How do I decide whom to follow on Twitter ? IARank: Ranking Users on Twitter in Near Real-time,...
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Transcript of How do I decide whom to follow on Twitter ? IARank: Ranking Users on Twitter in Near Real-time,...
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