How do I decide whom to follow on Twitter ?

IARank: Ranking Users on Twitter in Near Real-time,

Based on their Information Amplification Potential

Motivation• Follow the right users in order to catch up the

breaking news. (Twitter showed to be a very good news media social network)

• Cost-effective users. (chain reaction of information spread by word-of-mouth)

• PageRank-like algorithms have been used to rank users in Twitter in the past, however their convergence time is non-trivial.

• Therefore, it is not possible to rank users in large-scale events in near real-time since the ranks need to be recomputed after every tweet is received.

Data Set Summary

Event #Hashtag Period Number of TweetsLondon Fashion

Week Winter 2012 #LFW Feb 23 – Mar 13 168201

Ipad 3 Launch #Ipad3 Feb 29 – Mar 14 29523London Olympics

2012 #London2012 Feb 8 – Jul 27 1273959

London Olympics 2012 (Closing

Ceremony only)#London2012 Aug 12 – Aug 12 429595

Data Set used to test and compare the Ranking systems.

PageRank Convergence Time

89 8899.999999999990

0.5

1

1.5

2

London Olympics 2012 Closing Ceremony

Number of users

Tim

e (

secon

ds)

Time between tweets

PageRank convergence time benchmark

Influence• Defining Influence. How much excitation a user causes in the network by

receiving attention from other users.

• Retweets, Replies and Mentions. Retweets, replies and mentions are mechanisms of interaction between users in Twitter in which they can show interest to tweets or usernames. More interactions a user receives, more attention they have achieved.

• Influence accumulated over time versus Instant Influence.

Influential User A

Ordinary User

Ordinary User

Ordinary User

Ordinary User

Ordinary User

Ordinary User B

Influential User A

Models of Influence• Cumulative Influence Model: Summation of interactions, weighed by an

Information amplification factor.

• Instantaneous Influence Model: User k receives the accumulated influence from user j, plus his previous value of influence decayed by an α constant.

Information Amplification

• Amplification of Information as a measure of influence is the capacity of a user to amplify the reach of a post shared by another user.

• Features which can measure Information Amplification: o Buzz factor:

• Event activity. how many times a user actively participated in an event

• Attention acquired. how much attention a user received, directly related to the content of the their posts

o Structure Advantage factor:• Social connectivity. Popularity, how many people are connected to the user and

have direct and instant access to their tweets.

Information Amplification

• Each weighed link in the cumulative influence model is substituted by the factors which measure the information amplification of a user.

• Resulting in the fully defined model of cumulative Influence (IARank):

Performance Evaluation: User

Study for LFW event• Poll 1:

o Do you know this user?o It this user relevant to the event? o Would you follow this user?

• Poll 2: Reference Rank: Top 20 from IARank and PageRank

Performance Evaluation:

Comparison Measures• Scaling levels of comparison:

o Sets: content comparisono Position: accounts for the difference between the users positions within

the setso Ranking progression, or accordance between ranks: how similar is a

sequence of ranked usernames between two ranks.

• Respectively, each level requires an appropriate mathematical tool: Precision, Error and Pearson’s Correlation.

Performance Evaluation: Precision

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

0.2

0.4

0.6

0.8

1

London Fashion Week Winter 2012

Top K

Pre

cis

ion PageRank

IARank

Performance Evaluation: Error

1 3 5 7 9 11 13 150

10

20

30

London Fashion Week Winter 2012

Top K

Err

or

IARank PageRank

IARank excluding anomalous user

Performance Evaluation: Correlation

2 3 4 5 6 7 8 9 10 11 12 13 14 15-0.2

0

0.2

0.4

0.6

0.8

1

London Fashion Week Winter 2012

Top K

Corr

ela

tion PageRank

IARank

Conclusion• This work showed that PageRank is not fast enough rank users in large-

scale events such as London Fashion Week, Ipad 3 and London Olympics. • IARank, a simpler, faster and accurate ranking system was designed based

on the concept of information amplification, which takes as influential those users who are generating buzz in the network, or have a potential to reach a high audience.

• The ranking scheme is capable to accurately rank the most influential users in near real-time for large-scale events.

• IARank was evaluated with a user study, which showed that it is marginally better than PageRank in finding relevant usernames. However, PageRank have a slightly better correlation and smaller error in comparison with the rank manually generated by the user study participants, inferring that there is a noticeable trade-off between having a small processing time and the accuracy of the recommended list.

• Lastly, our user study also revealed that users have different personal opinions on the kinds of sources or usernames that would be useful to them. Therefore, incorporating personal preferences into a ranking scheme is likely to be a promising direction for further improving the performance of the IARank.

Simulation