390/su12073064
Huynh T, Nguyen H, Zelinka I, Dinh D, Pham XH. Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †. Sustainability. 2020; 12(7):3064. https://doi.org/10.3390/su12073064
Chicago/Turabian StyleHuynh, Tai, Hien Nguyen, Ivan Zelinka, Dac Dinh, and Xuan Hau Pham. 2020. "Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †" Sustainability 12, no. 7: 3064. https://doi.org/10.3390/su12073064
APA StyleHuynh, T., Nguyen, H., Zelinka, I., Dinh, D., & Pham, X. H. (2020). Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †. Sustainability, 12(7), 3064. https://doi.org/10.3390/su12073064