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We Know What @You #Tag: Does the Dual Role Affect Hashtag Adoption?Lei Yang1, Tao Sun2, Ming Zhang2,
Qiaozhu Mei11School of Information, the University of
Michigan2School of EECS, Peking University
#France #www2012#Lyon #In_Action
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Hashtag: Content Tagging
#Obama #Tax
• Mark Content
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Hashtag: Content Tagging• Browse and Retrieve
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• Link Relevant Topics and EventsHashtag: Content Tagging
e.g., #BREAKINGNEWS: #earthquake with preliminary magnitude of 3.4 has struck 11 miles north of Indio
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Hashtag =? Traditional Tag
Hashtag Tag
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• The study (Starbird et al., 2011) found that
• According to their email interview
Hashtag: Another Role
“I had never spoken with all of these people, prior to the earthquake. I would have found all of them via the #Haiti #HelpHaiti or other Haiti hashtags, or occasionally a retweet from someone already in my Haiti network.”
HASHTAG
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• A hashtag defines a virtual community of users• with the same background• e.g., #umich, #Microsoft
• with the same interests• e.g., #iphone, #politics
• involved in the same conversation or event• e.g., #www2012, #VoteForObama
Hashtag: Community Participation
Dual RoleContent Tagging + Community
Participation
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Initialize a new communityOrParticipate a community
Create a new bookmarkOrPresent interests to a topic
• A user adopts a hashtag
Dual Role Hashtag Adoption
Community Participation
ContentTagging
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Dual Role Hashtag Adoption
Content Tagging
Community Participation
Factors
Hashtag Adoption
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• To quantify factors that affect the dual role.
• To test whether the proposed factors will affect the behavior of hashtag adoption.
• To make predictions of future adoptions of hashtags.
What to do
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• Provided a macroscopical analysis of the dual role.
• Provided a foundation of the rationality of the behavior of hashtag adoption in terms of the dual role.
• Provided an empirical analysis of how the dual role affects the behavior of hashtag adoption.
• Provided a feasibility study of hashtag recommendation.
Contribution
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Step by StepStep 1. Quantify the factors associated with the dual role
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• Content Tagging • Relevance to the content (e.g., adaptive
filtering)• Closeness to users’ personal Preference (e.g.,
collaborative filtering)• …
• Community Participation• Prestige of community members (e.g.,
preferential attachment)• Influence of friends in the community (e.g.,
social influence)• …
Step 1. Factors Affecting the Dual Role
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• Relevance assesses the similarity between a user u and a hashtag h.
Step 1. Content Role - Relevance
Relevance to my interests = sim (Du, Dh)
A new hashtag
h
Dh : Tweets containing h
Du : Tweets u have posted
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• Preference measures how close a hashtag h is tied to the personal preference of a user u.• Any reasonable function f (.) introduces an
instantiation of preference, such as sum, average, maximum or minimum.
Step 1. Content Role - Preference
My preference to h = f { sim (h, h’ ) | h’ in H }
H : hashtags I have used
beforeA new hashtag
h
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• Prestige is one of the major factors affecting the behavior of joining communities.• Any reasonable function f (.) introduces an
instantiation of prestige.
Step 1. Community Role - Prestige
A new hashtag
h
Users who have
adopted h
Retweet network
G
Prestige of users
in G
f {prestige of u’ | u’ has used h}
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• Influence assesses how much a user u is influenced by its friends already in the community of hashtag h.• The function f (.) can be realized as any reasonable
aggregate function of all the individual influences.
Step 1. Community Role - Influence
Retweet network
G
A new hashtag
h
U = {friends of u who have used h
and may influence u}
f { influence (u, u’) | u’ in U }
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• Role-Specific Factors• Relevance• Preference• Prestige• Influence
• Role-Unspecific Factors• Popularity• Length• Degree• Freshness• Activeness
Role-Specific and -Unspecific Factors
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DatasetsDataset Time Span # Users # Tweets
Politics Dataset 03/2007-12/2010 1,029 373,439Stream Dataset 06/2009-12/2009 19 million 476
million
Group DescriptionPOLITICS Users in Political dataset.
MOVIE Users interested in movies in Stream dataset.
RANDOM Randomly sampled users in Stream dataset.
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Step by StepStep 1. Quantify the factors associated with the dual role
Step 2. Correlation Analysis
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• The relationship between role-specific factors and users’ degree of interests in hashtags.
Step 2. Correlation Analysis
target factor
averagedegree
of interests
1 2 3 … K
TimeTime Interval
<u1, h1>, <u2, h2>, …, <un, hn>
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Step 2. Correlation AnalysisRelevance Preference Prestige Influence
Relevance Preference Prestige Influence
Stream Dataset
Politics Dataset
Deg
ree
of In
tere
sts
Deg
ree
of In
tere
sts
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Step by StepStep 1. Quantify the factors associated with the dual role
Step 2. Correlation Analysis
Step 3. Regression Analysis
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• We want to further look for evidences ofStep 3. Regression Analysis
Whether each of the proposed measures has a predictive power of hashtag adoption?
