Online social networks support users in a wide range of activities, such as sharing information and making recommendations. In this talk, I will talk about a human-generated recommendations of friendship based on hashtags that emerged in Twitter around 2010. I will present a study of a large-scale corpus of human friendship recommendations, using a large corpus of tweets gathered over a 24 week period and involving a set of nearly 6 million users. I will show that these explicit recommendations had a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6% more longevity than other Twitter ties. Finally, I will talk about a supervised system that ranks our user-generated recommendations, surfacing the most valuable ones with high precision (0.52 MAP). We find that features describing users and the relationships between them are discriminative for this task. After the talk, we will carry out some examples on the collection of online data

1. Follow my Friends this Friday.
An Analysis of Human-Generated
Friendship Recommendations
Ruth García-Gavilanes,
Neil O’Hare, Luca Maria Aiello, Alejandro Jaimes
@ruthygarcia
Universidad San Francisco de Quito
November 15, 2016

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4. Computational
Social Science
Computational Social Sciences
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5. Computational Social Sciences
Computational
Social Science
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6. • Hashtags to recommend people every Friday
• No personalized recommendations of who to follow in 2010
• It became trendy and a lot of people used it
• Most popular hashtag in 2010 in several countries
Tweets with #followfriday and #ff
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7. SOCIAL NETWORK RECOMMENDS PEOPLE TO
FOLLOW ON FRIDAYS.
TARGET USER
follows
Tie Strength
to follow or not to follow?
What is the Follow Friday Trend?
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8. • Analyze the dynamics of Follow Friday trend
• Interactions, Impact , effect in time, repetitions and longevity.
• Develop a recommender system for ranking human-generated
recommendations received by the user.
• Identify important features and best classifier
Contributions
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9. FRIDAY SATURDAY SUNDAY MONDAYTHURSDAY
Follows Who is new?
Follows
?
#followfriday
RECEIVER
RECOMMENDER RECOMMENDED
USERS
ACCEPTED
RECOMMENDATION
93% of follow friday Tweets
48 snapshots
24 weeks
snapshot snapshot
Methodology
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10. Total
Initial set of users 55,000
Receivers 21,270
Recommenders 589,844
Recommendation 3,261,133
Recommendation Instances 59,055,205
Accepted Recommendation Instances 354,687
Dataset
0.60% of recommendation instances are accepted
0.86% of recommendations are acceptedGarcía-Gavilanes, Ruth 11 of 23

11. 0.60%
0.90%
1.00%
1.10%
1.20%
9.50%
9.70%
14.50%
Recommender -> Recommendation
Recommender <-> Recommendation
Receiver -> Recommender
Recommender -> Receiver
Recommender <-> Recommendation
Receiver -> Recommendation
Recommendation -> Receiver
Receiver <-> Recommendation
Effect of Interaction
Interactions improve
Acceptance but not
common in FF
recommendations
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12. • We need to compare Follow Friday recommendations
to other models:
• Implicit : Mentions that were not Follow Friday recommendations
• Unobserved : Follow Friday recommendations of the future only
Impact
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13. Follow Friday recommendations
outperform the two alternative conditions
Performance
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14. A small proportion follow recommendations
anyway even without repetitions.
Acceptance after n weeks
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15. Longevity
More #ff accepted recommendation
are still in receiver network after
12 weeks
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16. Repetitions and # of Recommenders
Repetitions and # of recommenders make a significant difference
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15 implicit = 1 #ff

17. USER-BASED
(per user)
Popularity
• Followers vs Followees
• Mentions by other users
• Recommendations
Activity
• Average tweets per day
• New followees
• Accepted recommendations
and recommenders
• Mentions
RECOMMENDATION-BASED
(per recommendation)
Repetitions
• Repeated recommendations
• Different recommenders
Format
• Day of the week
• Re-tweet or not
• Same tweet
recommendations
• Urls
RELATION-BASED
(per pair)
Tie Strength
• Mentionss
• Folllow Friday
recommendations
• Previous acceptances
• Friendship longevity
Similarity
• Words, mentions,
hashtags and urls
• Geolocation
Features for a classifier
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18. • Three methods: Rotation Forest, Linear combination and random
• Training : week 1 to 16
• Test : week 17 to 23
• Goal : accepted recommendations towards the top of the ranking
• Evaluation: Mean Average Precision
• Up to 2 weeks to calculate acceptance rate
• Balanced set for training
Classifiers and methodology
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19. Ranking MAP
Rotation Forest 0.496
Linear
Combination
0.057
Random 0.037
Features MAP
All 0.496
User-based 0.074
Relation-based 0.398
Recommendation-
based
0.062
User + Relation 0.518
User + Format 0.079
Relation + Format 0.379
Results using WEKA
Most important feature is
previous behavior of the
receiver in accepted
recommendations from the
recommender.
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20. • Recommendations derived from social networks have an impact
on users decisions
• Social accepted recommendations seems to last longer/more
relevant
• Many broadcasted recommendations are not seen
• Not accepted recommendations can be followed in the future
• Relation and user based features are better predictors of tie
formation
Lessons learned
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21. QUESTIONS?
@ruthygarcia
ruthygarciag@gmail.com
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Rotation Forest
• Boos$ng
• Ensemble classifier: same classifier model trained on different
data subsets or feature subsets.
• Bagging: majority voting bootstrapping, random forest
• Boosting: AdaBoost – classifiers are added one at a time
and more weights on “hard to classify” data
• Problem: diversity and accuracy
• Solution: Rotation Forest

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Rotation Forest
• Boos$ng
• Proposed in 2006 by Rodríguez et al.
• Ensemble method which trains L decision trees independently,
• using a different set of extracted features for each tree data
subsets or feature subsets.
• Each tree is trained on the whole data set in a rotated feature
space.
• A small rotation of the axes may lead to a very different tree

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• Boos$ng

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• Boos$ng

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Rotation Forest
• Boos$ng
Rotation matrix for Fi,j
How to rearrange for all subsets?

27. References
• Ruth García-Gavilanes, Neil O’Hare, Luca Maria Aiello, Alejandro Jaimes. Follow My Friends This Friday! An Analysis of
Human- generated Friendship Recommendations. In The 5th International Conference on Social Informatics (SocInfo),
Springer 2013. [Best paper award]
• Juan J. Rodriguez, Ludmila I. Kuncheva, and Carlos J. Alonso. 2006. Rotation Forest: A New Classifier Ensemble Method.
IEEE Trans. Pattern Anal. Mach. Intell. 28, 10 (October 2006), 1619-1630. DOI=http://dx.doi.org/10.1109/TPAMI.2006.211
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