when-to-post on social networks - zhisheng li & prantik bhattacharyya, lithium
TRANSCRIPT
When-To-Post On Social NetworksDecember 2, 2015 @ Big Data Application Meetup, Cask
Nemanja Spasojevic, *Zhisheng Li, Adithya Rao, *Prantik Bhattacharyya
● Klout is a social influence measurement tool.
● Users register on Klout.com and connect their social network accounts.
● Klout collects authorized/public information from connected networks.
● Klout derives influence scores and topics for users from collected data.
● Klout recommends:○ content to post○ times when to post.
What is Klout ?
What is Klout ?
What is Klout ?
● Maximize audience engagements:○ Reach friends ○ Better targeting by brands ○ Schedule campaign
● Personalized schedules vs. infographics
Motivation
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Challenges● Data sparsity ● Lack of open data sets ● Unique audiences ● Specificity network dynamics
Problem SettingFor a user on a social network, find the best time to post a message in order to maximize the probability of receiving audience reactions.
Problem SettingFor a user on a social network, find the best time to post a message in order to maximize the probability of receiving audience reactions.
● Consider only: replies, retweets, favorites, likes, comments.● Weekly user behaviour cycle ● Observe only first 24hr of reactions● 15 min time bucket● Starting bucket is 00:00-00:15 Monday (relative to user’s
timezone)
System Overview
Open Dataset https://github.com/klout/opendata
https://github.com/klout/opendata● Anonymized Post-Reaction Timestamps ● 144+ million posts
○ Twitter 119M ○ Facebook 25M
● 1.1+ billion reactions○ Twitter 104M ○ Facebook 1B
● Anonymized user ID fingerprints map across networks
● Slightly perturbed timestamps
Audience Behaviour
● Inherent delay ● Different networks have different engagement dynamics● 50% of first 24h reactions Twitter in 24 min while Facebook in 1h
42min● Estimate anticipated reactions over time
Post To Reaction Analysis: Network
● Reaction speed may depend on topic of the content
Post To Reaction Analysis: Topic
● Depending on Actors in-degree they may react to posts faster or slower
Post To Reaction Analysis: Actor In-Degree
Audience Behaviour - Network
Audience Behaviour - Location
Audience Behaviour - Location
Audience Behaviour - Location
Same City Audience Behaviour Correlation and Similarity
Audience Behaviour - Topics
Personalized Schedules
Personalized SchedulesAuthor
Audience
● When do the users ai create posts?
● When does a specific audience member b0 react to the posts created by ai?
● What is the probability that b0 reacts to post in a certain time bucket tk?
1st Degree Schedule
2nd Degree Schedule
Personalized Schedules
First-Degree Reaction Schedule
First-Degree Reaction Schedule● When do the users ai create posts?● When does a specific audience member b0 react to the posts
created by ai?
Second-Degree Reaction Schedule
Second-Degree Reaction Schedule● When do the users ai create posts?● When does a specific audience member b0 react to the posts
created by ai?● What is the probability that b0 reacts to a post in a certain time
bucket tk?
Weighted Schedules● Unweighted:
○ All audience members treated the same
● Weighted○ Audience members weighted by
past engagement.○ A close friend may respond to
your posts more often than an acquaintance.
Personalized Schedules - Twitter Example
Personalized Schedules - EvaluationEvaluate on:
● 56 days of unseen data● 0.5M active users
Baselines for a timezone :● Most Frequently Used (MFU)● Aggregate First-Degree (AFD)
Reaction gain of:● 17% on Facebook ● 4% on Twitter
Future Work
https://github.com/klout/opendata● Personalized Post To Reaction Filter Functions ● More Sophisticated 2nd degree Model ● Topical Awareness ● Content Analysis (text, photo, video)● More Networks and Signals
Conclusion
https://github.com/klout/opendata● Reaction times are more than 4x faster on Twitter compared to other
networks.● Audience behavior varies across different networks.● Users audiences across different cities exhibit different behavior
patterns.● Using personalized schedules users can see reaction gain of up to :
○ 17% on Facebook ○ 4% on Twitter
● We hope open dataset can benefit future research on when-to-post problem.
KDD2015 http://arxiv.org/pdf/1506.02089v1
Q & A