13 sdm-blda-slides
DESCRIPTION
Textual information exchanged among users on online social network platforms provides deep understanding into users' interest and behavioral patterns. However, unlike traditional text-dominant settings such as online publishing, one distinct feature for online social network is users' rich interactions with the textual content, which, unfortunately, has not yet been well incorporated in the existing topic modeling frameworks. In this paper, we propose an LDA-based behavior-topic model (B-LDA) which jointly models user topic interests and behavioral patterns. We focus the study of the model on on-line social network settings such as microblogs like Twitter where the textual content is relatively short but user inter-actions on them are rich. We conduct experiments on real Twitter data to demonstrate that the topics obtained by our model are both informative and insightful. As an application of our B-LDA model, we also propose a Twitter followee rec-ommendation algorithm combining B-LDA and LDA, which we show in a quantitative experiment outperforms LDA with a signicant margin.TRANSCRIPT
It Is Not Just What We Say, But How We Say Them:Joint Behaviour-Topic Modelling
Minghui QIU, Feida ZHU and Jing JIANG
Singapore Management University
Microblogs
• Rich user interactions with textual information (posting behaviors)
RETWEET
REPLY
POST
MENTION
Why do we need to consider user behaviors?
2
Observation 1: users with similar topics of interest can have different behavioral patterns
• Users who are interested in `politics’ topic
3
Different behaviors people exhibit in Twitter suggest different motivations using the platform.
• Top 5 users who frequently post tweets about the topic `politics’
4
Observation 2: user clusters with distinct behavioral patterns usually represent different user profiles
Official news media accounts
IT Is Not Just What We Say, But How we Say Them
• The way people interact with text is critical in understanding user behavior patterns and modeling user interest in social networks
• To joint model the topic interests and interactions of a user with the topic in Microbloggs like Twitter
5
Outline
• Topic Modeling in Twitter• Joint behavior-topic model• Applications and Empirical Results
– Topic analysis– User clustering– Followee Recommendation
• Summary
Topic Modeling in Twitter
• Twitter– 140 character limit– Noisy tweets
• Comparison between LDA and Twitter-LDA [Zhao et al., ECIR’10]
LDA T-LDA (Twitter-LDA)
Document All tweets of a given Twitter user
Words Words in user’s tweets
Topic assignment Each word has a topic Each tweet has a topic
Word pools Topical words Topical words or background words
To extend T-LDA to jointly modelthe topic interests and interactions of a user.
LDA-based Behaviour-Topic Modelling – B-LDA
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• U: # of users• N: # of tweets • L: # of words• z: a topic label• y: a switch
U
L
w
N
b
user’s topic distribution θ z
topic’s behavior distribution
𝜓T
background word distribution.
𝜙 ′𝜑
y
topic’s word
distributionT𝜙
𝜂
𝛽𝛽 ′γ𝛼background word
distribution.
Outline
• Topic Modeling in Twitter• Joint behavior-topic model• Applications and Empirical Results
– Topic analysis– User clustering– Followee Recommendation
• Summary
Data sets
• Base data set– 151,055 twitter users in Singapore and their tweets
• Our data set– Randomly selected 5000 users, among whom 1000 are further
selected to obtain their followees, totally 9688 users– Tweets from Sep 1, 2011 to Nov 30.2011– Total tweets: 11,882,441 tweets
• Preprocess– Remove stop words– Remove words with non-standard characters (url, emoticon etc.)
• Parameters setting (LDA, Twitter-LDA, B-LDA):
– # of topics: T = 80– α = 50/T, β = 0.01
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Topic Analysis
• Whether the resulting topics in B-LDA has some dominant behaviors?
• Entropy on topic’s behavior distribution
– B-LDA: p(b|t) could be learnt– LDA and T-LDA:
– C(t,b): # of times topic t co-occurs with behavior b– δ: normalization factor
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Topic Analysis
• Whether the resulting topics in B-LDA has some dominant behaviors?
– Low entropy means the topic is with dominant behaviors– B-LDA: topic is enhanced by dominant behavior patterns
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• Topics of distinct behavior patterns
Topic Analysis
POST
REPLY
RETWEET
13
Topics that are similar but
with different behaviors
• Topics in T-LDA would be split by different behavioral patterns in B-LDA
– 15 topic groups each with two topics– 1 topic group with three topics
Topics Analysis
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T-LDA B-LDA
1
2
T
…1
2
T
…
Distance:KL-divergence
Topic group
• Topics split by different behavioral patterns
Topics Analysis
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Topics in B-LDA are with more distinct behavioral pattern than those in T-LDA
Topic 16 is mainly contributed by new media accounts, but topic 13 is not.Topic 61 is a retweet topic and contains more words with hashtags.