If yes, how significant they are?
Whether the effect remains significant when the factors interplay with each other?
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Step 3. Regression Analysis• Dependent variable <u, h>: 1 / 0 indicating whether u will use h.• Independent variables: one instantiation of each role-specific
factor.• Control Factors: five instantiations of role-unspecific factors.• Logistic Regression
Time
Calculate independent variables
Calculate dependent variable
Time Interval 1 Time Interval 2
Never used before
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Feature Abbr.
β (POLITICS | MOVIE | RANDOM)
+ : positive, - : negativeSignificance
Influence + | + | + *** | *** | ***Preferenc
e + | + | + *** | *** | ***
Relevance + | + | + *** | *** | ***Prestige + | + | + *** | *** | ***Popularity + | - | - | *** | ***Indegree - | - | - | *** | ***
Outdegree - | - | - | ** | ***Length - | - | - *** | *** | ***
N.uniTag - | + | + | *** | ***Significance at the: *** 0.01, ** 0.05, or * 0.1 level.
Step 3. Regression Analysis
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Step by StepStep 1. Quantify the factors associated with the dual role
Step 2. Correlation Analysis
Step 3. Regression Analysis
Step 4. Prediction of hashtag future adoption
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• Feasibility study of constructing an accurate and effective hashtag prediction and recommendation system.
• Given a user and a hashtag, we formulate the binary classification problem as the following:
• Support Vector Machine
Step 4. Prediction of Hashtag Adoption
- Classes: class 1 indicates that the user will use the hashtag in future, and class 0 denotes that the user won’t use the hashtag in future.
- Features: role-specific factors and role-unspecific factors.
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• Training and Test
Step 4. Prediction of Hashtag Adoption
Time
Training Test
Interval 1 Interval 2 Interval 3 Interval 4
CalculateFeatures
EstimateClass
CalculateFeatures
EstimateClass
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• Systems• Baseline: all role-unspecific factors• Baseline + relevance / preference / prestige /
influence• Baseline + relevance + preference + prestige +
influence
• Hashtag adoption in retweets and non-retweets• All: all tweets• NonRTs: all non-retweets• RTs: all retweets
Step 4. Prediction of Hashtag Adoption
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Group Measures Accuracy (%)All NonRTs RTs
POLITICS
(B)aseline 68.15 66.97 65.54B+Relevan
ce 75.29 *** 74.23 *** 72.53 ***
B+Preference 70.84 *** 71.17 *** 67.23 ***
B+Influence 69.31 *** 68.42 *** 67.23 ***
B+Prestige 75.52 *** 74.88 *** 71.32 ***All 78.25 *** 78.32 *** 74.93 ***
Significance at the: *** 0.01, ** 0.05, or * 0.1 level.
Step 4. Prediction of Hashtag Adoption
• Prediction Performance on POLITICS
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Group Measures Accuracy (%)All NonRTs RTs
MOVIE
(B)aseline 75.98 74.43 77.10B+Relevan
ce 80.42 *** 78.93 *** 81.66 **
B+Preference 79.63 *** 77.66 *** 80.62 ***
B+Influence 79.93 *** 76.89 *** 81.04 ***
B+Prestige 74.09 *** 71.57 *** 74.12 ***All 80.64 *** 79.13 *** 82.80 ***
Significance at the: *** 0.01, ** 0.05, or * 0.1 level.
Step 4. Prediction of Hashtag Adoption
• Prediction Performance on MOVIE
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Group Measures Accuracy (%)All NonRTs RTs
RANDOM
(B)aseline 74.66 73.30 75.41B+Relevan
ce 83.19 *** 82.64 *** 84.50 ***
B+Preference 81.39 *** 79.97 *** 83.39 ***
B+Influence 77.42 *** 75.56 *** 80.18 ***
B+Prestige 74.37 *** 73.39 *** 75.72 ***All 84.03 *** 82.45 *** 85.64 ***
Significance at the: *** 0.01, ** 0.05, or * 0.1 level.
Step 4. Prediction of Hashtag Adoption
• Prediction Performance on RANDOM
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• Results of analyses in this work all indicate that a hashtag serves as both a tag of content and a symbol of membership of a community.
• The measures we propose to quantify the factors all present significant predictive power to the adoption of hashtags.
• The prediction analysis provides a feasibility study of hashtag recommendation systems, suggesting a promising future direction of research.
Conclusion
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• Study and differentiate the two roles of hashtags.
• Study what role users are adopting when they are adopting a new hashtag.
• Study how to better make use of the dual role to do hashtag recommendation.
Future Work
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Thanks!