We Know What @You #Tag: Does the Dual Role Affect Hashtag Adoption?

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

Hashtag: Content Tagging

#Obama #Tax

• Mark Content

Hashtag: Content Tagging• Browse and Retrieve

• Link Relevant Topics and EventsHashtag: Content Tagging

e.g., #BREAKINGNEWS: #earthquake with preliminary magnitude of 3.4 has struck 11 miles north of Indio

Hashtag =? Traditional Tag

Hashtag Tag

• 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

• 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

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

Dual Role Hashtag Adoption

Content Tagging

Community Participation

Factors

Hashtag Adoption

• 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

• 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

Step by StepStep 1. Quantify the factors associated with the dual role

• 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

• 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

• 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

• 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}

• 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 }

• Role-Specific Factors• Relevance• Preference• Prestige• Influence

• Role-Unspecific Factors• Popularity• Length• Degree• Freshness• Activeness

Role-Specific and -Unspecific Factors

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.

Step by StepStep 1. Quantify the factors associated with the dual role

Step 2. Correlation Analysis

• 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>

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

Step by StepStep 1. Quantify the factors associated with the dual role

Step 2. Correlation Analysis

Step 3. Regression Analysis

• 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?

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

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

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

• 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.

• Training and Test

Step 4. Prediction of Hashtag Adoption

Time

Training Test

Interval 1 Interval 2 Interval 3 Interval 4

CalculateFeatures

EstimateClass

CalculateFeatures

EstimateClass

• 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

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

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

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

• 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

• 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

Thanks!