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How do I decide whom to follow on Twitter ?

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)

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How do I decide whom to follow on Twitter ?

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  1. How do I decide whom to follow on Twitter ? IARank: Ranking Users on Twitter in Near Real-time, Based on their Information Amplification Potential

  2. 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.

  3. Data Set Summary Data Set used to test and compare the Ranking systems.

  4. PageRank Convergence Time

  5. 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.

  6. 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.

  7. 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: • 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 • Structure Advantage factor: • Social connectivity. Popularity, how many people are connected to the user and have direct and instant access to their tweets.

  8. 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):

  9. Performance Evaluation: User Study for LFW event • Poll 1: • Do you know this user? • It this user relevant to the event? • Would you follow this user? • Poll 2: Reference Rank: Top 20 from IARank and PageRank

  10. Performance Evaluation: Comparison Measures • Scaling levels of comparison: • Sets: content comparison • Position: accounts for the difference between the users positions within the sets • 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.

  11. Performance Evaluation: Precision

  12. Performance Evaluation: Error

  13. Performance Evaluation: Correlation

  14. 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.

  15. Simulation

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