Applications – followee recommendation
• Followee recommendation– User profile: user’s or user’s followees’ textual content– Does not consider behavior patterns
• Behavior-matters– People who use Twitter as instant massager: follow users
who they may interact with– People who use Twitter as information source and news
feeds: follow official new media channels.– Twitter - news media or social network [Kwak et al., WWW’10]
• Definition: users who cares about the behavioral patterns of their followees, explicitly or implicitly, are “behavior-driven followers”. 16
Applications – followee recommendation
• Finding behavior-driven followers– A behavior-driven follower’s followees will naturally form a
small number of clusters within each of which the followees would share similar behavioral patterns.
– k-nearest-neighbor distance
• S: a given space, U: a set of users, : user v’s k-nearest-neighbors
– Behavior-driven index
• ST: the topic space, SB: the joint behavior-topic space, Fu: followees of u
• Behavior-driven follower has a large βK 17
Applications – followee recommendation
• Definition– βK ≥ τ : behavior-driven follower
– βK < τ : topic-driven follower
• Behavior-driven index
– K = 1, topic space: LDA, joint behavior-topic space: B-LDA– Half of users are to some extent behavior-driven
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Applications – followee recommendation
• Followee recommendation approach [Chen et al., WWW’09]
– For a target user u, we randomly pick one followee from u’s current followee set, and then combine her with another m randomly-selected non-followees.
– For these m + 1 users, any recommendation algorithm would generate a ranking of them in descending order.
– The performance is measured by examining how high the real followee is ranked.
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Applications – followee recommendation
• Followee recommendation approach [Chen et al., WWW’09]
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• Evaluation– Rank of the real followee
– Mean reciprocal rank
Applications – followee recommendation
• Evaluation
– Smaller neighbourhood size K has better results– BLDA and TLDA ranks real followees higher than LDA with a smaller
deviation than LDA– Adding behaviours to topic modelling help the task: BLDA > TLDA– LDA: better MRR but low average rank: LDA is not robust and performs
particular well or worse on some set of users 22
Applications – followee recommendation
• Study on behavior-driven index
– Correlation between DKNN and Rank of the real followee
– Correlation between βK and relative rank rLDA/rBLDA
– β1 will be used judge whether a given user is behavior-driven or topic driven follower
Applications – followee recommendation
• Topic-driven follower vs. Behavior-driven follower
• Results on behavior-driven follower
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Applications – followee recommendation
BLDA significantly performs better than LDA on behavior-driven followees.
• A combined followee recommendation method (comModel)– Using behavior-driven index to choose model
• Model selection
Applications – followee recommendation
• Comparisons of comModel, B-LDA and LDA– Rank of the real followee and MRR
– Cummulative distribution of ranks (CDR) for real followees
Applications – followee recommendation
Summary
• We propose B-LDA - a Behaviour-integrated topic model based on LDA
• Comparison B-LDA with LDA and Twitter-LDA– Experiment results show B-LDA can find topics with dominant
behaviours– We propose an index βK to characterize users who are behaviour-
driven followers, and demonstrate that B-LDA significantly outperforms other models on followee recommendation for behaviour-driven followers.
– Based on the βK index, we propose a new recommendation framework combining B-LDA and LDA which gives promising recommendations.
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• Thanks!
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Reference• [Zhao et al., ECIR’10] W. X. Zhao, J. Jiang, J. Weng, J. He, E.-P. Lim, H. Yan,
and X. Li, “Comparing twitter and traditional media using topic models,” ser. ECIR, 2011, pp. 338–349
• [Kwak et al., WWW’10] H. Kwak, C. Lee, H. Park, and S. Moon, “What is Twitter, a social network or a news media?” in WWW, 2010, pp. 591–600.
• W.-Y. Chen, J.-C. Chu, J. Luan, H. Bai, Y. Wang, and E. Y. Chang, “Collaborative filtering for orkut communities: discovery of user latent behavior,” ser. WWW, 2009, pp. 681–690.