WI-IAT2022

Real-World Popularity Estimation from
Community Structure of Followers on SNS
Shuhei Kobayashi, Keishi Tajima
Kyoto University

2Research Background

Does the number of followers accurately reflect the real-world popularity?
⚫ The number of followers is a metric of
popularity on SNS⚫ But followers on SNS are a sampling fromreal-world fans and are often biased⚫ Therefore, the number of followers does
not necessarily directly reflect real-world
popularity⚫ Can we better estimate real-world
popularity by using the community
structure of followers?

• Do different community structuresreflect the target user’s real-world
popularity?
• Do SNS users with followers more
distributed over the graph have morereal-world popularity?

3

Related WorkPageRank⚫ A method for determining the value of a node in a network⚫ Need a whole graphIn our research, we use only 2-hop graph4

Proposed Method(1/3)

Calculate a score of each user
⚫ Create the follower-neighbor graph⚫ Retrieve a target user’s ID, followers’ IDs,and IDs of followers/friends of eachfollower⚫ Calculate the score of each follower
using the method on the next page
⚫ Sum up the scores of all thefollowers, and the result is themetric for estimating real-world
popularity⚫ The higher the score, the higher thepopularity

User score = a + b + c
uabc

u is a target user,
red nodes are followers, andblue nodes are friends/followers of each follower
a, b, c is each follower’s score

Proposed Method(2/3)

Scoring the followers by the proposed method
𝒍𝒐𝒈𝟐(# 𝒐𝒇 𝒃𝒍𝒖𝒆 𝒏𝒐𝒅𝒆𝒔 𝒂𝒅𝒋𝒂𝒄𝒆𝒏𝒕 𝒕𝒐 𝒓𝒆𝒅 𝒏𝒐𝒅𝒆𝒔 +𝟐)⚫ 𝐒𝒄𝒐𝒓𝒆 = 𝒍𝒐𝒈 (# 𝒐𝒇 𝒓𝒆𝒅 𝒏𝒐𝒅𝒆𝒔 𝒂𝒅𝒋𝒂𝒄𝒆𝒏𝒕 𝒕𝒐 𝒓𝒆𝒅 𝒏𝒐𝒅𝒆𝒔+𝟐)
𝟐⚫

The more the number of adjacent red nodes, the smaller the score⚫ Followers from a same community (e.g. real friends) score lower⚫ Smaller distribution when there are many adjacent red nodes⚫

The more the number of adjacent blue nodes, the larger the score⚫ The more connections, the higher the score⚫ More adjacent blue nodes are more valuable because followers are more likely to beinfluential or belong to various communities⚫

The above score is calculated for each follower, and then summed up to obtain thetarget user’s score

56

Proposed Method(3/3)

Example of score calculation
User score= 1+1+1=3u⚫ Each user’s follower has two red adjacentnodes and two blue adjacent nodes
1⚫ Each follower’s score is 1
⚫ Each follower′ s score =𝑙𝑜𝑔𝟐 (𝟐+𝟐)𝑙𝑜𝑔𝟐 (𝟐+𝟐)

1=1⚫ Therefore, the score of this target user is 3
⚫ User score = 1 + 1 + 1 = 3

1

7

Experiment(1/4)

Predicting Ms/Mr University Competitions Standings⚫ Predicting the winner and the runners-up of ninecompetitions⚫ Compare the accuracy of proposed methods and themethod using the number of followers⚫ Four evaluation metrics
CombinationOrdered

１２…

21…

１２

Winner only…

１Separate
…

…

１
2…

Experiment(2/4)

Calculate the score of each target user
⚫ Use Twitter and Instagram⚫ Construct the follower-neighbor graph on Twitter andcalculate the score⚫ Instagram scores are estimated from Twitter scores

89Experiment(3/4)

Obtain twitter graph only
⚫ Although it is desirable to obtain the follower structure for both
Twitter and Instagram, the Instagram follower structure is difficult toobtain due to restriction⚫ Instagram scores are estimated by converting scores obtained fromTwitter# 𝐼𝑛𝑠𝑡𝑎𝑔𝑟𝑎𝑚 𝑓𝑜𝑙𝑙𝑜𝑤𝑒𝑟𝑠

0.106⚫ 𝐼𝑛𝑠𝑡𝑎‐ 𝑠𝑐𝑜𝑟𝑒 = 𝑇𝑤𝑖𝑡𝑡𝑒𝑟‐ 𝑠𝑐𝑜𝑟𝑒 × ( # 𝑇𝑤𝑖𝑡𝑡𝑒𝑟 𝑓𝑜𝑙𝑙𝑜𝑤𝑒𝑟𝑠 ) × ( 0.42 )⚫ 0.106 is the clustering coefficient of Twitter social graph (A. Java, 2007),
0.42 is that of Instagram social graph (L. Manikonda, 2014)⚫ Experiments with two patterns: Twitter+Instagram and Twitter only
Java, X. Song, T. Finin, and B. Tseng. “Why we twitter: Understanding microblogging usage and communities” in Proc. of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web
Mining and Social Network Analysis, 2007, pp. 56–65.
L. Manikonda, Y. Hu, and S. Kambhampati, “Analyzing user activities, demographics, social network structure and user-generated content on instagram,” arXiv:1410.8099, 2014

10Experiment(4/4)

Example of score converting⚫ Estimate Instagram score
Twitter score

Instagram score# Twi followers# Ins followers

1,000?

1,0002,000⚫ Instagram score is 504
𝐼𝑛𝑠𝑡𝑎‐ 𝑠𝑐𝑜𝑟𝑒 = 1000 ×

2000
1000×0.106
0.42

≈ 504

𝑇𝑤𝑖＋𝐼𝑛𝑠𝑡𝑎 ‐ 𝑠𝑐𝑜𝑟𝑒 = 1000 + 504 = 1504

11Results

Twitter + Instagram
Accuracy

Combi Ordernation ed

Winner Separate
only# Twi followers

3/9

1/9

6/9

7/18

# Ins followers

5/9

3/9

6/9

9/18

#Twi + Ins
followers

5/93/97/9

10/18

Proposed method 4/9

2/9

6/9

8/18

⚫ The number of total followers
shows the best accuracy on
Twitter+Instagram

Twitter only

Accuracy

Combinationorde Winner separate
red only# Twi followers

3/9

1/9

6/9

7/18

Proposed Method

5/9

4/9

6/9

10/18

⚫ Proposed method shows thebest accuracy on Twitter onlyConclusion

12Follower structure may be used to estimate real-world popularity⚫ The proposed method shows a lower accuracy on Twitter+Instagram, butshows a higher accuracy on Twitter only

⚫ If the follower-neighbor graph of the Instagram could be obtained, theproposed method could show higher accuracy on Twitter+Instagram⚫ The characteristics of the proposed method suggest that the more
distributed the followers are, the higher the real-world popularity is, andthe higher the value of the followers, the higher the real-world popularity.